Prediction of Cardiovascular Diseases by Integrating Electrocardiogram (ECG) and Phonocardiogram (PCG) Multi-Modal Features using Hidden Semi Morkov Model

Main Article Content

Prasadgouda B. Patil
Vijay Bhaskar Reddy
Ashokumar P. S.


Because the health care field generates a large amount of data, we must employ modern ways to handle this data in order to give effective outcomes and make successful decisions based on data. Heart diseases are the major cause of mortality worldwide, accounting for 1/3th of all fatalities. Cardiovascular disease detection can be accomplished by the detection of disturbance in cardiac signals, one of which is known as phonocardiography. The aim of this project is for using machine learning to categorize cardiac illness based on electrocardiogram (ECG) and phonocardiogram (PCG) readings. The investigation began with signal preprocessing, which included cutting and normalizing the signal, and was accompanied by a continuous wavelet transformation utilizing a mother wavelet analytic morlet. The results of the decomposition are shown using a scalogram, and the outcomes are predicted using the Hidden semi morkov model (HSMM). In the first phase, we submit the dataset file and choose an algorithm to run on the chosen dataset. The accuracy of each selected method is then predicted, along with a graph, and a modal is built for the one with the max frequency by training the dataset to it. In the following step, input for each cardiac parameter is provided, and the sick stage of the heart is predicted based on the modal created. We then take measures based on the patient's condition. When compared to current approaches, the suggested HSMM has 0.952 sensitivity, 0.92 specificity, 0.94 F-score, 0.91 ACC, and 0.96 AUC.

Article Details

How to Cite
Patil, P. B. ., Reddy, V. B. ., & P. S., A. . (2022). Prediction of Cardiovascular Diseases by Integrating Electrocardiogram (ECG) and Phonocardiogram (PCG) Multi-Modal Features using Hidden Semi Morkov Model. International Journal on Recent and Innovation Trends in Computing and Communication, 10(10), 32–44.


Li M et al (2021) Piwi-interacting rnas (pirnas) as potential biomarkers and therapeutic targets for cardiovascular diseases. Angiogenesis 24(1):19–34

Bui AL, Horwich TB, Fonarow GC (2011) Epidemiology and risk profile of heart failure. Nat Rev Cardiol 8(1):30–41

Hassanin A, Hassanein M, Bendary A, Maksoud MA (2020) Demographics, clinical characteristics, and outcomes among hospitalized heart failure patients across different regions of egypt. Egypt Heart J 72(1):1–9

Allen LA et al (2012) Decision making in advanced heart failure: a scientific statement from the american heart association. Circulation 125(15):1928–1952

Yusuf S, Reddy S, Ounpuu S, Anand S (2001) Global burden of cardiovascular diseases: Part ii: variations in cardiovascular disease by specific ethnic groups and geographic regions and prevention strategies. Circulation 104(23):2855–2864

Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36(4):7675–7680

Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Comput Methods Programs Biomed 141:19–26

Balaha HM, Ali HA, Saraya M, Badawy M (2021) A new arabic handwritten character recognition deep learning system (ahcrdls). Neural Comput Appl 33(11):6325–6367

Namejs Zeltins. (2022). Implementation of Shutdown Mode for the Boosting and Inverting Buck-Boost Converter. Acta Energetica, (03), 15–21. Retrieved from

Chen AH, Huang SY, Hong PS, Cheng CH, Lin EJ (2011) Hdps: heart disease prediction system, In: 2011 computing in cardiology. IEEE, pp 557–560

Olmez, T. and Dokur, Z. (2003). Classification of heart ¨ sounds using an artificial neural network. Pattern Recognition Letters, 24(1-3):617–629.

Wang, X., Li, Y., Sun, C., and Liu, C. (2009). Detection of the first and second heart sound using heart sound energy. In 2009 2nd International Conference on Biomedical Engineering and Informatics, pages 1– 4. IEEE.

Zhong, L., Guo, X., Ji, A., and Ding, X. (2011). A robust envelope extraction algorithm for cardiac sound signal segmentation. In 2011 5th International Conference on Bioinformatics and Biomedical Engineering, pages 1–5. IEEE.

Kumar, D., Carvalho, P. d., Antunes, M., Henriques, J., Maldonado, M., Schmidt, R., and Habetha, J. (2006). Wavelet transform and simplicity based heart murmur segmentation. In 2006 Computers in Cardiology, pages 173–176. IEEE

Jacek Marecki, & Dr. Sunita Chaudhary. (2022). Electrical Structure for Embedded Commuter Vision for Automobile Sector. Acta Energetica, (02), 44–51. Retrieved from

Zeng, Y., Yang, S., Yu, X., Lin, W., Wang, W., Tong, J., & Xia, S. (2022). A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and electrocardiogram signals synchronous analysis. Mathematical Biosciences and Engineering, 19(9), 9612-9635.

FAISANT, M., FONTECAVE-JALLON, J., GENOUX, B., RIVET, B., DIA, N., RESENDIZ, M., ... & HOFFMANN, P. (2022). Non-invasive fetal monitoring: Fetal Heart Rate multimodal estimation from abdominal electrocardiography and phonocardiography. Journal of Gynecology Obstetrics and Human Reproduction, 102421.

Huang, Y., Li, H., Tao, R., Han, W., Zhang, P., Yu, X., & Wu, R. (2022). A customized framework for coronary artery disease detection using phonocardiogram signals. Biomedical Signal Processing and Control, 78, 103982.

Ranipa, K., Zhu, W. P., & Swamy, M. N. S. (2021, May). Multimodal CNN fusion architecture with multi-features for heart sound classification. In 2021 IEEE International symposium on circuits and systems (ISCAS) (pp. 1-5). IEEE.

Shokouhmand, A., Antoine, C., Young, B. K., & Tavassolian, N. (2021, November). Multi-modal Framework for Fetal Heart Rate Estimation: Fusion of Low-SNR ECG and Inertial Sensors. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 7166-7169). IEEE.

Zhang, H., Wang, X., Liu, C., Liu, Y., Li, P., Yao, L., ... & Jiao, Y. (2020). Detection of coronary artery disease using multi-modal feature fusion and hybrid feature selection. Physiological Measurement, 41(11), 115007.

Tariq, Z., Shah, S. K., & Lee, Y. (2020, December). Automatic multimodal heart disease classification using phonocardiogram signal. In 2020 IEEE International conference on big data (Big Data) (pp. 3514-3521). IEEE.

Raghuram, S., Niyaz, T., Purma, H., Choubey, S. B., & Sreenivasulu, Y. Cardiac Arrhythmia Prediction and Prevention of Heart failure using PCG (PhonoCardioGram) and CNN.

Liu, G., Xu, J., Wang, C., Yu, M., Yuan, J., Tian, F., & Zhang, G. A Machine Learning Method for Predicting the Probability of Mods Using Only Non-Invasive Parameters. Available at SSRN 4129902.

Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2022). RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance. Scientific Reports, 12(1), 1-12.

Amal, S., Safarnejad, L., Omiye, J. A., Ghanzouri, I., Cabot, J. H., & Ross, E. G. (2022). Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care. Frontiers in Cardiovascular Medicine, 9.

Antony Kumar, K., & Carmel Mary Belinda, M. J. (2022). A Multi-Layer Acoustic Neural Network-Based Intelligent Early Diagnosis System for Rheumatic Heart Disease. International Journal of Image and Graphics, 2450012.

Li, P., Hu, Y., & Liu, Z. P. (2021). Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods. Biomedical Signal Processing and Control, 66, 102474.

C. Liu, D. Springer, Q. Li, B. Moody, R.A. Juan, F.J. Chorro, F. Castells, J.M. Roig, I. Silva, A.E.W. Johnson, Z. Syed, S.E. Schmidt, C.D. Papadaniil, L. Hadjileontiadis, H. Naseri, A. Moukadem, A. Dieterlen, C. Brandt, H. Tang, M. Samieinasab, M. R. Samieinasab, R. Sameni, R.G. Mark, G.D. Clifford, An open access database for the evaluation of heart sound algorithms, Physiol. Meas. 37 (2016) 2181–2213,

Syed Hassan, MIT Automated Auscultation System, 2005.

Z. Syed, D. Leeds, D. Curtis, F. Nesta, R.A. Levine, J. Guttag, A framework for the analysis of acoustical cardiac signals, IEEE Trans. Biomed. Eng. 54 (2007) 651–662,