journal article·2025-07

Detection of Abnormal Cardiac Rhythms Using Feature Fusion Technique with Heart Sound Spectrograms

Saif Ur Rehman Khan, Zia Khan

Journal of Bionic Engineering· Volume 22· Issue 4
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Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

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Research context

OpenAlex topics: Phonocardiography and Auscultation Techniques · ECG Monitoring and Analysis · EEG and Brain-Computer Interfaces

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References (41)

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1. Yang, J., & Xi, C. (2022). The diagnosis of congestive heart failure based on generalized multiscale entropy-wavelet leaders. Entropy, 24(12), 1763. https://doi.org/10.3390/e2412176310.3390/e24121763 ↗
2. Chen, P., & Zhang, Q. (2020). Classification of heart sounds using discrete time-frequency energy feature based on S transform and the wavelet threshold denoising. Biomedical Signal Processing and Control, 57, 101684. https://doi.org/10.1016/j.bspc.2019.10168410.1016/j.bspc.2019.101684 ↗
3. Chen, Y., Su, B., Zeng, W., Yuan, C., & Ji, B. (2023). Abnormal heart sound detection from unsegmented phonocardiogram using deep features and shallow classifiers. Multimedia Tools and Applications, 82(17), 26859–26883. https://doi.org/10.1007/s11042-022-14315-810.1007/s11042-022-14315-8 ↗
4. Fijačko, N., Prosen, G., Abella, B. S., Metličar, Š., & Štiglic, G. (2023). Can novel multimodal chatbots such as Bing chat enterprise, chatGPT-4 pro, and Google bard correctly interpret electrocardiogram images? Resuscitation. https://doi.org/10.1016/j.resuscitation.2023.11000910.1016/j.resuscitation.2023.110009 ↗
5. Gentille-Lorente, D., Hernández-Pinilla, A., Satue-Gracia, E., Muria-Subirats, E., Forcadell-Peris, M. J., Gentille-Lorente, J., Ballesta-Ors, J., Martín-Lujan, F. M., & Clua-Espuny, J. L. (2023). Echocardiography and electrocardiography in detecting atrial cardiomyopathy: A promising path to predicting cardioembolic strokes and atrial fibrillation. Journal of Clinical Medicine, 12(23), 7315. https://doi.org/10.3390/jcm1223731510.3390/jcm12237315 ↗
6. Gjoreski, M., Gradišek, A., Budna, B., Gams, M., & Poglajen, G. (2020). Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds. Ieee Access: Practical Innovations, Open Solutions, 8, 20313–20324. https://doi.org/10.1109/ACCESS.2020.296890010.1109/ACCESS.2020.2968900 ↗
7. Glezeva, N., & Baugh, J. (2014). Role of inflammation in the pathogenesis of heart failure with preserved ejection fraction and its potential as a therapeutic target. Heart Failure Reviews, 19, 681–694. https://doi.org/10.1007/s10741-013-9405-810.1007/s10741-013-9405-8 ↗
8. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision & pattern recognition, Las Vegas, NV, USA, pp. 770–778. https://doi.org/10.1109/CVPR.2016.9010.1109/CVPR.2016.90 ↗
9. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. ArXiv Preprint arXiv: 170404861. https://doi.org/10.48550/arXiv.1704.0486110.48550/arXiv.1704.04861 ↗
10. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision & pattern recognition, Honolulu, HI, USA, 2261–2269. https://doi.org/10.48550/arXiv.1608.0699310.48550/arXiv.1608.06993 ↗
11. Khan, S. U. R., & Asif, S. (2024). Oral cancer detection using feature-level fusion and novel self-attention mechanisms. Biomedical Signal Processing and Control, 95, 106437. https://doi.org/10.1016/j.bspc.2024.10643710.1016/j.bspc.2024.106437 ↗
12. Khan, S. U. R., Asif, S., Bilal, O., & Ali, S. (2024). Deep hybrid model for Mpox disease diagnosis from skin lesion images. International Journal of Imaging Systems and Technology, 34(2), e23044. https://doi.org/10.1002/ima.2304410.1002/ima.23044 ↗
13. Khan, S. U. R., Asif, S., Zhao, M., Zou, W., Li, Y., & Li, X. (2024). Optimized deep learning model for comprehensive medical image analysis across multiple modalities. Neurocomputing. https://doi.org/10.1016/j.neucom.2024.12918210.1016/j.neucom.2024.129182 ↗
14. Khan, S. U. R., Zhao, M., Asif, S., Chen, X., & Zhu, Y. (2023). GLNET: global–local CNN’s-based informed model for detection of breast cancer categories from histopathological slides. The Journal of Supercomputing. https://doi.org/10.1007/s11227-023-05742-x10.1007/s11227-023-05742-x ↗
15. Khan, U. S., & Khan, S. U. R. (2024). Boost diagnostic performance in retinal disease classification utilizing deep ensemble classifiers based on OCT. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-19922-110.1007/s11042-024-19922-1 ↗
16. Zhang, C. J., Tang, F. Q., Cai, H. P., & Qian, Y. F. (2024). Heart failure classification using deep learning to extract Spatiotemporal features from Ecg. BMC Medical Informatics and Decision Making, 24(1), 17. https://doi.org/10.1186/s12911-024-02415-410.1186/s12911-024-02415-4 ↗
17. Kumar, S., Gola, K. K., Jee, N., & Singh, B. M. (2024). Optimized feature fusion-based modified cascaded kernel extreme learning machine for heart disease prediction in E-healthcare. Computer Methods in Biomechanics and Biomedical Engineering, 27(8), 980–993. https://doi.org/10.1080/10255842.2023.221852010.1080/10255842.2023.2218520 ↗
18. Kuveskar, M. R., Pawar, S. K., Pandit, S. V., Dongre, Y., Vast, P. P., & Vanjari, H. B. (2024). A hybrid iot-enabled transfer learning model for accurate diagnosis of coronary heart disease. Journal of Electrical Systems, 20(2s), 1145–1158. https://doi.org/10.52783/jes.176010.52783/jes.1760 ↗
19. Mishra, J., & Tiwari, M. (2022). Detection of heart disease utilising a feature fusion technique and machine learning-augmented classification. SN Computer Science, 3(5), 406. https://doi.org/10.1007/s42979-022-01294-810.1007/s42979-022-01294-8 ↗
20. Mongirdienė, A., & Liobikas, J. (2022). Phenotypic and functional heterogeneity of monocyte subsets in chronic heart failure patients. Biology, 11(2), 195. https://doi.org/10.3390/biology1102019510.3390/biology11020195 ↗
21. Nahak, S., & Saha, G. (2020). A fusion based classification of normal, arrhythmia and congestive heart failure in ECG. National Conference on Communications, Kharagpur, India, 1–6. https://doi.org/10.1109/NCC48643.2020.905609510.1109/NCC48643.2020.9056095 ↗
22. Ning, W., Li, S., Wei, D., Guo, L. Z., & Chen, H. (2020). Automatic detection of congestive heart failure based on a hybrid deep learning algorithm in the internet of medical things. IEEE Internet of Things Journal, 8(16), 12550–12558. https://doi.org/10.1109/JIOT.2020.302310510.1109/JIOT.2020.3023105 ↗
23. Shorewala, V. (2021). Early detection of coronary heart disease using ensemble techniques. Informatics in Medicine Unlocked, 26, 100655. https://doi.org/10.1016/j.imu.2021.10065510.1016/j.imu.2021.100655 ↗
24. Plati, D. K., Tripoliti, E. E., Bechlioulis, A., Rammos, A., Dimou, I., Lakkas, L., Watson, C., McDonald, K., Ledwidge, M., & Pharithi, R. (2021). A machine learning approach for chronic heart failure diagnosis. Diagnostics, 11(10), 1863. https://doi.org/10.3390/diagnostics1110186310.3390/diagnostics11101863 ↗
25. Sharma, M., Patel, S., & Acharya, U. R. (2023). Expert system for detection of congestive heart failure using optimal wavelet and heart rate variability signals for wireless cloud-based environment. Expert Systems, 40(4), e12903. https://doi.org/10.1111/exsy.1290310.1111/exsy.12903 ↗
26. Prabhakararao, E., & Dandapat, S. (2022). Congestive heart failure detection from ECG signals using deep residual neural network. IEEE Transactions on Systems Man and Cybernetics: Systems, 53(5), 3008–3018. https://doi.org/10.1109/TSMC.2022.322184310.1109/TSMC.2022.3221843 ↗
27. Qadri, A. M., Raza, A., Munir, K., & Almutairi, M. S. (2023). Effective feature engineering technique for heart disease prediction with machine learning. Ieee Access: Practical Innovations, Open Solutions, 11, 56214–56224. https://doi.org/10.1109/ACCESS.2023.328148410.1109/ACCESS.2023.3281484 ↗
28. Rani, P., Kumar, R., Jain, A., Lamba, R., Sachdeva, R. K., Kumar, K., & Kumar, M. (2024). An extensive review of machine learning and deep learning techniques on heart disease classification and prediction. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-024-10075-w10.1007/s11831-024-10075-w ↗
29. Shahzad, I., Khan, S. U. R., Waseem, A., Abideen, Z. U., & Liu, J. (2024). Enhancing ASD classification through hybrid attention-based learning of facial features. Signal Image and Video Processing. https://doi.org/10.1007/s11760-024-03167-410.1007/s11760-024-03167-4 ↗
30. Rey, J. R., Caro-Codón, J., Rosillo, S. O., Iniesta, Á. M., Castrejón‐Castrejón, S., Marco‐Clement, I., Martín‐Polo, L., Merino‐Argos, C., Rodríguez‐Sotelo, L., & García‐Veas, J. M. (2020). Heart failure in COVID‐19 patients: Prevalence, incidence and prognostic implications. European Journal of Heart Failure, 22(12), 2205–2215. https://doi.org/10.1002/ejhf.199010.1002/ejhf.1990 ↗
31. Singh, M. S., Thongam, K., Choudhary, P., & Bhagat, P. (2024). An integrated machine learning approach for congestive heart failure prediction. Diagnostics, 14(7), 736. https://doi.org/10.3390/diagnostics1407073610.3390/diagnostics14070736 ↗
32. Raza, A., Mehmood, A., Ullah, S., Ahmad, M., Choi, G. S., & On, B. W. (2019). Heartbeat sound signal classification using deep learning. Sensors (Basel, Switzerland), 19(21), 4819. https://doi.org/10.3390/s1921481910.3390/s19214819 ↗
33. Potes, C., Parvaneh, S., Rahman, A., & Conroy, B. (2016). Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. Computing in cardiology Conference, Vancouver, BC, Canada, pp. 621–624. https://doi.org/10.22489/CinC.2016.182-39910.22489/CinC.2016.182-399 ↗
34. Zabihi, M., Rad, A. B., Kiranyaz, S., Gabbouj, M., & Katsaggelos, A. K. (2016). Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. Computing in cardiology Conference, pp. 613–616. https://doi.org/10.22489/CinC.2016.180-21310.22489/CinC.2016.180-213 ↗
35. Rubin, J., Abreu, R., Ganguli, A., Nelaturi, S., Matei, I., & Sricharan, K. (2017). Recognizing abnormal heart sounds using deep learning. ArXiv Preprint arXiv. https://doi.org/10.48550/arXiv.1707.04642.:1707.04642.10.48550/arXiv.1707.04642 ↗
36. Krishnan, P. T., Balasubramanian, P., & Umapathy, S. (2020). Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network. Physical and Engineering Sciences in Medicine, 43(2), 505–515. https://doi.org/10.1007/s13246-020-00851-w10.1007/s13246-020-00851-w ↗
37. Noman, F., Salleh, S. H., Ting, C. M., Samdin, S. B., Ombao, H., & Hussain, H. (2019). A Markov-switching model approach to heart sound segmentation and classification. IEEE Journal of Biomedical and Health Informatics, 24(3), 705–716. https://doi.org/10.1109/JBHI.2019.292503610.1109/JBHI.2019.2925036 ↗
38. Zhang, W., Han, J., & Deng, S. (2019). Abnormal heart sound detection using Temporal quasi-periodic features and long short-term memory without segmentation. Biomedical Signal Processing and Control, 53, 101560. https://doi.org/10.1016/j.bspc.2019.10156010.1016/j.bspc.2019.101560 ↗
39. Xiao, B., Xu, Y., Bi, X., Zhang, J., & Ma, X. (2020). Heart sounds classification using a novel 1d convolutional neural network with extremely low parameter consumption. Neurocomputing, 392, 153–159. https://doi.org/10.1016/j.neucom.2018.09.10110.1016/j.neucom.2018.09.101 ↗
40. Alkhodari, M., & Fraiwan, L. (2021). Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings. Computer Methods and Programs in Biomedicine, 200, 105940. https://doi.org/10.1016/j.cmpb.2021.10594010.1016/j.cmpb.2021.105940 ↗
41. Zheng, Y., Guo, X., Wang, Y., Qin, J., & Lv, F. (2022). A multi-scale and multi-domain heart sound feature-based machine learning model for Acc/aha heart failure stage classification. Physiological Measurement, 43(6), 065002. https://doi.org/10.1088/1361-6579/ac6d4010.1088/1361-6579/ac6d40 ↗