Ullah T, et al. Machine learning-based cardiovascular disease detection using optimal feature selection. IEEE Access. 2024;12:16431–46. https://doi.org/10.1109/ACCESS.2024.3359910.

Article  Google Scholar 

Brites ISG, da Silva LM, Barbosa JLV, Rigo SJ, Correia SD, Leithardt VRQ. Machine learning and iot applied to cardiovascular diseases identification through heart sounds: A literature review. Informatics. 2021. https://doi.org/10.3390/informatics8040073.

Article  Google Scholar 

Schultz WM, et al. Socioeconomic status and cardiovascular outcomes: challenges and interventions. Circulation. 2018;137(20):2166–78. https://doi.org/10.1161/CIRCULATIONAHA.117.029652.

Article  Google Scholar 

Oliveira J, et al. The CirCor DigiScope dataset: from murmur detection to murmur classification. IEEE J Biomed Health Inform. 2022;26(6):2524–35. https://doi.org/10.1109/JBHI.2021.3137048.

Article  Google Scholar 

Ameen A, Fattoh IE, Abd El-Hafeez T, Ahmed K. Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods. J Big Data. 2024. https://doi.org/10.1186/s40537-024-01011-7.

Article  Google Scholar 

Schwalm JD, McKee M, Huffman MD, Yusuf S. Resource effective strategies to prevent and treat cardiovascular disease. Circulation. 2016;133(8):742–55. https://doi.org/10.1161/CIRCULATIONAHA.115.008721.

Article  Google Scholar 

Olawade DB, Aderinto N, Olatunji G, Kokori E, David-Olawade AC, Hadi M. Advancements and applications of artificial intelligence in cardiology: Current trends and future prospects. J Med, Surg, Public Health. 2024;3: 100109. https://doi.org/10.1016/j.glmedi.2024.100109.

Article  Google Scholar 

Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Front Media SA. 2022. https://doi.org/10.3389/fcvm.2022.945726.

Article  Google Scholar 

Lin J, et al. Wearable sensors and devices for real-time cardiovascular disease monitoring. Cell Press. 2021. https://doi.org/10.1016/j.xcrp.2021.100541.

Article  Google Scholar 

Ansari Y, Mourad O, Qaraqe K, Serpedin E. Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023. Front Media SA. 2023. https://doi.org/10.3389/fphys.2023.1246746.

Article  Google Scholar 

Zhang H, et al. Co-learning-assisted progressive dense fusion network for cardiovascular disease detection using ECG and PCG signals. Expert Syst Appl. 2024. https://doi.org/10.1016/j.eswa.2023.122144.

Article  Google Scholar 

Ayano YM, Schwenker F, Dufera BD, Debelee TG. Interpretable machine learning techniques in ECG-based heart disease classification: a systematic review. Multidiscipl Digital Publish Inst (MDPI). 2023. https://doi.org/10.3390/diagnostics13010111.

Article  Google Scholar 

Zheng Y, Guo X, Yang Y, Wang H, Liao K, Qin J. Phonocardiogram transfer learning-based CatBoost model for diastolic dysfunction identification using multiple domain-specific deep feature fusion. Comput Biol Med. 2023. https://doi.org/10.1016/j.compbiomed.2023.106707.

Article  Google Scholar 

Sugiyarto AW, Abadi AM, Sumarna,. Classification of heart disease based on PCG signal using CNN. Telkomnika Telecommun Comput Electron Control. 2021;19(5):1697–706. https://doi.org/10.12928/TELKOMNIKA.v19i5.20486.

Article  Google Scholar 

Chowdhury TH, Poudel KN, Hu Y. Time-frequency analysis, denoising, compression, segmentation, and classification of PCG signals. IEEE Access. 2020;8:160882–90. https://doi.org/10.1109/ACCESS.2020.3020806.

Article  Google Scholar 

Li J, Ke L, Du Q, Ding X, Chen X. Research on the classification of ECG and PCG signals based on BiLSTM-GoogLeNet-DS. Appl Sci (Switzerland). 2022. https://doi.org/10.3390/app122211762.

Article  Google Scholar 

Neha HK, Sardana RK, Tewary S. Arrhythmia detection and classification using ECG and PPG techniques: a review. Phys Eng Sci Med. 2021. https://doi.org/10.1007/s13246-021-01072-5.

Article  Google Scholar 

Sun B, Wang C, Chen X, Zhang Y, Shao H. PPG signal motion artifacts correction algorithm based on feature estimation. Optik (Stuttg). 2019;176:337–49. https://doi.org/10.1016/j.ijleo.2018.09.085.

Article  Google Scholar 

Wu Y, Tang Q, Zhan W, Li S, Chen Z. Res-BiANet: a hybrid deep learning model for arrhythmia detection based on PPG signal. Electronics Switzerland. 2024. https://doi.org/10.3390/electronics13030665.

Article  Google Scholar 

Huang Y, et al. AI-driven system for non-contact continuous nocturnal blood pressure monitoring using fiber optic ballistocardiography. Commun Eng. 2024. https://doi.org/10.1038/s44172-024-00326-w.

Article  Google Scholar 

Goetz L, Seedat N, Vandersluis R, van der Schaar M. Generalization—a key challenge for responsible AI in patient-facing clinical applications. Nat Res. 2024. https://doi.org/10.1038/s41746-024-01127-3.

Article  Google Scholar 

Hajiarbabi M. Heart disease detection using machine learning methods: a comprehensive narrative review. J Med Artif Intell. 2024. https://doi.org/10.21037/jmai-23-152.

Article  Google Scholar 

F. M. Dias et al., ‘Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices’, 2023, [Online]. Available: http://arxiv.org/abs/2307.08766

Torres-Soto J, Ashley EA. Multi-task deep learning for cardiac rhythm detection in wearable devices. NPJ Digit Med. 2020. https://doi.org/10.1038/s41746-020-00320-4.

Article  Google Scholar 

Tereshchenko LG, Josephson ME. Frequency content and characteristics of ventricular conduction. J Electrocardiol. 2015. https://doi.org/10.1016/j.jelectrocard.2015.08.034.

Article  Google Scholar 

Giordano N, Knaflitz M. A novel method for measuring the timing of heart sound components through digital phonocardiography. Sensors Switzerland. 2019. https://doi.org/10.3390/s19081868.

Article  Google Scholar 

Reiss A, Indlekofer I, Schmidt P, Van Laerhoven K (2019) Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks. Sensors 19(14):3079.https://doi.org/10.3390/s19143079

Tamborini A, Gharib M. Listening to heart sounds through the pressure waveform. Sci Rep. 2024;14(1):26824. https://doi.org/10.1038/s41598-024-78554-5.

Article  Google Scholar 

O. Tahar, O. Nadia, B. Redouane, D. Nabil, and B. R. Fethi, ‘New Parameter Available in Phonocardiogram for Blood Pressure Estimation’, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2018, pp. 301–310. https://doi.org/10.1007/978-3-319-78759-6_28.

Xiao Q, et al. Deep learning-based ECG arrhythmia classification: a systematic review. Appl Sci. 2023. https://doi.org/10.3390/app13084964.

Article  Google Scholar 

Johnson AEW, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016. https://doi.org/10.1038/sdata.2016.35.

Article  Google Scholar 

Sabut S, Pandey O, Mishra BSP, Mohanty M. Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network. Phys Eng SciMed 2021;44:135-45.https://doi.org/10.1007/s13246-020-00964-2

‘The Impact of the MIT-BIH Arrhythmia Database History, Lessons Learned, and Its Influence on Current and Future Databases’.

A. L. Goldberger et al., ‘PhysioBank, PhysioToolkit, and PhysioNet Components of a New Research Resource for Complex Physiologic Signals’, 2000. [Online]. Available: http://www.physionet.org

Moody RGMGB. A new method for detecting atrial fibrillation using rr intervals. Comput Cardiol. 1983;10:227–30.

Google Scholar 

Bousseljot KDSA. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomed Tech. 2009. https://doi.org/10.1515/bmte.1995.40.s1.317.

Article  Google Scholar 

Wagner P, et al. PTB-XL a large publicly available electrocardiography dataset. Sci Data. 2020. https://doi.org/10.1038/s41597-020-0495-6.

Article  Google Scholar 

Zheng J, Zhang J, Danioko S, Yao H, Guo H, Rakovski C. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Sci Data. 2020. https://doi.org/10.1038/s41597-020-0386-x.

Article  Google Scholar 

E. F. Gomes, P. J. Bentley, M. Coimbra, E. Pereira, and Y. Deng, ‘Classifying heart sounds: Approaches to the PASCAL challenge’, in HEALTHINF 2013 - Proceedings of the International Conference on Health Informatics, 2013, pp. 337–340. https://doi.org/10.5220/0004234403370340

J. CAI, ‘Deep learning multi-classification heart sound detection method based on high order spectrum and wavelet analysis’, 18, 2024. https://doi.org/10.21203/rs.3.rs-3847114/v1.

Y. Arjoune, T. Nguyen, R. Doroshow, and R. Shekhar, ‘A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy’, https://doi.org/10.1109/ACCESS.2017.DOI.

Y. Zhou, X. Diao, Y. Huo, Y. Liu, X. Fan, and W. Zhao, ‘Masked Transformer for Electrocardiogram Classification’, 2023, [Online]. Available: http://arxiv.org/abs/2309.07136

Liu C, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas. 2016;37(12):2181–213. https://doi.org/10.1088/0967-3334/37/12/2181.

Article  Google Scholar 

Z. Guo et al., ‘SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection’, 23, [Online]. Available: http://arxiv.org/abs/2310.09203

Iyengar N, Peng C-K, Morin R, Goldberger AL, Lipsitz LA. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol-Regulatory, Integrat Comp Physiol. 1996. https://doi.org/10.1152/ajpregu.1996.271.4.R1078.

Article  Google Scholar 

Karlen W, Raman S, Ansermino JM, Dumont GA. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Trans Biomed Eng. 2013;60(7):1946–53. https://doi.org/10.1109/TBME.2013.2246160.

Article  Google Scholar 

Mohagheghian F, et al. Noise reduction in photoplethysmography signals using a convolutional denoising autoencoder with unconventional training scheme. IEEE Trans Biomed Eng. 2024;71(2):456–66. https://doi.org/10.1109/TBME.2023.3307400.

Article  Google Scholar 

Garcia A, Balasubramanian V, Lee J, Gardner R, Gummidipundi S, Hung G