<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2d1 20170631//EN" "JATS-journalpublishing1.dtd">
<ArticleSet>
<Article>
<Journal>
<PublisherName>theaimsjournal</PublisherName>
<JournalTitle>Allana Management Journal of Research, Pune</JournalTitle>
<PISSN> 2581 - 3137 (</PISSN>
<EISSN>) 2231 - 0290 (Print)</EISSN>
<Volume-Issue>Volume 15, Issue 2</Volume-Issue>
<PartNumber/>
<IssueTopic>Multidisciplinary</IssueTopic>
<IssueLanguage>English</IssueLanguage>
<Season>July 2025 - Dec 2025</Season>
<SpecialIssue>N</SpecialIssue>
<SupplementaryIssue>N</SupplementaryIssue>
<IssueOA>Y</IssueOA>
<PubDate>
<Year>2025</Year>
<Month>07</Month>
<Day>28</Day>
</PubDate>
<ArticleType>Information Technology Management</ArticleType>
<ArticleTitle>ARTIFICIAL INTELLIGENCE BASED PROGNOSIS OF CARDIOVASCULAR ABNORMALITY USING PHOTOPLETHYSMOGRAPHY (PPG) SENSORS IN SMART WATCHES</ArticleTitle>
<SubTitle/>
<ArticleLanguage>English</ArticleLanguage>
<ArticleOA>Y</ArticleOA>
<FirstPage>26</FirstPage>
<LastPage>42</LastPage>
<AuthorList>
<Author>
<FirstName>Mir Mehdi Ali</FirstName>
<LastName>Jafri</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>N</CorrespondingAuthor>
<ORCID/>
</Author>
</AuthorList>
<DOI>https://doi.org/10.62223/AMJR.2025.150204</DOI>
<Abstract>Purpose: This study aims to address the critical issue of delayed detection of cardiovascular heart diseases, a leading cause of sudden death and heart failure in younger populations. It seeks to develop an innovative, personalized, and ambulatory system for early detection and timely intervention to prevent life-threatening outcomes.
Design/Methodology/Approach: The research proposes a novel approach utilizing Photoplethysmography (PPG) integrated into smartwatches and wearable devices. An artificially intelligent prognosis system, combining Convolutional Neural Network – Long-Short Term Memory (CNN-LSTM) and random forest algorithms, is employed to detect and quantify cardiovascular abnormalities. Datasets such as MIMIC-IV and PPG-DaLiA are used to train and validate the framework, ensuring continuous monitoring during daily activities.
Findings: The CNN-LSTM and random forest-based system effectively detects cardiovascular abnormalities with high accuracy, quantifying the degree of irregularity. The framework demonstrates the potential for real-time, non-invasive monitoring, enabling early identification of cardiovascular issues before they escalate.
Research Limitations/Implications: The study relies on existing datasets, which may not fully represent diverse populations or real-world conditions. The accuracy of PPG-based monitoring may be affected by factors such as motion artifacts or device placement. Further research is needed to validate the system across broader demographics and refine its robustness for clinical adoption.
Practical Implications: The proposed system offers an affordable, privacy-conscious, and accessible solution for continuous cardiovascular monitoring. By integrating with widely used wearable devices, it empowers individuals to proactively manage their heart health without requiring frequent medical consultations or specialized setups.
Originality/Value: This research introduces a pioneering approach to cardiovascular monitoring by combining PPG with advanced AI techniques (CNN-LSTM and random forest) in wearable technology. Its emphasis on privacy, affordability, and real-time detection during daily activities adds significant value to preventive healthcare, addressing the critical gap in early diagnosis of cardiovascular diseases.</Abstract>
<AbstractLanguage>English</AbstractLanguage>
<Keywords>Prognosis, Artificial Intelligence, Machine learning, Deep Learning, Photoplethysmography (PPG), Sensors, Cardiovascular heart disease, Abnormality, Smart Watches</Keywords>
<URLs>
<Abstract>https://www.theaimsjournal.org/ubijournal-v1copy/journals/abstract.php?article_id=15871&title=ARTIFICIAL INTELLIGENCE BASED PROGNOSIS OF CARDIOVASCULAR ABNORMALITY USING PHOTOPLETHYSMOGRAPHY (PPG) SENSORS IN SMART WATCHES</Abstract>
</URLs>
<References>
<ReferencesarticleTitle>References</ReferencesarticleTitle>
<ReferencesfirstPage>16</ReferencesfirstPage>
<ReferenceslastPage>19</ReferenceslastPage>
<References>Ali, F., El–Sappagh, S., Islam, S. M. R., Kwak, D., Ali, A., Imran, M., and; Kwak, K. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208–222. https://doi.org/10.1016/j.inffus.2020.06.008
Bae, S., Borac, S., Emre, Y., Wang, J., Wu, J., Kashyap, M., ... Po, M. J. (2022). Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms. Communications Medicine, 2(1). https://doi.org/10.1038/s43856-022-00102-x
Banu, N. K. S., and; Swamy, S. (2016). Prediction of heart disease at early stage using data mining and big data analytics: A survey. In IEEE International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT). https://doi.org/10.1109/iceeccot.2016.7955226
Berry, J. D., Lloyd?Jones, D. M., Garside, D. B., and; Greenland, P. (2007). Framingham risk score and prediction of coronary heart disease death in young men. American Heart Journal, 154(1), 80–86. https://doi.org/10.1016/j.ahj.2007.03.042
Chong, J. W., Esa, N., McManus, D. D., and; Chon, K. H. (2015). Arrhythmia discrimination using a smart phone. IEEE Journal of Biomedical and Health Informatics,https://doi.org/10.1109/jbhi.2015.2418195
Frederix, I., Caiani, E. G., Dendale, P., Anker, S. D., Bax, J. J., Band;ouml;hm, A., ... Van Der Velde, E. (2019). ESC e-Cardiology Working Group position paper: Overcoming challenges in digital health implementation in cardiovascular medicine. European Journal of Preventive Cardiology, 26(11), 1166–1177. https://doi.org/10.1177/2047487319832394
Gawande, N., and; Barhatte, A. (2017). Heart diseases classification using convolutional neural network. In Proceedings of the 2nd International Conference on Communication and Electronics Systems (ICCES).https://doi.org/10.1109/cesys.2017.8321264
Ghafoori, M., Clevenger, C. M., Abdallah, M., and; Rens, K. (2023). Heart rate modeling and prediction of construction workers based on physical activity using deep learning. Automation in Construction, 155, 105077.https://doi.org/10.1016/j.autcon.2023.105077
Ghosal, P., Sarkar, D., Kundu, S., Roy, S., Sinha, A., and; Ganguli, S. (2017). ECG beat quality assessment using self-organizing map. IEEE Explore. https://doi.org/10.1109/optronix.2017.8349994
Hu, S., Wei, H., Chen, Y., and; Tan, J. (2012). A real-time cardiac arrhythmia classification system with wearable sensor networks. Sensors, 12(9), 12844–12869. https://doi.org/10.3390/s120912844
Huang, J., Wang, J., Ramsey, E., Leavey, G., Chico, T. J. A., and; Condell, J. (2022). Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: A review. Sensors, 22(20),8002.https://doi.org/10.3390/s22208002
Islam, M. T., Rafa, S. R., and; Kibria, M. G. (2020). Early prediction of heart disease using PCA and hybrid genetic algorithm with k-means. In 23rd International Conference on Computer and Information Technology (ICCIT). https://doi.org/10.1109/iccit51783.2020.9392655
Jahangiry, L., Farhangi, M. A., and; Rezaei, F. (2017). Framingham risk score for estimation of 10-years of cardiovascular diseases risk in patients with metabolic syndrome. Journal of Health, Population and Nutrition,
36(1).https://doi.org/10.1186/s41043-017-0114-0
Junaid, M. J. A., and; Kumar, R. (2020). Data science and its application in heart disease prediction. In International Conference on Intelligent Engineering and Management (ICIEM).https://doi.org/10.1109/iciem48762.2020.9160056
Kumar, A., and; Sinha, N. (2020). Cardiovascular disease in India: A 360 degree overview. Medical Journal Armed Forces India, 76(1), 1–3. https://doi.org/10.1016/j.mjafi.2019.12.005
Li, C., Hu, X., and; Zhang, L. (2017). The IoT-based heart disease monitoring system for pervasive healthcare service. Procedia Computer Science, 112, 2328–2334. https://doi.org/10.1016/j.procs.2017.08.265
Li, Y., Luo, J., Dai, Q., Eshraghian, J. K., Ling, B. W., Zheng, C., and; Wang, X. (2023). A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction. Biomedical Signal Processing and Control, 79, 104188. https://doi.org/10.1016/j.bspc.2022.104188
Lou, Y., Lin, C., Fang, W., Lee, C., and; Lin, C. (2023). Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification. Computer Methods and Programs in Biomedicine, 231, 107359.
Macfarlane, P. W. (2022). The application of computer techniques to ECG interpretation. Hearts, 3(1), 1–5. https://doi.org/10.3390/hearts3010001
Macfarlane, P. W., Lloyd, S. M., Singh, D., Hamde, S. T., Clark, E., Devine, B., Francq, B., and; Kumar, V. (2015). Normal limits of the electrocardiogram in Indians. Journal of Electrocardiology, 48(4), 652–668.https://doi.org/10.1016/j.jelectrocard.2015.04.013
Malakouti, S. M. (2023). Heart disease classification based on ECG using machine learning models. Biomedical Signal Processing and Control, 84, 104796. https://doi.org/10.1016/j.bspc.2023.104796
Martand;iacute;nez?Selland;eacute;s, M., and; Marina-Breysse, M. (2023). Current and future use of artificial intelligence in electrocardiography. Journal of Cardiovascular Development and Disease, 10(4), 175. https://doi.org/10.3390/jcdd10040175
Mercer, B., Leese, L., Ahmed, N., Holden, A. V., and; Tayebjee, M. H. (2020). A simple adaptation of a handheld ECG recorder to obtain chest lead equivalents. Journal of Electrocardiology, 63, 54–56. https://doi.org/10.1016/j.jelectrocard.2020.10.005
Mischie, N., and; Albu, A. (2020). Artificial neural networks for diagnosis of coronary heart disease. 2020 International Conference on e-Health and Bioengineering (EHB). https://doi.org/10.1109/ehb50910.2020.9280271
Mizuno, A., Miyashita, M., Hayashi, A., Kawai, F., Niwa, K., Utsunomiya, A., Kohsaka, S., Kohno, T., Yamamoto, T., Takayama, M., and; Anzai, T. (2017). Potential palliative care quality indicators in heart disease patients: A review of the literature. Journal of Cardiology, 70(4), 335–341. https://doi.org/10.1016/j.jjcc.2017.02.010
Pand;eacute;rez, M., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcand;iacute;a, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P. R., Talati, N., Nag, D., Gummidipundi, S., Beatty, A. L., Hills, M. T., Desai, S., … Investigators, A. H. S. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. The New England Journal of Medicine, 381(20), 1909–1917.
Prakash, J. V., Vijay, S. A. A., Kumar, P. G., and; Karthikeyan, N. K. (2024). A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease. Computers in Biology and Medicine, 170, 107977. https://doi.org/10.1016/j.compbiomed.2024.107977
Rijnbeek, P. R., Van Herpen, G., Bots, M. L., Man, S., Verweij, N., Hofman, A., Hillege, H. L., Numans, M. E., Swenne, C. A., Witteman, J. C. M., and; Kors, J. A. (2014). Normal values of the electrocardiogram for ages 16–90 years. Journal of Electrocardiology, 47(6), 914–921. https://doi.org/10.1016/j.jelectrocard.2014.07.022
Romiti, S., Vinciguerra, M., Saade, W., Cortajarena, I. A., and; Greco, E. (2020). Artificial intelligence (AI) and cardiovascular diseases: An unexpected alliance. Cardiology Research and Practice, 2020, 1–8. https://doi.org/10.1155/2020/4972346
Schmidt, S. E., Holst?Hansen, C., Hansen, J. H. L., Toft, E., and; Struijk, J. (2015). Acoustic features for the identification of coronary artery disease. IEEE Transactions on Biomedical Engineering, 62(11), 2611–2619. https://doi.org/10.1109/tbme.2015.2432129
Seshadri, D. R., Bittel, B., Browsky, D., Houghtaling, P. L., Drummond, C. K., Desai, M. Y., and; Gillinov, A. M. (2020). Accuracy of the Apple Watch 4 to measure heart rate in patients with atrial fibrillation. IEEE Journal of Translational Engineering in Health and Medicine, 8, 1–4. https://doi.org/10.1109/jtehm.2019.2950397
Singh, P. K., Kourav, P. S., Mohapatra, S. S. D., Kumar, V., and; Panda, S. (2024). Human heart health prediction using GAIT parameters and machine learning model. Biomedical Signal Processing and Control, 88, 105696. https://doi.org/10.1016/j.bspc.2023.105696
Stehlik, J., Schmalfuss, C., Bozkurt, B., Nativi?Nicolau, J., Wohlfahrt, P., Wegerich, S., Rose, K. A., Ray, R., Schofield, R. S., Deswal, A., Sekaric, J., Anand, S., Richards, D., Hanson, H., Pipke, M., and; Pham, M. (2020). Continuous wearable monitoring analytics predict heart failure hospitalization. Circulation: Heart Failure, 13(3). https://doi.org/10.1161/circheartfailure.119.006513
Wang, W., Brinker, A. D., Stuijk, S., and; De Haan, G. (2017). Algorithmic principles of remote PPG. IEEE Transactions on Biomedical Engineering, 64(7), 1479–1491.https://doi.org/10.1109/tbme.2016.2609282
Wijaya, R., Prihatmanto, A. S., and; Kuspriyanto. (2013). Preliminary design of estimation heart disease by using machine learning ANN within one year. Joint International Conference on Rural Information and; Communication Technology and Electric-Vehicle Technology (rICT and; ICeV-T), Bandung-Bali, Indonesia. https://doi.org/10.1109/rict-icevt.2013.6741541
Wu, J., Kors, J. A., Rijnbeek, P. R., Van Herpen, G., Lu, Z., and; Xu, C. (2003). Normal limits of the electrocardiogram in Chinese subjects. International Journal of Cardiology, 87(1), 37–51. https://doi.org/10.1016/s0167-5273(02)00248-6
Yu, J., Park, S. J., Kwon, S., Cho, K., and; Lee, H. (2022). AI-based stroke disease prediction system using ECG and PPG bio-signals. IEEE Access, 10, 43623–43638. https://doi.org/10.1109/access.2022.3169284
Zaman, R., Cho, C. H., Hartmann-Vaccarezza, K., Phan, T. N., Yoon, G., and; Chong, J. W. (2017). Novel fingertip image-based heart rate detection methods for a smartphone. Sensors, 17(2), 358. https://doi.org/10.3390/s17020358
Zhang, H., Zhang, P., Lin, F., Chao, L., Wang, Z., Ma, F., and; Li, Q. (2024). Co-learning–assisted progressive dense fusion network for cardiovascular disease detection using ECG and PCG signals. Expert Systems With Applications, 238, 122144. https://doi.org/10.1016/j.eswa.2023.122144
Zishan, M. O., Shihab, H., Islam, S. S., Riya, M. A., Rahman, G. M., and; Noor, J. (2024). Dense neural network based arrhythmia classification on low-cost and low-compute micro-controller. Expert Systems With Applications, 239, 122560. https://doi.org/10.1016/j.eswa.2023.122560</References>
</References>
</Journal>
</Article>
</ArticleSet>