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

Jahmunah, 2019, Computer-aided diagnosis of congestive heart failure using ECG signals–a review, Physica Med, 62, 95, 10.1016/j.ejmp.2019.05.004 Ning, 2021, Automatic detection of congestive heart failure based on a hybrid deep learning algorithm in the internet of medical things, IEEE Internet Things J, 15, 12550, 10.1109/JIOT.2020.3023105 Li, 2018, Deep convolutional neural network based ECG classification system using information fusion and one-hot encoding techniques, Math Probl Eng, 1 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. Acharya, 2017, Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal, Knowl-Based Syst, 132, 156, 10.1016/j.knosys.2017.06.026 Li, 2020, Multifractal Detrended fluctuation analysis of congestive heart failure disease based on constructed heartbeat sequence, IEEE Access, 10, 205244, 10.1109/ACCESS.2020.3037080 Wendt, 2019, Wavelet $ p $-leader non Gaussian multiscale expansions for heart rate variability analysis in congestive heart failure patients, IEEE Trans Biomed Eng, 66, 80, 10.1109/TBME.2018.2825500 Alarsan, 2019, Analysis and classification of heart diseases using heartbeat features and machine learning algorithms, J Big Data, 6, 1, 10.1186/s40537-019-0244-x Tripathy, 2019, Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme, Comput Methods Programs Biomed, 173, 53, 10.1016/j.cmpb.2019.03.008 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. Kwon, 2019, Development and validation of deep-learning algorithm for electrocardiography-based heart failure identification, Korean Circ J, 49, 629, 10.4070/kcj.2018.0446 Yildirim, 2019, A new approach for arrhythmia classification using deep coded features and LSTM networks, Comput Methods Programs Biomed, 176, 121, 10.1016/j.cmpb.2019.05.004 Pandey, 2019, Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE, Australas Phys Eng Sci Med, 42, 1129, 10.1007/s13246-019-00815-9 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. Kumar, 2018, Heart rate monitoring and therapeutic devices: a wavelet transform based approach for the modeling and classification of congestive heart failure, ISA Trans, 79, 239, 10.1016/j.isatra.2018.05.003 Acharya, 2019, Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals, Appl Intell, 49, 16, 10.1007/s10489-018-1179-1 Li, 2018, Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure, IEEE Access, 6, 39734, 10.1109/ACCESS.2018.2855420 Hasan, 2019, Deep learning approach to cardiovascular disease classification employing modified ECG signal from empirical mode decomposition, Biomed Signal Process Control, 52, 128, 10.1016/j.bspc.2019.04.005 Wang, 2019, Deep ensemble detection of congestive heart failure using short-term RR intervals, IEEE Access, 7, 69559, 10.1109/ACCESS.2019.2912226 Fujita, 2019, Computer aided detection for fibrillations and flutters using deep convolutional neural network, Inf Sci, 486, 231, 10.1016/j.ins.2019.02.065 Fujita, 2019, Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing, Appl Intell, 49, 3383, 10.1007/s10489-019-01461-0 Zhang, 2020, Application of deep neural network for congestive heart failure detection using ECG signals, J Phys: Conf Ser, 1642, 012021 Zhang, 2020, Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system, Cardiovasc Diagn Ther, 10, 227, 10.21037/cdt.2019.12.10 Ribeiro, 2020, Automatic diagnosis of the 12-lead ECG using a deep neural network, Nat Commun, 11, 1 Eltrass, 2021, 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, 65, 10.1016/j.bspc.2020.102326 Hernandez-Matamoros, 2020, A novel approach to create synthetic biomedical signals using BiRNN, Inf Sci, 541, 218, 10.1016/j.ins.2020.06.019 Hernandez-Matamoros, 2020, Recognition of ECG signals using wavelet based on atomic functions, Biocybern Biomed Eng, 40, 803, 10.1016/j.bbe.2020.02.007 Li, 2020, Classification of congestive heart failure from ECG segments with a multi-scale residual network, Symmetry, 12, 2019, 10.3390/sym12122019 Çınar, 2021, 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, 24, 203, 10.1080/10255842.2020.1821192 Baccouche, 2020, Ensemble deep learning models for heart disease classification: A case study from Mexico, Information, 11, 207, 10.3390/info11040207