CVD PREDICTION FROM HRV DERIVED FROM WEARABLE PPG
DOI:
https://doi.org/10.26577/jpcsit202542Keywords:
CVD, IHD, HRV, Machine learning, PPG, wearable sensorAbstract
Cardiovascular disease is the leading global cause of death; ischemic heart disease (IHD) is its most common and lethal form, motivating scalable, non-invasive screening. We tested whether a single 60-minute photoplethysmography (PPG) recording from the Zhurek fingertip wearable can distinguish healthy autonomic control from IHD-related dysregulation. Agreement with a three-lead Holter reference was clinically acceptable (HR −0.601 bpm; SDNN +33.1 ms; RMSSD −4.8 ms). Forty hour-long sessions were analyzed (20 healthy, 18–22 years; 20 angiography-confirmed IHD) using eight HRV/demographic features. Mann–Whitney tests showed significant differences for SDNN, LF, HF, Max_HR, BMI, and age (p<0.05), and a two-component PCA (49.5% variance) separated cohorts without labels. SHAP for a CatBoost model highlighted LF and age as strongest positive contributors and HF as protective. Thus, one-hour PPG preserves diagnostically useful autonomic signatures, enabling ~24× shorter monitoring than Holter and supporting scalable ambulatory IHD risk stratification.
Downloads
References
‘Cardiovascular diseases (CVDs)’. Accessed: Jul. 21, 2025. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
M. A. Khan et al., ‘Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study’, Cureus, Jul. 2020, doi: 10.7759/cureus.9349.
tengrinews.kz, ‘Названа самая распространенная болезнь среди казахстанцев’, Главные новости Казахстана - Tengrinews.kz. Accessed: Jul. 21, 2025. [Online]. Available: https://tengrinews.kz/kazakhstan_news/nazvana-samaya-rasprostranennaya-bolezn-sredi-kazahstantsev-503527/
P. Severino et al., ‘Ischemic Heart Disease Pathophysiology Paradigms Overview: From Plaque Activation to Microvascular Dysfunction’, IJMS, vol. 21, no. 21, p. 8118, Oct. 2020, doi: 10.3390/ijms21218118.
Ł. J. Janicki, W. Leoński, J. S. Janicki, M. Nowotarski, M. Dziuk, and R. Piotrowicz, ‘Comparative Analysis of the Diagnostic Effectiveness of SATRO ECG in the Diagnosis of Ischemia Diagnosed in Myocardial Perfusion Scintigraphy Performed Using the SPECT Method’, Diagnostics, vol. 12, no. 2, p. 297, Jan. 2022, doi: 10.3390/diagnostics12020297.
Ștefania-T. Duca et al., ‘Enhancing Comprehensive Assessments in Chronic Heart Failure Caused by Ischemic Heart Disease: The Diagnostic Utility of Holter ECG Parameters’, Medicina, vol. 60, no. 8, p. 1315, Aug. 2024, doi: 10.3390/medicina60081315.
N. Singh et al., ‘Heart Rate Variability: An Old Metric with New Meaning in the Era of using mHealth Technologies for Health and Exercise Training Guidance. Part One: Physiology and Methods’, Arrhythmia & Electrophysiology Review, vol. 7, no. 3, p. 193, 2018, doi: 10.15420/aer.2018.27.2.
P. Ribeiro, J. Sá, D. Paiva, and P. M. Rodrigues, ‘Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis’, Bioengineering, vol. 11, no. 1, p. 58, Jan. 2024, doi: 10.3390/bioengineering11010058.
S. P. Gaine et al., ‘Multimodality Imaging in the Detection of Ischemic Heart Disease in Women’, JCDD, vol. 9, no. 10, p. 350, Oct. 2022, doi: 10.3390/jcdd9100350.
L. Wang, T. Bi, J. Hao, and T. H. Zhou, ‘Heart Diseases Recognition Model Based on HRV Feature Extraction over 12-Lead ECG Signals’, Sensors, vol. 24, no. 16, p. 5296, Aug. 2024, doi: 10.3390/s24165296.
G. Doolub et al., ‘Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease’, Medical Sciences, vol. 11, no. 1, p. 20, Feb. 2023, doi: 10.3390/medsci11010020.
L. Verma and S. Srivastava, ‘A Data Mining Model for Coronary Artery Disease Detection Using Noninvasive Clinical Parameters’, Indian Journal of Science and Technology, vol. 9, no. 48, Dec. 2016, doi: 10.17485/ijst/2016/v9i48/105707.
M. Sayadi, V. Varadarajan, F. Sadoughi, S. Chopannejad, and M. Langarizadeh, ‘A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters’, Life, vol. 12, no. 11, p. 1933, Nov. 2022, doi: 10.3390/life12111933.
A. M. Alaa, T. Bolton, E. Di Angelantonio, J. H. F. Rudd, and M. Van Der Schaar, ‘Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants’, PLoS ONE, vol. 14, no. 5, p. e0213653, May 2019, doi: 10.1371/journal.pone.0213653.
H. ChuDuc, K. NguyenPhan, and D. NguyenViet, ‘A Review of Heart Rate Variability and its Applications’, APCBEE Procedia, vol. 7, pp. 80–85, 2013, doi: 10.1016/j.apcbee.2013.08.016.
C. Brinza et al., ‘Heart Rate Variability in Acute Myocardial Infarction: Results of the HeaRt-V-AMI Single-Center Cohort Study’, JCDD, vol. 11, no. 8, p. 254, Aug. 2024, doi: 10.3390/jcdd11080254.
A. Hazra, S. K. Mandal, A. Gupta, A. Mukherjee, and A. Mukherjee, ‘Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review’.
M. Trigka and E. Dritsas, ‘Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models’, Sensors, vol. 23, no. 3, p. 1193, Jan. 2023, doi: 10.3390/s23031193.
B. A. Marzoog et al., ‘Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis’, Biomedicines, vol. 12, no. 12, p. 2814, Dec. 2024, doi: 10.3390/biomedicines12122814.
G. Sibrecht, J. Piskorski, T. Krauze, and P. Guzik, ‘Heart Rate Asymmetry, Its Compensation, and Heart Rate Variability in Healthy Adults during 48-h Holter ECG Recordings’, JCM, vol. 12, no. 3, p. 1219, Feb. 2023, doi: 10.3390/jcm12031219.
X. Wu, Q. Yang, J. Li, and F. Hou, ‘Investigation on the Prediction of Cardiovascular Events Based on Multi-Scale Time Irreversibility Analysis’, Symmetry, vol. 13, no. 12, p. 2424, Dec. 2021, doi: 10.3390/sym13122424.
B. M. Curtis and J. H. O’Keefe, ‘Autonomic Tone as a Cardiovascular Risk Factor: The Dangers of Chronic Fight or Flight’, Mayo Clinic Proceedings, vol. 77, no. 1, pp. 45–54, Jan. 2002, doi: 10.4065/77.1.45.
R. A. Rather and V. Dhawan, ‘Genetic markers: Potential candidates for cardiovascular disease’, International Journal of Cardiology, vol. 220, pp. 914–923, Oct. 2016, doi: 10.1016/j.ijcard.2016.06.251.
S. De Rosa, B. Arcidiacono, E. Chiefari, A. Brunetti, C. Indolfi, and D. P. Foti, ‘Type 2 Diabetes Mellitus and Cardiovascular Disease: Genetic and Epigenetic Links’, Front. Endocrinol., vol. 9, Jan. 2018, doi: 10.3389/fendo.2018.00002.
J. L. Moraes, M. X. Rocha, G. G. Vasconcelos, J. E. Vasconcelos Filho, V. H. C. De Albuquerque, and A. R. Alexandria, ‘Advances in Photopletysmography Signal Analysis for Biomedical Applications’, Sensors, vol. 18, no. 6, p. 1894, Jun. 2018, doi: 10.3390/s18061894.
M. Elgendi et al., ‘The use of photoplethysmography for assessing hypertension’, npj Digit. Med., vol. 2, no. 1, Jun. 2019, doi: 10.1038/s41746-019-0136-7.
M. A. Almarshad, M. S. Islam, S. Al-Ahmadi, and A. S. BaHammam, ‘Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review’, Healthcare, vol. 10, no. 3, p. 547, Mar. 2022, doi: 10.3390/healthcare10030547.
K. B. Kim and H. J. Baek, ‘Photoplethysmography in Wearable Devices: A Comprehensive Review of Technological Advances, Current Challenges, and Future Directions’, Electronics, vol. 12, no. 13, p. 2923, Jul. 2023, doi: 10.3390/electronics12132923.
M. Shabaan et al., ‘Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis’, BMC Med Inform Decis Mak, vol. 20, no. 1, Dec. 2020, doi: 10.1186/s12911-020-01199-7.
I. C. Dipto, T. Islam, H. M. M. Rahman, and M. A. Rahman, ‘Comparison of Different Machine Learning Algorithms for the Prediction of Coronary Artery Disease’, JDAIP, vol. 08, no. 02, pp. 41–68, 2020, doi: 10.4236/jdaip.2020.82003.
R. C. Ripan et al., ‘A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection’, SN COMPUT. SCI., vol. 2, no. 2, Apr. 2021, doi: 10.1007/s42979-021-00518-7.
V. Jahmunah, E. Y. K. Ng, T. R. San, and U. R. Acharya, ‘Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals’, Computers in Biology and Medicine, vol. 134, p. 104457, Jul. 2021, doi: 10.1016/j.compbiomed.2021.104457.
K. Kusunose et al., ‘A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images’, JACC: Cardiovascular Imaging, vol. 13, no. 2, pp. 374–381, Feb. 2020, doi: 10.1016/j.jcmg.2019.02.024.
M. Zreik et al., ‘Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography’, IEEE Trans. Med. Imaging, vol. 39, no. 5, pp. 1545–1557, May 2020, doi: 10.1109/tmi.2019.2953054.
A. Ogunpola, F. Saeed, S. Basurra, A. M. Albarrak, and S. N. Qasem, ‘Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases’, Diagnostics, vol. 14, no. 2, p. 144, Jan. 2024, doi: 10.3390/diagnostics14020144.
R. M. Birn et al., ‘The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity’, Brain Connectivity, vol. 4, no. 7, pp. 511–522, Sep. 2014, doi: 10.1089/brain.2014.0284.
G. Sanchez-Delgado et al., ‘Reliability of resting metabolic rate measurements in young adults: Impact of methods for data analysis’, Clinical Nutrition, vol. 37, no. 5, pp. 1618–1624, Oct. 2018, doi: 10.1016/j.clnu.2017.07.026.
D. McDuff, S. Gontarek, and R. Picard, ‘Remote measurement of cognitive stress via heart rate variability’, in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL: IEEE, Aug. 2014, pp. 2957–2960. doi: 10.1109/embc.2014.6944243.
N. Gullett, Z. Zajkowska, A. Walsh, R. Harper, and V. Mondelli, ‘Heart rate variability (HRV) as a way to understand associations between the autonomic nervous system (ANS) and affective states: A critical review of the literature’, International Journal of Psychophysiology, vol. 192, pp. 35–42, Oct. 2023, doi: 10.1016/j.ijpsycho.2023.08.001.
A. Zierle-Ghosh and A. Jan, ‘Physiology, Body Mass Index’, in StatPearls, Treasure Island (FL): StatPearls Publishing, 2025. Accessed: Jul. 21, 2025. [Online]. Available: http://www.ncbi.nlm.nih.gov/books/NBK535456/
J. Zeng et al., ‘High vagally mediated resting-state heart rate variability is associated with superior working memory function’, Front. Neurosci., vol. 17, Feb. 2023, doi: 10.3389/fnins.2023.1119405.
J. Hart, ‘OPTIMAL LEVEL OF HEART RATE VARIABILITY FOR SPINAL ADJUSTMENT: A CASE REPORT’, JCC, vol. 2, no. 1, pp. 103–108, Sep. 2019.
M. Volpe et al., ‘How cardiologists can manage excess body weight and related cardiovascular risk. An expert opinion’, International Journal of Cardiology, vol. 381, pp. 101–104, Jun. 2023, doi: 10.1016/j.ijcard.2023.03.054.
S. Kathiresan and D. Srivastava, ‘Genetics of Human Cardiovascular Disease’, Cell, vol. 148, no. 6, pp. 1242–1257, Mar. 2012, doi: 10.1016/j.cell.2012.03.001.
