Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network. (November 2020) - Atal, Dinesh Kumar; Singh, Mukhtiar
Highlights: The accurate classification is done using the deep CNN (Convolutional Neural Network), which is optimally tuned using the proposed BaROA ( Bat-Rider Optimization Algorithm ). Initially, the gabor, wave, and interval features are derived from the ECG signal and the feature vector is established. The feature vector is fed to the arrhythmia classification module, in which the extracted features are employed for deciding the patient to be with arrhythmia or no arrhythmia. The methods are analyzed using the MIT-BIH Arrhythmia Database and the analysis is performed based on the evaluation parameters, like accuracy, specificity, and sensitivity, which are found to be 93.19 %, 95 %, and 93.98 %, respectively. Abstract: Arrhythmia classification is the need of the hour as the world is reporting a higher death troll as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia classification face a hectic challenge of classification accuracy and they raised the challenge of automatic monitoring and classification methods. Accordingly, the paper proposes the automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (deep CNN). The optimization algorithm named, Bat-Rider optimization algorithm (BaROA) is newly developed using the multi-objective bat algorithm (MOBA) and Rider Optimization Algorithm (ROA).At first, the wave and gabor features are extracted from the ECG signals in such a way that these features represent the individual ECG features. Finally, the signals are provided to the BaROA-based DCNN classifier that identifies conditions of the individual as arrhythmia and no-arrhythmia from the ECG signals. The methods are analyzed using the MIT-BIH Arrhythmia Database and the analysis is performed based on the evaluation parameters, like accuracy, specificity, and sensitivity, which are found to be 93.19 %, 95 %, and 93.98 %, respectively.