Classification of myocardial infarction with multi-lead ECG signals and deep CNN

Fujita, 2017, Characterization of cardiovascular diseases using wavelet packet decomposition and nonlinear measures of electrocardiogram signal, 259

Kenttä, 2016, Prediction of sudden cardiac death with automated high-throughput analysis of heterogeneity in standard resting 12-lead electrocardiograms, Heart Rhythm, 13, 713, 10.1016/j.hrthm.2015.11.035

Acharya, 2017, Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal, Knowledge-Based Syst., 132, 156, 10.1016/j.knosys.2017.06.026

Tripathy, 2016, Detection of cardiac abnormalities from multilead ECG using multiscale phase alternation features, J. Med. Syst., 40, 143, 10.1007/s10916-016-0505-6

Sharma, 2018, A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank, Comput. Biol. Med., 102, 341, 10.1016/j.compbiomed.2018.07.005

Lu, 2000, An automated ECG classification system based on a neuro-fuzzy system, 387

Sun, 2012, ECG analysis using multiple instance learning for myocardial infarction detection, IEEE Trans. Biomed. Eng., 59, 3348, 10.1109/TBME.2012.2213597

Acharya, 2016, Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads, Knowledge-Based Syst., 99, 146, 10.1016/j.knosys.2016.01.040

Kumar, 2017, Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework, Entropy, 19, 488, 10.3390/e19090488

Sharma, 2018, Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal, Image Video Process., 12, 199, 10.1007/s11760-017-1146-z

Oh, 2017, Shockable versus nonshockable life-threatening ventricular arrhythmias using DWT and nonlinear features of ECG signals, J. Mech. Med. Biol., 17, 10.1142/S0219519417400048

Weng, 2014, Myocardial infarction classification by morphological feature extraction from big 12-lead ECG data, 689

Kora, 2015, Improved Bat algorithm for the detection of myocardial infarction, SpringerPlus, 4, 666, 10.1186/s40064-015-1379-7

Dohare, 2018, Detection of myocardial infarction in 12 lead ECG using support vector machine, Appl. Soft Comput., 64, 138, 10.1016/j.asoc.2017.12.001

He, 2019, Unsupervised classification of 12-lead ECG signals using wavelet tensor decomposition and two-dimensional Gaussian spectral clustering, Knowledge-Based Syst., 163, 392, 10.1016/j.knosys.2018.09.001

Tripathy, 2014, A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification, Healthcare Technol. Lett., 1, 98, 10.1049/htl.2014.0080

Sopic, 2018, Real-time event-driven classification technique for early detection and prevention of myocardial infarction on wearable systems, IEEE Trans. Biomed. Circuits Syst., 12, 1

Sadhukhan, 2018, Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data, IEEE Trans. Instrum. Meas., 67, 1

Padhy, 2017, Third-order tensor based analysis of multilead ECG for classification of myocardial infarction, Biomed. Signal Process. Control, 31, 71, 10.1016/j.bspc.2016.07.007

Martis, 2009, An integrated ECG feature extraction scheme using PCA and wavelet transform, 1

Pławiak, 2018, Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system, Expert Syst. Appl., 92, 334, 10.1016/j.eswa.2017.09.022

Barstuğan, 2018, The effect of dictionary learning on weight update of adaboost and ECG classification, J. King Saud Univ.-Comp. Inform. Sci.

Tao, 2018, Magnetocardiography based ischemic heart disease detection and localization using machine learning methods, IEEE Trans. Biomed. Eng., 10.1109/TBME.2018.2877649

Lodhi, 2018, A novel approach using voting from ecg leads to detect myocardial infarction, 337

Lui, 2018, Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices, Informat. Med. Unlock., 13, 26, 10.1016/j.imu.2018.08.002

Acharya, 2017, Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Inform. Sci., 415, 190, 10.1016/j.ins.2017.06.027

Faust, 2018, Deep learning for healthcare applications based on physiological signals: a review, Comput. Methods Programs Biomed., 161, 1, 10.1016/j.cmpb.2018.04.005

Yıldırım, 2018, Arrhythmia detection using deep convolutional neural network with long duration ECG signals, Comput. Biol. Med., 102, 411, 10.1016/j.compbiomed.2018.09.009

Assodiky, 2018, Arrhythmia classification using long short-term memory with adaptive learning rate, EMITTER Int. J. Eng. Technol., 6, 75, 10.24003/emitter.v6i1.265

Oh, 2018, Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats, Comput. Biol. Med., 102, 278, 10.1016/j.compbiomed.2018.06.002

Goldberger, 2000, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215

Chollet, F. Keras: dDeep learning library for theano and tensorflow. URL: https://keras. io, 7(8), 2015.

Zhu, 2010, Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations, 19, 67

Yıldırım, 2018, A deep convolutional neural network model for automated identification of abnormal EEG signals, Neur. Comp. Appl., 1

Pławiak, 2018, Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals, Swarm Evolution. Comp., 39, 192, 10.1016/j.swevo.2017.10.002

Pławiak, 2019, Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals, Neur. Comp. Appl., 10.1007/s00521-018-03980-2

Jayachandran, 2010, Analysis of myocardial infarction using discrete wavelet transform, J. Med. Syst., 34, 985, 10.1007/s10916-009-9314-5