ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture

• Autorzy: Kusuma S. , Jothi K. R.

Identyfikatory

DOI

10.1016/j.bbe.2022.02.003

• Języki publikacji: EN

Abstrakty

EN

In humans, Congestive Heart Failure (CHF) refers to the chronic progressive condition that drastically influences the pumping potentiality of the heart muscle. This CHF has the possibility of increasing health expenditure, morbidity, mortality and minimized quality of life. In this context, Electrocardiogram (ECG) is considered as the simplest and a non-invasive diagnosis method that aids in detecting and demonstrating the realizable changes in CHF. However, diagnosing CHF based on manual exploration of ECG signals is frequently impacted by errors as duration and small amplitude of the signals either investigated separately or in the integration is determined to neither specific nor sensitive. At this juncture, the reliability and diagnostic objectivity of ECG signals during the CHF detection process may be enhanced through the inclusion of automated computer-aided system. In this paper, Deep CNN and LSTM Architecture (DCNN-LSTM)-based automated diagnosis system is proposed for detecting CHF using ECG signals. In specific, CNN is included for the purpose of extracting deep features and LSTM is used for attaining the objective of CHF detection using the extracted features. This proposed DCNN-LSTM is evolved with minimal pre-processing of ECG signals and does not involve any classification process or manual engineered features during diagnosis. The experimentation of the proposed DCNN-LSTM conducted using the real time ECG signals datasets confirmed an accuracy of 99.52, sensitivity of 99.31%, specificity of 99.28%, F-Score of 98.94% and AUC of 99.9%, respectively.

Słowa kluczowe

EN

ECG; electrocardiogram; CNN; LSTM; CHF; congestive heart failure

PL

EKG; elektrokardiogram; CNN; LSTM; CHF; zastoinowa niewydolność serca

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 247--257
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Kusuma S.

• School of Computer Science and Engineering, VIT, Vellore, India

autor

Jothi K. R.

• School of Computer Science and Engineering, VIT, Vellore, India

Bibliografia

• [1] Jahmunah V, Oh SL, Wei JKE, Ciaccio EJ, Chua K, San TR, et al. Computer-aided diagnosis of congestive heart failure using ECG signals–a review. Physica Med 2019;62:95–104. https:// doi.org/10.1016/j.ejmp.2019.05.004.
• [2] Ning W, Li S, Wei D, Guo LZ, Chen H. Automatic detection of congestive heart failure based on a hybrid deep learning algorithm in the internet of medical things. IEEE Internet Things J 2021;15(8):12550–8. https://doi.org/10.1109/ JIOT.2020.3023105.
• [3] Li J, Si Y, Xu T, Jiang S. Deep convolutional neural network based ECG classification system using information fusion and one-hot encoding techniques. Math Probl Eng 2018:1–10. .https://doi.org/10.1155/2018/7354081
• [4] Huang SH, Chuang BL, Lin YH, Hung CS, Ma HP. A congestive heart failure detection system via multi-input deep learning networks. In IEEE Global Communications Conference (GLOBECOM) 2019 Dec 9 (pp. 1-6). IEEE. 10.1109/ GLOBECOM38437.2019.9013460.
• [5] Acharya UR, Fujita H, Sudarshan VK, Oh SL, Adam M, Tan JH, et al. Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal. Knowl-Based Syst 2017;132:156–66. https://doi.org/ 10.1016/j.knosys.2017.06.026.
• [6] Li S. Multifractal Detrended fluctuation analysis of congestive heart failure disease based on constructed heartbeat sequence. IEEE Access 2020;10(8):205244–9. https://doi.org/ 10.1109/ACCESS.2020.3037080.
• [7] Wendt H, Abry P, Kiyono K, Hayano J, Watanabe E, Yamamoto Y. Wavelet $ p $-leader non Gaussian multiscale expansions for heart rate variability analysis in congestive heart failure patients. IEEE Trans Biomed Eng 2019;66(1):80–8. https://doi.org/10.1109/TBME.2018.2825500.
• [8] Alarsan FI, Younes M. Analysis and classification of heart diseases using heartbeat features and machine learning algorithms. J Big Data 2019;6(1):1–5. https://doi.org/10.1186/ s40537-019-0244-x.
• [9] Tripathy RK, Paternina MRA, Arrieta JG, Zamora-Méndez A, Naik GR. Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. Comput Methods Programs Biomed 2019;173:53–65. https://doi.org/10.1016/j.Cmpb.2019.03.008.
• [10] Wang CC, Chang CD. SVD and SVM based approach for congestive heart failure detection from ECG signal. In The 40th International Conference on Computers & Industrial Engineering 2010 Jul 25 (pp. 1-5). IEEE. 10.1109/ICCIE.2010.5668319.
• [11] Kwon J-M, Kim K-H, Jeon K-H, Kim HM, Kim MJ, Lim S-M, et al. Development and validation of deep-learning algorithm for electrocardiography-based heart failure identification. Korean Circ J 2019;49(7):629. https://doi.org/10.4070/kcj.2018.0446.
• [12] Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR. A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Methods Programs Biomed 2019;176:121–33. https://doi.org/10.1016/j.cmpb.2019.05.004.
• [13] Pandey SK, Janghel RR. Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE. Australas Phys Eng Sci Med 2019;42(4):1129–39. https://doi.org/10.1007/s13246-019-00815-9.
• [14] Lee MY, Yu SN. Multiscale sample entropy based on discrete wavelet transform for clinical heart rate variability recognition. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2012 Jan 1 (pp. 4299-4302). IEEE. 10.1109/EMBC.2012.6346917.
• [15] Kumar A, Komaragiri R, Kumar M. Heart rate monitoring and therapeutic devices: a wavelet transform based approach for the modeling and classification of congestive heart failure. ISA Trans 2018;79:239–50. https://doi.org/10.1016/j. Isatra.2018.05.003.
• [16] Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M, et al. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Appl Intell 2019;49(1):16–27. https://doi.org/10.1007/s10489-018- 1179-1.
• [17] Li Y, Zhang Y, Zhao L, Zhang Y, Liu C, Zhang L, et al. Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure. IEEE Access 2018;6:39734–44. https://doi.org/10.1109/ ACCESS.2018.2855420.
• [18] Hasan NI, Bhattacharjee A. Deep learning approach to cardiovascular disease classification employing modified ECG signal from empirical mode decomposition. Biomed Signal Process Control 2019;52:128–40. https://doi.org/10.1016/j. Bspc.2019.04.005.
• [19] Wang L, Zhou W, Chang Q, Chen J, Zhou X. Deep ensemble detection of congestive heart failure using short-term RR intervals. IEEE Access 2019;7:69559–74. https://doi.org/ 10.1109/ACCESS.2019.2912226.
• [20] Fujita H, Cimr D. Computer aided detection for fibrillations and flutters using deep convolutional neural network. Inf Sci 2019;486:231–9. https://doi.org/10.1016/j.ins.2019.02.065.
• [21] Fujita H, Cimr D. Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. Appl Intell 2019;49(9):3383–91. https:// doi.org/10.1007/s10489-019-01461-0.
• [22] Zhang Y, Xia M. Application of deep neural network for congestive heart failure detection using ECG signals. J Phys: Conf Ser 2020;1642(1):012021. https://doi.org/10.1088/1742- 6596/1642/1/012021.
• [23] Zhang X, Gu K, Miao S, Zhang X, Yin Y, Wan C, et al. Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system. Cardiovasc Diagn Ther 2020;10(2):227–35.
• [24] Ribeiro AH, Ribeiro MH, Paixão GM, Oliveira DM, Gomes PR, Canazart JA, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 2020;11(1):1–9. https://doi.org/10.1038/s41467-020-15432-4.
• [25] Eltrass AS, Tayel MB, Ammar AI. A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomed Signal Process Control 2021;65. https://doi.org/10.1016/j.bspc.2020.102326 102326.
• [26] Hernandez-Matamoros A, Fujita H, Perez-Meana H. A novel approach to create synthetic biomedical signals using BiRNN. Inf Sci 2020;541:218–41. https://doi.org/10.1016/j.ins.2020.06.019.
• [27] Hernandez-Matamoros A, Fujita H, Escamilla-Hernandez E, Perez-Meana H, Nakano-Miyatake M. Recognition of ECG signals using wavelet based on atomic functions. Biocybern Biomed Eng 2020;40(2):803–14. https://doi.org/10.1016/j. Bbe.2020.02.007.
• [28] Li D, Tao Y, Zhao J, Wu H. Classification of congestive heart failure from ECG segments with a multi-scale residual network. Symmetry 2020;12(12):2019. https://doi.org/10.3390/ sym12122019.
• [29] Çınar A, Tuncer SA. Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks. Comput Methods Biomech Biomed Eng 2021;24(2):203–14. .https://doi.org/10.1080/10255842.2020.1821192.
• [30] Baccouche A, Garcia-Zapirain B, Castillo Olea C, Elmaghraby A. Ensemble deep learning models for heart disease classification: A case study from Mexico. Information 2020;11 (4):207. https://doi.org/10.3390/info11040207.