Affiliations: [a] Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thucalay, Tamilnadu, India | [b] Electronics and Instrumentation Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thucalay, Tamilnadu, India
Correspondence:
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Corresponding author: Haseena Beegum S, Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thucalay, Kanyakumari Distrct, Tamilnadu, India. E-mail: haseenasaujath@gmail.com.
Abstract: Electrocardiogram (ECG) signal plays an important role in monitoring and diagnosing patients who suffer from several cardiovascular diseases. Numerous conventional techniques designed for cardiovascular disease classification face challenges regarding classification accuracy and also, find difficulty in automatic monitoring and classification techniques. Therefore, this work aspires to design a robust approach, which can precisely classify the ECG even in the presence of noise. Following that, this research introduces the heartbeat classification scheme by utilizing the optimization-based deep learning scheme. Here, the optimization algorithm, called the Serial Exponential Hunger Games Search Algorithm (SExpHGS) is newly designed by integrating the serial exponential weighted moving average concept in the Hunger Games Search (HGS) approach to train deep learning scheme. Initially, the pre-processing is performed by utilizing a median filter and subsequently, wave components are detected by utilizing the resolution wavelet-based scheme. Ultimately, SExpHGS-based Deep Belief Network (SExpHGS-based DBN) recognizes the ECG conditions of individuals. Here, the techniques are analyzed by utilizing the ECG Lead 2 Dataset PhysioNet dataset and analysis is carried out based on performance parameters, namely accuracy, specificity, and sensitivity. The attained values of the aforementioned metrics are 0.954, 0.965, and 0.938, correspondingly.
Keywords: Cardiovascular disease, ECG signal, pre-processing, deep learning, optimization algorithm
