Statistics Physics Based Interpretation of the Classification Reliability of Convolutional Neural Ne

This paper presents the results of morphological heart arrhythmia detection based on parameters which are obtained from modeling of the cumulants of the electrocardiography, ECG signals. Cumulants possess many properties that make them effective tools to describe morphological variations of non-stationary signals. Among these properties, the two most attractive founded for analysis of ECG arrhythmia detections are the ability of suppressing morphological variations of different beats of ECG signals belonging to a specific class of heart arrhythmia and reducing the effect of Gaussian noise on the classification significantly. The proposed method combines these properties in conjunction with Hermitian model to perform an efficient classification method for five different heart arrhythmias. We achieved the sensitivity of 98.59% and specificity of 99.67% which are comparable to previous works. This novel combination has made the classification method much more accurate in discriminating different morphological based heart arrhythmias as well as making a good degree of robustness to remove additive Gaussian noises from ECG signals.