Identifying HRV patterns in ECG signals as early markers of dementia
The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in...
- Autores:
-
Arco, Juan E.
Gallego-Molina, Nicolás J.
Ortiz, Andrés
Arroyo-Alvis, Katy
López-Pérez, P. Javier
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13928
- Acceso en línea:
- https://hdl.handle.net/11323/13928
https://repositorio.cuc.edu.co/
- Palabra clave:
- Heart rate variability
Mild cognitive impairment
Dementia
Machine learning
Signal processing
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
| id |
RCUC2_175e2f4d98658b5cdc955e1710da662c |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/13928 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
Identifying HRV patterns in ECG signals as early markers of dementia |
| title |
Identifying HRV patterns in ECG signals as early markers of dementia |
| spellingShingle |
Identifying HRV patterns in ECG signals as early markers of dementia Heart rate variability Mild cognitive impairment Dementia Machine learning Signal processing |
| title_short |
Identifying HRV patterns in ECG signals as early markers of dementia |
| title_full |
Identifying HRV patterns in ECG signals as early markers of dementia |
| title_fullStr |
Identifying HRV patterns in ECG signals as early markers of dementia |
| title_full_unstemmed |
Identifying HRV patterns in ECG signals as early markers of dementia |
| title_sort |
Identifying HRV patterns in ECG signals as early markers of dementia |
| dc.creator.fl_str_mv |
Arco, Juan E. Gallego-Molina, Nicolás J. Ortiz, Andrés Arroyo-Alvis, Katy López-Pérez, P. Javier |
| dc.contributor.author.none.fl_str_mv |
Arco, Juan E. Gallego-Molina, Nicolás J. Ortiz, Andrés Arroyo-Alvis, Katy López-Pérez, P. Javier |
| dc.subject.proposal.eng.fl_str_mv |
Heart rate variability Mild cognitive impairment Dementia Machine learning Signal processing |
| topic |
Heart rate variability Mild cognitive impairment Dementia Machine learning Signal processing |
| description |
The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in this work a novel method for evaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers a complete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to the final classification stage. Numerous metrics are computed from the original data in terms of different domains (time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). The resulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines, Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an area under the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI) patients, showing that ECG contain crucial information for predicting the appearance of this pathology. These results are specially relevant given the fact that ECG acquisition is much more affordable and less invasive than brain imaging used in most of these intelligent systems, allowing our method to be used in environments of any socioeconomic range. |
| publishDate |
2024 |
| dc.date.issued.none.fl_str_mv |
2024-06-01 |
| dc.date.accessioned.none.fl_str_mv |
2025-01-20T22:10:56Z |
| dc.date.available.none.fl_str_mv |
2025-01-20T22:10:56Z |
| dc.type.none.fl_str_mv |
Artículo de revista |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| format |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| status_str |
publishedVersion |
| dc.identifier.citation.none.fl_str_mv |
Juan E. Arco, Nicolás J. Gallego-Molina, Andrés Ortiz, Katy Arroyo-Alvis, P. Javier López-Pérez, Identifying HRV patterns in ECG signals as early markers of dementia, Expert Systems with Applications, Volume 243, 2024, 122934, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.122934 |
| dc.identifier.issn.none.fl_str_mv |
0957-4174 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/13928 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.eswa.2023.122934 |
| dc.identifier.eissn.none.fl_str_mv |
1873-6793 |
| dc.identifier.instname.none.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.none.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.none.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Juan E. Arco, Nicolás J. Gallego-Molina, Andrés Ortiz, Katy Arroyo-Alvis, P. Javier López-Pérez, Identifying HRV patterns in ECG signals as early markers of dementia, Expert Systems with Applications, Volume 243, 2024, 122934, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.122934 0957-4174 10.1016/j.eswa.2023.122934 1873-6793 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/13928 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.none.fl_str_mv |
Expert Systems with Applications |
| dc.relation.references.none.fl_str_mv |
Abou-Abbas et al., 2023 Abou-Abbas L., Henni K., Jemal I., Mitiche A., Mezghani N. Patient-independent epileptic seizure detection by stable feature selection Expert Systems with Applications (2023), Article 120585 Adra et al., 2023 Adra N., Dümmer L., Paixao L., Tesh R., Sun H., Ganglberger W., et al. Decoding information about cognitive health from the brainwaves of sleep Scientific Reports, 13 (2023), pp. 1-14, 10.1038/s41598-023-37128-7 Alessio, 2006 Alessio, S. M. (2006). Discrete Wavelet Transform (DWT). In Encyclopedia of multimedia (pp. 645–714). Alizadehsani et al., 2021 Alizadehsani R., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Gorriz J.M., et al. Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data ACM Transactions on Multimedia Computing, Communications, and Applications, 17 (3s) (2021) Allan et al., 2005 Allan L., Kerr S., Ballard C., Allen J., Murray A., McLaren A., et al. Autonomic function assessed by heart rate variability is normal in Alzheimer’s disease and vascular dementia Dementia and geriatric cognitive disorders, 19 (2005), pp. 140-144 Arco, Ortiz, Castillo-Barnes, et al., 2022 Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Quantifying inter-hemispheric differences in Parkinson’s disease using siamese networks Ferrández Vicente J.M., Álvarez-Sánchez J.R., de la Paz López F., Adeli H. (Eds.), Artificial intelligence in neuroscience: affective analysis and health applications, Springer International Publishing (2022), pp. 156-165 Arco, Ortiz, Castillo-Barnes, Górriz, and Ramírez, 2023 Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression Applied Soft Computing, 134 (2023), Article 109991 Arco, Ortiz, Gallego-Molina, et al., 2023 Arco J.E., Ortiz A., Gallego-Molina N.J., Górriz J.M., Ramírez J. Enhancing multimodal patterns in neuroimaging by siamese neural networks with self-attention mechanism International Journal of Neural Systems, 33 (4) (2023), Article 2350019 Arco, Ortiz, Ramírez, et al., 2022 Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Broncano J., et al. Probabilistic combination of non-linear eigenprojections for ensemble classification IEEE Transactions on Emerging Topics in Computational Intelligence, 7 (2022), pp. 1-11 Arco, Ortiz, Ramírez, et al., 2023 Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Górriz J.M. Uncertainty-driven ensembles of multi-scale deep architectures for image classification Information Fusion, 89 (2023), pp. 53-65 Arco et al., 2021 Arco J.E., Ramírez J., Górriz J.M., Ruz M. Data fusion based on searchlight analysis for the prediction of Alzheimer’s disease Expert Systems with Applications, 185 (2021), Article 115549 Arco et al., 2016 Arco, J. E., Ramírez, J., Puntonet, C. G., Górriz, J. M., & Ruz, M. (2016). Improving short-term prediction from MCI to AD by applying Searchlight analysis. In 2016 IEEE 13th international symposium on biomedical imaging (pp. 10–13). Bach and Jordan, 2003 Bach, F., & Jordan, M. (2003). Kernel independent component analysis. In 2003 IEEE international conference on acoustics, speech, and signal processing, 2003. proceedings, vol. 4 (pp. IV–876). Barrero et al., 2006 Barrero F., Vives F., Morales B. Evaluación de la versión española del Memory Impariment Screen Revista de Neurología, 43 (1) (2006), pp. 15-19 Behbahani et al., 2013 Behbahani S., Jafarnia Dabanloo N., Motie Nasrabadi A. Ictal heart rate variability assessment with focus on secondary generalized and complex partial epileptic seizures Advances in Bioresearch, 4 (2013), pp. 50-58 Beniczky et al., 2021 Beniczky S., Karoly P., Nurse E., Ryvlin P., Cook M. Machine learning and wearable devices of the future Epilepsia, 62 (S2) (2021), pp. S116-S124 Benton, 1974 Benton A. Revised visual retention test (fourth ed.), Psychological Corporation, New York (1974) Benton, 1983 Benton A. Contributions to neuropsychological assessment: A clinical manual Oxford Medicine Publications (1983) Bhardwaj et al., 2023 Bhardwaj D., Jutai J., Fallavollita P. Chapter 9 - role of smart technologies in detecting cognitive impairment and enhancing assisted living El Saddik A. (Ed.), Digital twin for healthcare, Academic Press (2023), pp. 181-193 Bhaskar and Ghatak, 2013 Bhaskar R., Ghatak S.K. Nonlinear methods to assess changes in heart rate variability in type 2 diabetic patients Arquivos Brasileiros de Cardiologia, 101 (2013), pp. 317-327 Boissoneault et al., 2019 Boissoneault J., Letzen J., Robinson M., Staud R. Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome Brain Imaging and Behavior, 13 (2019), pp. 789-797 Boser et al., 1996 Boser, B., Guyon, I., & Vapnik, V. (1996). A Training Algorithm for Optimal Margin Classifier. In Proceedings of the fifth annual ACM workshop on computational learning theory, vol. 5. Bosl et al., 2021 Bosl W.J., Leviton A., Loddenkemper T. Prediction of seizure recurrence. A note of caution Frontiers in Neurology, 12 (2021) Bottani et al., 2023 Bottani S., Burgos N., Maire A., Saracino D., Ströer S., Dormont D., et al. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse Medical Image Analysis, 89 (2023), Article 102903 Bowie and Harvey, 2006 Bowie C., Harvey P. Administration and interpretation of trail making test Nature protocols, 1 (2006), pp. 2277-2281 Breiman, 2001 Breiman L. Random forests Machine Learning, 45 (1) (2001), pp. 5-32 Brennan et al., 2001 Brennan M., Palaniswami M., Kamen P. Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48 (11) (2001), pp. 1342-1347 Buchman et al., 2002 Buchman T., Stein P., Goldstein B. Heart rate variability in critical illness and critical care Current Opinion in Critical Care, 8 (2002), pp. 311-315 Calisto et al., 2023 Calisto F.M., Fernandes J., Morais M., Santiago C., Abrantes J.M., Nunes N., et al. Assertiveness-based agent communication for a personalized medicine on medical imaging diagnosis Proceedings of the 2023 CHI conference on human factors in computing systems, Association for Computing Machinery, New York, NY, USA (2023), pp. 1-20 Calisto et al., 2017 Calisto F.M., Ferreira A., Nascimento J.C., Gonçalves D. Towards touch-based medical image diagnosis annotation Proceedings of the 2017 ACM international conference on interactive surfaces and spaces, Association for Computing Machinery, New York, NY, USA (2017), pp. 390-395 Calisto et al., 2020 Calisto F.M., Nunes N., Nascimento J.C. BreastScreening: On the use of multi-modality in medical imaging diagnosis Proceedings of the international conference on advanced visual interfaces, Association for Computing Machinery, New York, NY, USA (2020), pp. 1-5 Cha et al., 2018 Cha S.-A., Park Y.-M., Yun J.-S., Lee S.-H., Ahn Y.-B., Kim S.-R., et al. Time- and frequency-domain measures of heart rate variability predict cardiovascular outcome in patients with type 2 diabetes Diabetes Research and Clinical Practice, 143 (2018), pp. 159-169 Chagué et al., 2021 Chagué P., Marro B., Fadili S., Houot M., Morin A., Samper-González J., et al. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps Journal of Neuroradiology, 48 (6) (2021), pp. 412-418 Chen et al., 2023 Chen C.-W., Kwok Y.-T., Cheng Y.-T., Huang Y.-S., Kuo T., Wu C.H., et al. Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer’s disease transgenic mice Scientific Reports (2023), pp. 1-17, 10.1038/s41598-023-38214-6 Chen et al., 2017 Chen, W., Liu, G.-Z., Su, S., Jiang, Q., & Nguyen, H. (2017). A CHF Detection Method based on Deep Learning with RR Intervals. In Annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. conference, vol. 2017 (pp. 3369–3372). Chou et al., 2022 Chou Y.-T., Sun Z.-J., Shao S.-C., Yang Y.-C., Lu F.-H., Chang C.-J., et al. Autonomic modulation and the risk of dementia in a middle-aged cohort: A 17-year follow-up study Biomedical Journal (2022) Coelho et al., 2023 Coelho B.F.O., Massaranduba A.B.R., dos Santos Souza C.A., Viana G.G., Brys I., Ramos R.P. Parkinson’s disease effective biomarkers based on Hjorth features improved by machine learning Expert Systems with Applications, 212 (2023), Article 118772 Colzato and Steenbergen, 2017 Colzato L.S., Steenbergen L. High vagally mediated resting-state heart rate variability is associated with superior action cascading Neuropsychologia, 106 (2017), pp. 1-6 De Vilhena Toledo and Junqueira, 2008 De Vilhena Toledo M.A., Junqueira L.F. Jr. Cardiac sympathovagal modulation evaluated by short-term heart interval variability is subtly impaired in Alzheimer’s disease Geriatrics & Gerontology International, 8 (2) (2008), pp. 109-118 Deng et al., 2023 Deng X., Liu E., Li S., Duan Y., Xu M. Interpretable multi-modal image registration network based on disentangled convolutional sparse coding IEEE Transactions on Image Processing, 32 (1) (2023), pp. 1078-1091 Duan et al., 2023 Duan H., Zhou D., Xu N., Yang T., Wu Q., Wang Z., et al. Association of unhealthy lifestyle and genetic risk factors with mild cognitive impairment in Chinese older adults JAMA Network Open, 6 (7) (2023), p. e2324031 Duarte Pedroza et al., 2016 Duarte Pedroza L., Espitia A., Montañés P. Aportes y limitaciones del Boston naming test: evidencia a partir de controles colombianos Acta Neurológica Colombiana, 32 (2016), pp. 290-296 Ellis and Thayer, 2010 Ellis R.J., Thayer J.F. Music and autonomic nervous system (Dys)function Music Perception, 27 (4) (2010), pp. 317-326 Feng et al., 2023 Feng H., Yang B., Wang J., Liu M., Yin L., Zheng W., et al. Identifying malignant breast ultrasound images using ViT-patch Applied Sciences, 13 (6) (2023) Ferdinando et al., 2016 Ferdinando H., Seppänen T., Alasaarela E. Comparing features from ECG pattern and HRV analysis for emotion recognition system 2016 IEEE conference on computational intelligence in bioinformatics and computational biology, vol. 1 (2016), pp. 1-6 Florjanski et al., 2019 Florjanski W., Malysa A., Orzeszek S., Smardz J., Olchowy A., Paradowska-Stolarz A., et al. Evaluation of biofeedback usefulness in masticatory muscle activity management—A systematic review Journal of Clinical Medicine, 8 (6) (2019) Folstein et al., 1975 Folstein M.F., Folstein S.E., McHugh P.R. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician Journal of Psychiatric Research, 12 (3) (1975), pp. 189-198 Forte et al., 2019 Forte G., Favieri F., Casagrande M. Heart rate variability and cognitive function: A systematic review Frontiers in Neuroscience, 13 (2019) Gallego-Molina et al., 2022 Gallego-Molina N.J., Ortiz A., Martínez-Murcia F.J., Formoso M.A., Giménez A. Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis Knowledge-Based Systems, 240 (2022), Article 108098 Galluzzi et al., 2009 Galluzzi S., Nicosia F., Geroldi C., Alicandri A., Bonetti M., Romanelli G., et al. Cardiac autonomic dysfunction is associated with white matter lesions in patients with mild cognitive impairment The Journals of Gerontology: Series A, 64A (12) (2009), pp. 1312-1315 Golland and Fischl, 2003 Golland P., Fischl B. Permutation tests for classification: Towards statistical significance in image-based studies Taylor C., Noble J.A. (Eds.), Information processing in medical imaging, Springer Berlin Heidelberg, Berlin, Heidelberg (2003), pp. 330-341 Gomes et al., 2019 Gomes, P., Margaritoff, P., & Silva, H. (2019). pyHRV: Development and evaluation of an open-source python toolbox for heart rate variability (HRV). In Proc. int’l conf. on electrical, electronic and computing engineering (pp. 822–828). Górriz et al., 2023 Górriz J., álvarez Illán I., álvarez Marquina A., Arco J., Atzmueller M., Ballarini F., et al. Computational approaches to explainable artificial intelligence: advances in theory, applications and trends Information Fusion, 100 (2023), p. 101945 Grant and Berg, 1948 Grant D., Berg E. A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-type card-sorting problem Journal of Experimental Psychology, 38 (4) (1948), pp. 404-411 Hadjem et al., 2016 Hadjem, M., Naït-Abdesselam, F., & Khokhar, A. (2016). ST-segment and T-wave anomalies prediction in an ECG data using RUSBoost. In 2016 IEEE 18th international conference on E-health networking, applications and services, vol. 1 (pp. 1–6). Haensel et al., 2008 Haensel A., Mills P.J., Nelesen R.A., Ziegler M.G., Dimsdale J.E. The relationship between heart rate variability and inflammatory markers in cardiovascular diseases Psychoneuroendocrinology, 33 (10) (2008), pp. 1305-1312 Hajian-Tilaki, 2013 Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation Caspian Journal of Internal Medicine, 4 (2013), pp. 627-635 Hämmerle et al., 2020 Hämmerle P., Eick C., Blum S., Schlageter V., Bauer A., Rizas K.D., et al. Heart rate variability triangular index as a predictor of cardiovascular mortality in patients with atrial fibrillation Journal of the American Heart Association, 9 (15) (2020), Article e016075 Han et al., 2023 Han S.-J., Xu Q.-Q., Pan H., Liu W.-J., Dai Q.-Q., Lin H.-Y., et al. Network pharmacology and molecular docking prediction, combined with experimental validation to explore the potential mechanism of Qishen Yiqi pills against HF-related cognitive dysfunction Journal of Ethnopharmacology, 314 (2023), Article 116570 Hillebrand et al., 2013 Hillebrand S., Gast K.B., de Mutsert R., Swenne C.A., Jukema J.W., Middeldorp S., et al. Heart rate variability and first cardiovascular event in populations without known cardiovascular disease: meta-analysis and dose–response meta-regression EP Europace, 15 (5) (2013), pp. 742-749 Hoshi et al., 2013 Hoshi R.A., Pastre C.M., Vanderlei L.C.M., Godoy M.F. Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables Autonomic Neuroscience, 177 (2) (2013), pp. 271-274 Huikuri, 1995 Huikuri H.V. Heart rate variability in coronary artery disease Journal of Internal Medicine, 237 (4) (1995), pp. 349-357 Ilias et al., 2023 Ilias L., Askounis D., Psarras J. Multimodal detection of epilepsy with deep neural networks Expert Systems with Applications, 213 (2023), Article 119010 Jiménez-Mesa et al., 2023 Jiménez-Mesa C., Arco J.E., Valentí-Soler M., Frades-Payo B., Zea-Sevilla M.A., Ortiz A., et al. Using explainable artificial intelligence in the clock drawing test to reveal the cognitive impairment pattern International Journal of Neural Systems, 33 (04) (2023), Article 2350015 Jolliffe, 1986 Jolliffe I.T. Principal component analysis and factor analysis Principal component analysis, Springer New York (1986), pp. 115-128 Khedher et al., 2015 Khedher L., Ramírez J., Górriz J.M., Brahim A., Segovia F. Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images Neurocomputing, 151 (2015), pp. 139-150 Kohavi, 1995 Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection Proceedings of the 14th international joint conference on artificial intelligence - volume 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995), pp. 1137-1143 Kong et al., 2020 Kong S.D.X., Hoyos C.M., Phillips C.L., McKinnon A.C., Lin P., Duffy S.L., et al. Altered heart rate variability during sleep in mild cognitive impairment Sleep, 44 (4) (2020) Kristal-Boneh et al., 1995 Kristal-Boneh E., Raifel M., Froom P., Ribak J. Heart rate variability in health and disease Scandinavian Journal of Work, Environment & Health, 21 (2) (1995), pp. 85-95 Lampe et al., 2023 Lampe L., Huppertz H.-J., Anderl-Straub S., Albrecht F., Ballarini T., et al. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging NeuroImage: Clinical, 37 (2023), Article 103320 Lezak et al., 2004 Lezak M., Howieson D., Loring D., Hannay H., Fischer J. Neuropsyhological assesment (fourth ed.), Oxford University Press (2004) Li et al., 2016 Li H., Yuan D., Wang Y., Cui D., Cao L. Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system Sensors, 16 (10) (2016) Liao et al., 1995 Liao D., Cai J., Brancati F.L., Folsom A., Barnes R.W., Tyroler H.A., et al. Association of vagal tone with serum insulin, glucose, and diabetes mellitus — The ARIC study Diabetes Research and Clinical Practice, 30 (3) (1995), pp. 211-221 Lippman et al., 1993 Lippman N., Stein K.M., Lerman B.B. Nonlinear predictive interpolation: a new method for the correction of ectopic beats for heart rate variability analysis Journal of Electrocardiology, 26 (Supplement) (1993), pp. S14-S19 Lippman et al., 1994 Lippman N., Stein K.M., Lerman B.B. Comparison of methods for removal of ectopy in measurement of heart rate variability American Journal of Physiology-Heart and Circulatory Physiology, 267 (1) (1994), pp. 411-418 López, Ramírez, Górriz, Illan, et al., 2009 López M., Ramírez J., Górriz J.M., Illan I., Salas-Gonzalez D., Segovia F., et al. SVM-based CAD system for early detection of the alzheimer’s disease using kernel PCA and LDA Neuroscience Letters, 464 (2009), pp. 233-238 López, Ramírez, Górriz, Salas-Gonzalez, et al., 2009 López M., Ramírez J., Górriz J.M., Salas-Gonzalez D., Álvarez I., Segovia F., et al. Automatic tool for Alzheimer’s disease diagnosis using PCA and Bayesian classification rules Electronics Letters, 45 (2009), pp. 389-391 Lotufo et al., 2012 Lotufo P.A., Valiengo L., Benseñor I.J.M., Brunoni A.R. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs Epilepsia, 53 (201 Lucena et al., 2009 Lucena F., Barros A.K., Takeuchi Y., Ohnishi N. Heart instantaneous frequency based estimation of HRV from blood pressure waveforms IEICE Transactions on Information and Systems, E92.D (3) (2009), pp. 529-537 Lv et al., 2022 Lv Z.-H., Yu Z., Xie S., Alamri A. Deep learning-based smart predictive evaluation for interactive multimedia-enabled smart healthcare ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18 (2022), pp. 1-20 Lyle et al., 2017 Lyle, J. V., Charlton, P. H., Bonet-Luz, E., Chaffey, G., Christie, M., Nandi, M., et al. (2017). Beyond HRV: Analysis of ECG signals using attractor reconstruction. In 2017 computing in cardiology (pp. 1–4). Mandrekar, 2010 Mandrekar J.N. Receiver operating characteristic curve in diagnostic test assessment Journal of Thoracic Oncology, 5 (9) (2010), pp. 1315-1316 Mccraty and Shaffer, 2015 Mccraty R., Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk Global Advances in Health and Medicine, 4 (1) (2015), pp. 46-61 Morris and Samad, 2021 Morris J., Samad T. Multiscale multivariate statistical process control Encyclopedia of systems and control, Springer International Publishing (2021), pp. 1396-1402 Murat et al., 2021 Murat F., Sadak F., Yildirim O., Talo M., Murat E., Karabatak M., et al. Review of Deep Learning-Based Atrial Fibrillation Detection Studies International Journal of Environmental Research and Public Health, 18 (21) (2021) Nezamabadi et al., 2023 Nezamabadi K., Sardaripour N., Haghi B., Forouzanfar M. Unsupervised ECG analysis: A review IEEE Reviews in Biomedical Engineering, 16 (1) (2023), pp. 208-224 Nicolini et al., 2014 Nicolini P., Ciulla M.M., Malfatto G., Abbate C., Mari D., Rossi P.D., et al. Autonomic dysfunction in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a cross-sectional case-control study PLoS One, 9 (5) (2014), pp. 1-15 Nicolini et al., 2022 Nicolini P., Lucchi T., Abbate C., Inglese S., Tomasini E., Mari D., et al. Autonomic function predicts cognitive decline in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a longitudinal study Frontiers in Aging Neuroscience, 14 (2022) North et al., 2002 North B., Curtis D., Sham P. A note on the calculation of empirical P values from Monte Carlo procedures American Journal of Human Genetics, 71 (2002), pp. 439-441 O’Brien et al., 2017 O’Brien P.D., Hinder L.M., Callaghan B.C., Feldman E.L. Neurological consequences of obesity The Lancet Neurology, 16 (6) (2017), pp. 465-477 Ottaviani et al., 2019 Ottaviani C., Zingaretti P., Petta A.M., Antonucci G., Thayer J.F., Spitoni G.F. Resting heart rate variability predicts inhibitory control above and beyond impulsivity Journal of Psychophysiology, 33 (3) (2019), pp. 198-206 Ranpuria et al., 2007 Ranpuria R., Hall M., Chan C.T., Unruh M. Heart rate variability (HRV) in kidney failure: measurement and consequences of reduced HRV Nephrology Dialysis Transplantation, 23 (2) (2007), pp. 444-449 Raudys and Jain, 1991 Raudys S., Jain A. Small sample size effects in statistical pattern recognition: Recommendations for practitioners IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (1991), pp. 252-264 Rey, 1941 Rey A. L’examen psychologique dans les cas d’encéphalopathie traumatique. (Les problems) Archives de psychologie (1941), pp. 215-285 Rey, 2009 Rey A. REY, Test de copia y de reproducción de memoria de figuras geométricas complejas, Publicaciones de psicología aplicada, TEA Ediciones, S.A. (2009) Reyes Del Paso et al., 2009 Reyes Del Paso G.A., González M.I., Hernández J.A., Duschek S., Gutiérrez N. Tonic blood pressure modulates the relationship between baroreceptor cardiac reflex sensitivity and cognitive performance Psychophysiology, 46 (5) (2009), pp. 932-938 Rodríguez-Rodríguez et al., 2023 Rodríguez-Rodríguez I., Ortiz A., Gallego-Molina N.J., Formoso M.A., Woo W.L. EEG interchannel causality to identify source/sink phase connectivity patterns in developmental dyslexia International Journal of Neural Systems, 33 (04) (2023), Article 2350020 Rogers et al., 2022 Rogers B., Schaffarczyk M., Clauß M., Mourot L., Gronwald T. The movesense medical sensor chest belt device as single channel ECG for RR interval detection and HRV analysis during resting state and incremental exercise: A cross-sectional validation study Sensors, 22 (5) (2022) Rovere et al., 2011 Rovere M.T.L., Maestri R., Pinna G.D. Baroreflex sensitivity assessment - latest advances and strategies European Cardiology, 7 (2) (2011), pp. 89-92 Rubin et al., 2016 Rubin J., Abreu R., Ahern S., Eldardiry H., Bobrow D. Time, frequency & complexity analysis for recognizing panic states from physiologic time-series PervasiveHealth ’16: Proceedings of the 10th EAI international conference on pervasive computing technologies for healthcare, ACM (2016), pp. 81-88 Schaich et al., 2020 Schaich C.L., Malaver D., Chen H., Shaltout H.A., Hazzouri A.Z.A., Herrington D.M., et al. Association of heart rate variability with cognitive performance: The multi-ethnic study of atherosclerosis Journal of the American Heart Association, 9 (7) (2020), Article e013827 Schölkopf et al., 1998 Schölkopf B., Smola A., Müller K. Nonlinear component analysis as a kernel eigenvalue problem Neural Computation, 10 (5) (1998), pp. 1299-1319 Seiffert et al., 2010 Seiffert C., Khoshgoftaar T.M., Hulse J.V., Napolitano A. RUSboost: A hybrid approach to alleviating class imbalance IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40 (2010), pp. 185-197 Shaffer and Ginsberg, 2017 Shaffer F., Ginsberg J.P. An overview of heart rate variability metrics and norms Frontiers in Public Health, 5 (2017) Sigcha et al., 2023 Sigcha L., Borzí L., Amato F., Rechichi I., Ramos-Romero C., Cárdenas A., et al. Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review Expert Systems with Applications, 229 (2023), Article 120541 Štajner et al., 2019 Štajner S., Saggion H., Ponzetto S.P. Improving lexical coverage of text simplification systems for Spanish Expert Systems with Applications, 118 (2019), pp. 80-91 Stroop, 1935 Stroop J. Studies of interference in serial verbal reactions Journal of Experimental Psychology: General, 18 (1935), pp. 643-662 Sun et al., 2023 Sun L., Zhang M., Wang B., Tiwari P. Few-shot class-incremental learning for medical time series classification IEEE Journal of Biomedical and Health Informatics, 1 (1) (2023), pp. 1-11, 10.1109/JBHI.2023.3247861 Szegedy et al., 2015 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In 2015 IEEE conference on computer vision and pattern recognition, vol. 1 (pp. 1–9). Tarvainen et al., 2014 Tarvainen M.P., Niskanen J.-P., Lipponen J. Kubios HRV–heart rate variability analysis software Computer Methods and Programs in Biomedicine, 113 (1) (2014), pp. 210-220 Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology, 1996 Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology M.P. Heart rate variability - standards of measurement, physiological interpretation, and clinical use European Heart Journal, 17 (3) (1996), pp. 354-381 Thanou et al., 2016 Thanou A., Stavrakis S., Dyer J., Munroe M.E., James J.A., Merrill J.T. Impact of heart rate variability, a marker for cardiac health, on lupus disease activity Arthritis Research & Therapy, 18 (2016) Thayer et al., 2012 Thayer J.F., Åhs F., Fredrikson M., Sollers J.J., Wager T.D. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health Neuroscience & Biobehavioral Reviews, 36 (2) (2012), pp. 747-756 Trites, 2023 Trites R. Grooved pegboard test (2023) Wang, 2012 Wang Q. Kernel principal component analysis and its applications in face recognition and active shape models (2012) arXiv abs/1207.3538 Wang and Bi, 2022 Wang R., Bi H.-Y. A predictive model for chinese children with developmental dyslexia—Based on a genetic algorithm optimized back-propagation neural network Expert Systems with Applications, 187 (2022), Article 115949 Wang et al., 2023 Wang T., Chen X., Zhang X., Zhou S., Feng Q., Huang M. Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment Expert Systems with Applications (2023), Article 120761 Wang et al., 2019 Wang S., Fashanu O.E., Zhao D., Guallar E., Gottesman R.F., Schneider A.L., et al. Relation of elevated resting heart rate in mid-life to cognitive decline over 20 years (from the Atherosclerosis Risk in Communities [ARIC] study) The American Journal of Cardiology, 123 (2) (2019), pp. 334-340 Weinstein et al., 2021 Weinstein G., Davis-Plourde K., Beiser A.S., Seshadri S. Autonomic imbalance and risk of dementia and stroke: The framingham study Stroke, 52 (6) (2021), pp. 2068-2076 Xiao et al., 2018 Xiao R., Xu Y., Pelter M., Fidler R., Badilini F., Mortara D., et al. Monitoring significant ST changes through deep learning Journal of Electrocardiology, 51 (2018), pp. S78-S82 Yamakawa et al., 2020a Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors, 20 (14) (2020) Yamakawa et al., 2020b Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors (Basel, Switzerland), 20 (2020) Yang et al., 2008 Yang A.C., Tsai S.-J., Hong C.-J., Yang C.-H., Hsieh C.-H., Liu M.-E. Association between heart rate variability and cognitive function in elderly community-dwelling men without dementia: A preliminary report Journal of the American Geriatrics Society, 56 (2008), pp. 958-960 Zubrikhina et al., 2023 Zubrikhina M., Abramova O., Yarkin V., Ushakov V., Ochneva A., Bernstein A., et al. Machine learning approaches to mild cognitive impairment detection based on structural MRI data and morphometric features Cognitive Systems Research, 78 (2023), pp. 87-95 |
| dc.relation.citationendpage.none.fl_str_mv |
14 |
| dc.relation.citationstartpage.none.fl_str_mv |
1 |
| dc.relation.citationvolume.none.fl_str_mv |
243 |
| dc.rights.eng.fl_str_mv |
© 2023 The Authors. Published by Elsevier Ltd. |
| dc.rights.license.none.fl_str_mv |
Atribución 4.0 Internacional (CC BY 4.0) |
| dc.rights.uri.none.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Atribución 4.0 Internacional (CC BY 4.0) © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
14 páginas |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier Ltd |
| dc.publisher.place.none.fl_str_mv |
United Kingdom |
| publisher.none.fl_str_mv |
Elsevier Ltd |
| dc.source.none.fl_str_mv |
https://www.sciencedirect.com/science/article/pii/S095741742303436X |
| institution |
Corporación Universidad de la Costa |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/03c68028-3acc-48b2-ab73-62c7c1bc491b/download https://repositorio.cuc.edu.co/bitstreams/bff400fe-9294-45be-bcc5-9fcc4869e99d/download https://repositorio.cuc.edu.co/bitstreams/a8f7e25a-6395-4977-82a5-29d7d814c6d3/download https://repositorio.cuc.edu.co/bitstreams/cd25d9a9-021e-443f-9351-f2a50f56a0ed/download |
| bitstream.checksum.fl_str_mv |
9c977fdf782ce559b470fd3d742c6bc0 73a5432e0b76442b22b026844140d683 022fc60405a24809006346b7276ce0c0 4aea4f9f164143bead66b212016f422d |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1828166594205843456 |
| spelling |
Atribución 4.0 Internacional (CC BY 4.0)© 2023 The Authors. Published by Elsevier Ltd.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arco, Juan E.Gallego-Molina, Nicolás J.Ortiz, AndrésArroyo-Alvis, KatyLópez-Pérez, P. Javier2025-01-20T22:10:56Z2025-01-20T22:10:56Z2024-06-01Juan E. Arco, Nicolás J. Gallego-Molina, Andrés Ortiz, Katy Arroyo-Alvis, P. Javier López-Pérez, Identifying HRV patterns in ECG signals as early markers of dementia, Expert Systems with Applications, Volume 243, 2024, 122934, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.1229340957-4174https://hdl.handle.net/11323/1392810.1016/j.eswa.2023.1229341873-6793Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The appearance of Artificial Intelligence (IA) has improved our ability to process large amount of data. These tools are particularly interesting in medical contexts, in order to evaluate the variables from patients’ screening analysis and disentangle the information that they contain. We propose in this work a novel method for evaluating the role of electrocardiogram (ECG) signals in the human cognitive decline. This framework offers a complete solution for all the steps in the classification pipeline, from the preprocessing of the raw signals to the final classification stage. Numerous metrics are computed from the original data in terms of different domains (time, frequency, etc.), and dimensionality is reduced through a Principal Component Analysis (PCA). The resulting characteristics are used as inputs of different classifiers (linear/non-linear Support Vector Machines, Random Forest, etc.) to determine the amount of information that they contain. Our system yielded an area under the Receiver Operating Characteristic (ROC) curve of 0.80 identifying Mild Cognitive Impairment (MCI) patients, showing that ECG contain crucial information for predicting the appearance of this pathology. These results are specially relevant given the fact that ECG acquisition is much more affordable and less invasive than brain imaging used in most of these intelligent systems, allowing our method to be used in environments of any socioeconomic range.14 páginasapplication/pdfengElsevier LtdUnited Kingdomhttps://www.sciencedirect.com/science/article/pii/S095741742303436XIdentifying HRV patterns in ECG signals as early markers of dementiaArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Expert Systems with ApplicationsAbou-Abbas et al., 2023 Abou-Abbas L., Henni K., Jemal I., Mitiche A., Mezghani N. Patient-independent epileptic seizure detection by stable feature selection Expert Systems with Applications (2023), Article 120585Adra et al., 2023 Adra N., Dümmer L., Paixao L., Tesh R., Sun H., Ganglberger W., et al. Decoding information about cognitive health from the brainwaves of sleep Scientific Reports, 13 (2023), pp. 1-14, 10.1038/s41598-023-37128-7Alessio, 2006 Alessio, S. M. (2006). Discrete Wavelet Transform (DWT). In Encyclopedia of multimedia (pp. 645–714).Alizadehsani et al., 2021 Alizadehsani R., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Gorriz J.M., et al. Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data ACM Transactions on Multimedia Computing, Communications, and Applications, 17 (3s) (2021)Allan et al., 2005 Allan L., Kerr S., Ballard C., Allen J., Murray A., McLaren A., et al. Autonomic function assessed by heart rate variability is normal in Alzheimer’s disease and vascular dementia Dementia and geriatric cognitive disorders, 19 (2005), pp. 140-144Arco, Ortiz, Castillo-Barnes, et al., 2022 Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Quantifying inter-hemispheric differences in Parkinson’s disease using siamese networks Ferrández Vicente J.M., Álvarez-Sánchez J.R., de la Paz López F., Adeli H. (Eds.), Artificial intelligence in neuroscience: affective analysis and health applications, Springer International Publishing (2022), pp. 156-165Arco, Ortiz, Castillo-Barnes, Górriz, and Ramírez, 2023 Arco J.E., Ortiz A., Castillo-Barnes D., Górriz J.M., Ramírez J. Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression Applied Soft Computing, 134 (2023), Article 109991Arco, Ortiz, Gallego-Molina, et al., 2023 Arco J.E., Ortiz A., Gallego-Molina N.J., Górriz J.M., Ramírez J. Enhancing multimodal patterns in neuroimaging by siamese neural networks with self-attention mechanism International Journal of Neural Systems, 33 (4) (2023), Article 2350019Arco, Ortiz, Ramírez, et al., 2022 Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Broncano J., et al. Probabilistic combination of non-linear eigenprojections for ensemble classification IEEE Transactions on Emerging Topics in Computational Intelligence, 7 (2022), pp. 1-11Arco, Ortiz, Ramírez, et al., 2023 Arco J.E., Ortiz A., Ramírez J., Martínez-Murcia F.J., Zhang Y.-D., Górriz J.M. Uncertainty-driven ensembles of multi-scale deep architectures for image classification Information Fusion, 89 (2023), pp. 53-65Arco et al., 2021 Arco J.E., Ramírez J., Górriz J.M., Ruz M. Data fusion based on searchlight analysis for the prediction of Alzheimer’s disease Expert Systems with Applications, 185 (2021), Article 115549Arco et al., 2016 Arco, J. E., Ramírez, J., Puntonet, C. G., Górriz, J. M., & Ruz, M. (2016). Improving short-term prediction from MCI to AD by applying Searchlight analysis. In 2016 IEEE 13th international symposium on biomedical imaging (pp. 10–13).Bach and Jordan, 2003 Bach, F., & Jordan, M. (2003). Kernel independent component analysis. In 2003 IEEE international conference on acoustics, speech, and signal processing, 2003. proceedings, vol. 4 (pp. IV–876).Barrero et al., 2006 Barrero F., Vives F., Morales B. Evaluación de la versión española del Memory Impariment Screen Revista de Neurología, 43 (1) (2006), pp. 15-19Behbahani et al., 2013 Behbahani S., Jafarnia Dabanloo N., Motie Nasrabadi A. Ictal heart rate variability assessment with focus on secondary generalized and complex partial epileptic seizures Advances in Bioresearch, 4 (2013), pp. 50-58Beniczky et al., 2021 Beniczky S., Karoly P., Nurse E., Ryvlin P., Cook M. Machine learning and wearable devices of the future Epilepsia, 62 (S2) (2021), pp. S116-S124Benton, 1974 Benton A. Revised visual retention test (fourth ed.), Psychological Corporation, New York (1974)Benton, 1983 Benton A. Contributions to neuropsychological assessment: A clinical manual Oxford Medicine Publications (1983)Bhardwaj et al., 2023 Bhardwaj D., Jutai J., Fallavollita P. Chapter 9 - role of smart technologies in detecting cognitive impairment and enhancing assisted living El Saddik A. (Ed.), Digital twin for healthcare, Academic Press (2023), pp. 181-193Bhaskar and Ghatak, 2013 Bhaskar R., Ghatak S.K. Nonlinear methods to assess changes in heart rate variability in type 2 diabetic patients Arquivos Brasileiros de Cardiologia, 101 (2013), pp. 317-327Boissoneault et al., 2019 Boissoneault J., Letzen J., Robinson M., Staud R. Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome Brain Imaging and Behavior, 13 (2019), pp. 789-797Boser et al., 1996 Boser, B., Guyon, I., & Vapnik, V. (1996). A Training Algorithm for Optimal Margin Classifier. In Proceedings of the fifth annual ACM workshop on computational learning theory, vol. 5.Bosl et al., 2021 Bosl W.J., Leviton A., Loddenkemper T. Prediction of seizure recurrence. A note of caution Frontiers in Neurology, 12 (2021)Bottani et al., 2023 Bottani S., Burgos N., Maire A., Saracino D., Ströer S., Dormont D., et al. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse Medical Image Analysis, 89 (2023), Article 102903Bowie and Harvey, 2006 Bowie C., Harvey P. Administration and interpretation of trail making test Nature protocols, 1 (2006), pp. 2277-2281Breiman, 2001 Breiman L. Random forests Machine Learning, 45 (1) (2001), pp. 5-32Brennan et al., 2001 Brennan M., Palaniswami M., Kamen P. Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48 (11) (2001), pp. 1342-1347Buchman et al., 2002 Buchman T., Stein P., Goldstein B. Heart rate variability in critical illness and critical care Current Opinion in Critical Care, 8 (2002), pp. 311-315Calisto et al., 2023 Calisto F.M., Fernandes J., Morais M., Santiago C., Abrantes J.M., Nunes N., et al. Assertiveness-based agent communication for a personalized medicine on medical imaging diagnosis Proceedings of the 2023 CHI conference on human factors in computing systems, Association for Computing Machinery, New York, NY, USA (2023), pp. 1-20Calisto et al., 2017 Calisto F.M., Ferreira A., Nascimento J.C., Gonçalves D. Towards touch-based medical image diagnosis annotation Proceedings of the 2017 ACM international conference on interactive surfaces and spaces, Association for Computing Machinery, New York, NY, USA (2017), pp. 390-395Calisto et al., 2020 Calisto F.M., Nunes N., Nascimento J.C. BreastScreening: On the use of multi-modality in medical imaging diagnosis Proceedings of the international conference on advanced visual interfaces, Association for Computing Machinery, New York, NY, USA (2020), pp. 1-5Cha et al., 2018 Cha S.-A., Park Y.-M., Yun J.-S., Lee S.-H., Ahn Y.-B., Kim S.-R., et al. Time- and frequency-domain measures of heart rate variability predict cardiovascular outcome in patients with type 2 diabetes Diabetes Research and Clinical Practice, 143 (2018), pp. 159-169Chagué et al., 2021 Chagué P., Marro B., Fadili S., Houot M., Morin A., Samper-González J., et al. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps Journal of Neuroradiology, 48 (6) (2021), pp. 412-418Chen et al., 2023 Chen C.-W., Kwok Y.-T., Cheng Y.-T., Huang Y.-S., Kuo T., Wu C.H., et al. Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer’s disease transgenic mice Scientific Reports (2023), pp. 1-17, 10.1038/s41598-023-38214-6Chen et al., 2017 Chen, W., Liu, G.-Z., Su, S., Jiang, Q., & Nguyen, H. (2017). A CHF Detection Method based on Deep Learning with RR Intervals. In Annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. conference, vol. 2017 (pp. 3369–3372).Chou et al., 2022 Chou Y.-T., Sun Z.-J., Shao S.-C., Yang Y.-C., Lu F.-H., Chang C.-J., et al. Autonomic modulation and the risk of dementia in a middle-aged cohort: A 17-year follow-up study Biomedical Journal (2022)Coelho et al., 2023 Coelho B.F.O., Massaranduba A.B.R., dos Santos Souza C.A., Viana G.G., Brys I., Ramos R.P. Parkinson’s disease effective biomarkers based on Hjorth features improved by machine learning Expert Systems with Applications, 212 (2023), Article 118772Colzato and Steenbergen, 2017 Colzato L.S., Steenbergen L. High vagally mediated resting-state heart rate variability is associated with superior action cascading Neuropsychologia, 106 (2017), pp. 1-6De Vilhena Toledo and Junqueira, 2008 De Vilhena Toledo M.A., Junqueira L.F. Jr. Cardiac sympathovagal modulation evaluated by short-term heart interval variability is subtly impaired in Alzheimer’s disease Geriatrics & Gerontology International, 8 (2) (2008), pp. 109-118Deng et al., 2023 Deng X., Liu E., Li S., Duan Y., Xu M. Interpretable multi-modal image registration network based on disentangled convolutional sparse coding IEEE Transactions on Image Processing, 32 (1) (2023), pp. 1078-1091Duan et al., 2023 Duan H., Zhou D., Xu N., Yang T., Wu Q., Wang Z., et al. Association of unhealthy lifestyle and genetic risk factors with mild cognitive impairment in Chinese older adults JAMA Network Open, 6 (7) (2023), p. e2324031Duarte Pedroza et al., 2016 Duarte Pedroza L., Espitia A., Montañés P. Aportes y limitaciones del Boston naming test: evidencia a partir de controles colombianos Acta Neurológica Colombiana, 32 (2016), pp. 290-296Ellis and Thayer, 2010 Ellis R.J., Thayer J.F. Music and autonomic nervous system (Dys)function Music Perception, 27 (4) (2010), pp. 317-326Feng et al., 2023 Feng H., Yang B., Wang J., Liu M., Yin L., Zheng W., et al. Identifying malignant breast ultrasound images using ViT-patch Applied Sciences, 13 (6) (2023)Ferdinando et al., 2016 Ferdinando H., Seppänen T., Alasaarela E. Comparing features from ECG pattern and HRV analysis for emotion recognition system 2016 IEEE conference on computational intelligence in bioinformatics and computational biology, vol. 1 (2016), pp. 1-6Florjanski et al., 2019 Florjanski W., Malysa A., Orzeszek S., Smardz J., Olchowy A., Paradowska-Stolarz A., et al. Evaluation of biofeedback usefulness in masticatory muscle activity management—A systematic review Journal of Clinical Medicine, 8 (6) (2019)Folstein et al., 1975 Folstein M.F., Folstein S.E., McHugh P.R. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician Journal of Psychiatric Research, 12 (3) (1975), pp. 189-198Forte et al., 2019 Forte G., Favieri F., Casagrande M. Heart rate variability and cognitive function: A systematic review Frontiers in Neuroscience, 13 (2019)Gallego-Molina et al., 2022 Gallego-Molina N.J., Ortiz A., Martínez-Murcia F.J., Formoso M.A., Giménez A. Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis Knowledge-Based Systems, 240 (2022), Article 108098Galluzzi et al., 2009 Galluzzi S., Nicosia F., Geroldi C., Alicandri A., Bonetti M., Romanelli G., et al. Cardiac autonomic dysfunction is associated with white matter lesions in patients with mild cognitive impairment The Journals of Gerontology: Series A, 64A (12) (2009), pp. 1312-1315Golland and Fischl, 2003 Golland P., Fischl B. Permutation tests for classification: Towards statistical significance in image-based studies Taylor C., Noble J.A. (Eds.), Information processing in medical imaging, Springer Berlin Heidelberg, Berlin, Heidelberg (2003), pp. 330-341Gomes et al., 2019 Gomes, P., Margaritoff, P., & Silva, H. (2019). pyHRV: Development and evaluation of an open-source python toolbox for heart rate variability (HRV). In Proc. int’l conf. on electrical, electronic and computing engineering (pp. 822–828).Górriz et al., 2023 Górriz J., álvarez Illán I., álvarez Marquina A., Arco J., Atzmueller M., Ballarini F., et al. Computational approaches to explainable artificial intelligence: advances in theory, applications and trends Information Fusion, 100 (2023), p. 101945Grant and Berg, 1948 Grant D., Berg E. A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-type card-sorting problem Journal of Experimental Psychology, 38 (4) (1948), pp. 404-411Hadjem et al., 2016 Hadjem, M., Naït-Abdesselam, F., & Khokhar, A. (2016). ST-segment and T-wave anomalies prediction in an ECG data using RUSBoost. In 2016 IEEE 18th international conference on E-health networking, applications and services, vol. 1 (pp. 1–6).Haensel et al., 2008 Haensel A., Mills P.J., Nelesen R.A., Ziegler M.G., Dimsdale J.E. The relationship between heart rate variability and inflammatory markers in cardiovascular diseases Psychoneuroendocrinology, 33 (10) (2008), pp. 1305-1312Hajian-Tilaki, 2013 Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation Caspian Journal of Internal Medicine, 4 (2013), pp. 627-635Hämmerle et al., 2020 Hämmerle P., Eick C., Blum S., Schlageter V., Bauer A., Rizas K.D., et al. Heart rate variability triangular index as a predictor of cardiovascular mortality in patients with atrial fibrillation Journal of the American Heart Association, 9 (15) (2020), Article e016075Han et al., 2023 Han S.-J., Xu Q.-Q., Pan H., Liu W.-J., Dai Q.-Q., Lin H.-Y., et al. Network pharmacology and molecular docking prediction, combined with experimental validation to explore the potential mechanism of Qishen Yiqi pills against HF-related cognitive dysfunction Journal of Ethnopharmacology, 314 (2023), Article 116570Hillebrand et al., 2013 Hillebrand S., Gast K.B., de Mutsert R., Swenne C.A., Jukema J.W., Middeldorp S., et al. Heart rate variability and first cardiovascular event in populations without known cardiovascular disease: meta-analysis and dose–response meta-regression EP Europace, 15 (5) (2013), pp. 742-749Hoshi et al., 2013 Hoshi R.A., Pastre C.M., Vanderlei L.C.M., Godoy M.F. Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables Autonomic Neuroscience, 177 (2) (2013), pp. 271-274Huikuri, 1995 Huikuri H.V. Heart rate variability in coronary artery disease Journal of Internal Medicine, 237 (4) (1995), pp. 349-357Ilias et al., 2023 Ilias L., Askounis D., Psarras J. Multimodal detection of epilepsy with deep neural networks Expert Systems with Applications, 213 (2023), Article 119010Jiménez-Mesa et al., 2023 Jiménez-Mesa C., Arco J.E., Valentí-Soler M., Frades-Payo B., Zea-Sevilla M.A., Ortiz A., et al. Using explainable artificial intelligence in the clock drawing test to reveal the cognitive impairment pattern International Journal of Neural Systems, 33 (04) (2023), Article 2350015Jolliffe, 1986 Jolliffe I.T. Principal component analysis and factor analysis Principal component analysis, Springer New York (1986), pp. 115-128Khedher et al., 2015 Khedher L., Ramírez J., Górriz J.M., Brahim A., Segovia F. Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images Neurocomputing, 151 (2015), pp. 139-150Kohavi, 1995 Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection Proceedings of the 14th international joint conference on artificial intelligence - volume 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995), pp. 1137-1143Kong et al., 2020 Kong S.D.X., Hoyos C.M., Phillips C.L., McKinnon A.C., Lin P., Duffy S.L., et al. Altered heart rate variability during sleep in mild cognitive impairment Sleep, 44 (4) (2020)Kristal-Boneh et al., 1995 Kristal-Boneh E., Raifel M., Froom P., Ribak J. Heart rate variability in health and disease Scandinavian Journal of Work, Environment & Health, 21 (2) (1995), pp. 85-95Lampe et al., 2023 Lampe L., Huppertz H.-J., Anderl-Straub S., Albrecht F., Ballarini T., et al. Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging NeuroImage: Clinical, 37 (2023), Article 103320Lezak et al., 2004 Lezak M., Howieson D., Loring D., Hannay H., Fischer J. Neuropsyhological assesment (fourth ed.), Oxford University Press (2004)Li et al., 2016 Li H., Yuan D., Wang Y., Cui D., Cao L. Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system Sensors, 16 (10) (2016)Liao et al., 1995 Liao D., Cai J., Brancati F.L., Folsom A., Barnes R.W., Tyroler H.A., et al. Association of vagal tone with serum insulin, glucose, and diabetes mellitus — The ARIC study Diabetes Research and Clinical Practice, 30 (3) (1995), pp. 211-221Lippman et al., 1993 Lippman N., Stein K.M., Lerman B.B. Nonlinear predictive interpolation: a new method for the correction of ectopic beats for heart rate variability analysis Journal of Electrocardiology, 26 (Supplement) (1993), pp. S14-S19Lippman et al., 1994 Lippman N., Stein K.M., Lerman B.B. Comparison of methods for removal of ectopy in measurement of heart rate variability American Journal of Physiology-Heart and Circulatory Physiology, 267 (1) (1994), pp. 411-418López, Ramírez, Górriz, Illan, et al., 2009 López M., Ramírez J., Górriz J.M., Illan I., Salas-Gonzalez D., Segovia F., et al. SVM-based CAD system for early detection of the alzheimer’s disease using kernel PCA and LDA Neuroscience Letters, 464 (2009), pp. 233-238López, Ramírez, Górriz, Salas-Gonzalez, et al., 2009 López M., Ramírez J., Górriz J.M., Salas-Gonzalez D., Álvarez I., Segovia F., et al. Automatic tool for Alzheimer’s disease diagnosis using PCA and Bayesian classification rules Electronics Letters, 45 (2009), pp. 389-391Lotufo et al., 2012 Lotufo P.A., Valiengo L., Benseñor I.J.M., Brunoni A.R. A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs Epilepsia, 53 (201Lucena et al., 2009 Lucena F., Barros A.K., Takeuchi Y., Ohnishi N. Heart instantaneous frequency based estimation of HRV from blood pressure waveforms IEICE Transactions on Information and Systems, E92.D (3) (2009), pp. 529-537Lv et al., 2022 Lv Z.-H., Yu Z., Xie S., Alamri A. Deep learning-based smart predictive evaluation for interactive multimedia-enabled smart healthcare ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18 (2022), pp. 1-20Lyle et al., 2017 Lyle, J. V., Charlton, P. H., Bonet-Luz, E., Chaffey, G., Christie, M., Nandi, M., et al. (2017). Beyond HRV: Analysis of ECG signals using attractor reconstruction. In 2017 computing in cardiology (pp. 1–4).Mandrekar, 2010 Mandrekar J.N. Receiver operating characteristic curve in diagnostic test assessment Journal of Thoracic Oncology, 5 (9) (2010), pp. 1315-1316Mccraty and Shaffer, 2015 Mccraty R., Shaffer F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk Global Advances in Health and Medicine, 4 (1) (2015), pp. 46-61Morris and Samad, 2021 Morris J., Samad T. Multiscale multivariate statistical process control Encyclopedia of systems and control, Springer International Publishing (2021), pp. 1396-1402Murat et al., 2021 Murat F., Sadak F., Yildirim O., Talo M., Murat E., Karabatak M., et al. Review of Deep Learning-Based Atrial Fibrillation Detection Studies International Journal of Environmental Research and Public Health, 18 (21) (2021)Nezamabadi et al., 2023 Nezamabadi K., Sardaripour N., Haghi B., Forouzanfar M. Unsupervised ECG analysis: A review IEEE Reviews in Biomedical Engineering, 16 (1) (2023), pp. 208-224Nicolini et al., 2014 Nicolini P., Ciulla M.M., Malfatto G., Abbate C., Mari D., Rossi P.D., et al. Autonomic dysfunction in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a cross-sectional case-control study PLoS One, 9 (5) (2014), pp. 1-15Nicolini et al., 2022 Nicolini P., Lucchi T., Abbate C., Inglese S., Tomasini E., Mari D., et al. Autonomic function predicts cognitive decline in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a longitudinal study Frontiers in Aging Neuroscience, 14 (2022)North et al., 2002 North B., Curtis D., Sham P. A note on the calculation of empirical P values from Monte Carlo procedures American Journal of Human Genetics, 71 (2002), pp. 439-441O’Brien et al., 2017 O’Brien P.D., Hinder L.M., Callaghan B.C., Feldman E.L. Neurological consequences of obesity The Lancet Neurology, 16 (6) (2017), pp. 465-477Ottaviani et al., 2019 Ottaviani C., Zingaretti P., Petta A.M., Antonucci G., Thayer J.F., Spitoni G.F. Resting heart rate variability predicts inhibitory control above and beyond impulsivity Journal of Psychophysiology, 33 (3) (2019), pp. 198-206Ranpuria et al., 2007 Ranpuria R., Hall M., Chan C.T., Unruh M. Heart rate variability (HRV) in kidney failure: measurement and consequences of reduced HRV Nephrology Dialysis Transplantation, 23 (2) (2007), pp. 444-449Raudys and Jain, 1991 Raudys S., Jain A. Small sample size effects in statistical pattern recognition: Recommendations for practitioners IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (1991), pp. 252-264Rey, 1941 Rey A. L’examen psychologique dans les cas d’encéphalopathie traumatique. (Les problems) Archives de psychologie (1941), pp. 215-285Rey, 2009 Rey A. REY, Test de copia y de reproducción de memoria de figuras geométricas complejas, Publicaciones de psicología aplicada, TEA Ediciones, S.A. (2009)Reyes Del Paso et al., 2009 Reyes Del Paso G.A., González M.I., Hernández J.A., Duschek S., Gutiérrez N. Tonic blood pressure modulates the relationship between baroreceptor cardiac reflex sensitivity and cognitive performance Psychophysiology, 46 (5) (2009), pp. 932-938Rodríguez-Rodríguez et al., 2023 Rodríguez-Rodríguez I., Ortiz A., Gallego-Molina N.J., Formoso M.A., Woo W.L. EEG interchannel causality to identify source/sink phase connectivity patterns in developmental dyslexia International Journal of Neural Systems, 33 (04) (2023), Article 2350020Rogers et al., 2022 Rogers B., Schaffarczyk M., Clauß M., Mourot L., Gronwald T. The movesense medical sensor chest belt device as single channel ECG for RR interval detection and HRV analysis during resting state and incremental exercise: A cross-sectional validation study Sensors, 22 (5) (2022)Rovere et al., 2011 Rovere M.T.L., Maestri R., Pinna G.D. Baroreflex sensitivity assessment - latest advances and strategies European Cardiology, 7 (2) (2011), pp. 89-92Rubin et al., 2016 Rubin J., Abreu R., Ahern S., Eldardiry H., Bobrow D. Time, frequency & complexity analysis for recognizing panic states from physiologic time-series PervasiveHealth ’16: Proceedings of the 10th EAI international conference on pervasive computing technologies for healthcare, ACM (2016), pp. 81-88Schaich et al., 2020 Schaich C.L., Malaver D., Chen H., Shaltout H.A., Hazzouri A.Z.A., Herrington D.M., et al. Association of heart rate variability with cognitive performance: The multi-ethnic study of atherosclerosis Journal of the American Heart Association, 9 (7) (2020), Article e013827Schölkopf et al., 1998 Schölkopf B., Smola A., Müller K. Nonlinear component analysis as a kernel eigenvalue problem Neural Computation, 10 (5) (1998), pp. 1299-1319Seiffert et al., 2010 Seiffert C., Khoshgoftaar T.M., Hulse J.V., Napolitano A. RUSboost: A hybrid approach to alleviating class imbalance IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40 (2010), pp. 185-197Shaffer and Ginsberg, 2017 Shaffer F., Ginsberg J.P. An overview of heart rate variability metrics and norms Frontiers in Public Health, 5 (2017)Sigcha et al., 2023 Sigcha L., Borzí L., Amato F., Rechichi I., Ramos-Romero C., Cárdenas A., et al. Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review Expert Systems with Applications, 229 (2023), Article 120541Štajner et al., 2019 Štajner S., Saggion H., Ponzetto S.P. Improving lexical coverage of text simplification systems for Spanish Expert Systems with Applications, 118 (2019), pp. 80-91Stroop, 1935 Stroop J. Studies of interference in serial verbal reactions Journal of Experimental Psychology: General, 18 (1935), pp. 643-662Sun et al., 2023 Sun L., Zhang M., Wang B., Tiwari P. Few-shot class-incremental learning for medical time series classification IEEE Journal of Biomedical and Health Informatics, 1 (1) (2023), pp. 1-11, 10.1109/JBHI.2023.3247861Szegedy et al., 2015 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In 2015 IEEE conference on computer vision and pattern recognition, vol. 1 (pp. 1–9).Tarvainen et al., 2014 Tarvainen M.P., Niskanen J.-P., Lipponen J. Kubios HRV–heart rate variability analysis software Computer Methods and Programs in Biomedicine, 113 (1) (2014), pp. 210-220Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology, 1996 Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology M.P. Heart rate variability - standards of measurement, physiological interpretation, and clinical use European Heart Journal, 17 (3) (1996), pp. 354-381Thanou et al., 2016 Thanou A., Stavrakis S., Dyer J., Munroe M.E., James J.A., Merrill J.T. Impact of heart rate variability, a marker for cardiac health, on lupus disease activity Arthritis Research & Therapy, 18 (2016)Thayer et al., 2012 Thayer J.F., Åhs F., Fredrikson M., Sollers J.J., Wager T.D. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health Neuroscience & Biobehavioral Reviews, 36 (2) (2012), pp. 747-756Trites, 2023 Trites R. Grooved pegboard test (2023)Wang, 2012 Wang Q. Kernel principal component analysis and its applications in face recognition and active shape models (2012) arXiv abs/1207.3538Wang and Bi, 2022 Wang R., Bi H.-Y. A predictive model for chinese children with developmental dyslexia—Based on a genetic algorithm optimized back-propagation neural network Expert Systems with Applications, 187 (2022), Article 115949Wang et al., 2023 Wang T., Chen X., Zhang X., Zhou S., Feng Q., Huang M. Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment Expert Systems with Applications (2023), Article 120761Wang et al., 2019 Wang S., Fashanu O.E., Zhao D., Guallar E., Gottesman R.F., Schneider A.L., et al. Relation of elevated resting heart rate in mid-life to cognitive decline over 20 years (from the Atherosclerosis Risk in Communities [ARIC] study) The American Journal of Cardiology, 123 (2) (2019), pp. 334-340Weinstein et al., 2021 Weinstein G., Davis-Plourde K., Beiser A.S., Seshadri S. Autonomic imbalance and risk of dementia and stroke: The framingham study Stroke, 52 (6) (2021), pp. 2068-2076Xiao et al., 2018 Xiao R., Xu Y., Pelter M., Fidler R., Badilini F., Mortara D., et al. Monitoring significant ST changes through deep learning Journal of Electrocardiology, 51 (2018), pp. S78-S82Yamakawa et al., 2020a Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors, 20 (14) (2020)Yamakawa et al., 2020b Yamakawa T., Miyajima M., Fujiwara K., Kano M., Suzuki Y., Watanabe Y., et al. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability Sensors (Basel, Switzerland), 20 (2020)Yang et al., 2008 Yang A.C., Tsai S.-J., Hong C.-J., Yang C.-H., Hsieh C.-H., Liu M.-E. Association between heart rate variability and cognitive function in elderly community-dwelling men without dementia: A preliminary report Journal of the American Geriatrics Society, 56 (2008), pp. 958-960Zubrikhina et al., 2023 Zubrikhina M., Abramova O., Yarkin V., Ushakov V., Ochneva A., Bernstein A., et al. Machine learning approaches to mild cognitive impairment detection based on structural MRI data and morphometric features Cognitive Systems Research, 78 (2023), pp. 87-95141243Heart rate variabilityMild cognitive impairmentDementiaMachine learningSignal processingPublicationORIGINALIdentifying HRV patterns in ECG signals as early markers of dementia.pdfIdentifying HRV patterns in ECG signals as early markers of dementia.pdfapplication/pdf1270634https://repositorio.cuc.edu.co/bitstreams/03c68028-3acc-48b2-ab73-62c7c1bc491b/download9c977fdf782ce559b470fd3d742c6bc0MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/bff400fe-9294-45be-bcc5-9fcc4869e99d/download73a5432e0b76442b22b026844140d683MD52TEXTIdentifying HRV patterns in ECG signals as early markers of dementia.pdf.txtIdentifying HRV patterns in ECG signals as early markers of dementia.pdf.txtExtracted texttext/plain101303https://repositorio.cuc.edu.co/bitstreams/a8f7e25a-6395-4977-82a5-29d7d814c6d3/download022fc60405a24809006346b7276ce0c0MD53THUMBNAILIdentifying HRV patterns in ECG signals as early markers of dementia.pdf.jpgIdentifying HRV patterns in ECG signals as early markers of dementia.pdf.jpgGenerated Thumbnailimage/jpeg15293https://repositorio.cuc.edu.co/bitstreams/cd25d9a9-021e-443f-9351-f2a50f56a0ed/download4aea4f9f164143bead66b212016f422dMD5411323/13928oai:repositorio.cuc.edu.co:11323/139282025-01-21 04:01:02.655https://creativecommons.org/licenses/by/4.0/© 2023 The Authors. Published by Elsevier Ltd.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |