A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition. (July 2021)

• Record Type: Journal Article
• Authors: S., Clement Virgeniya; E., Ramaraj
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Cardiovascular disease -- Electrocardiogram -- Deep learning -- Class imbalance -- ADASYN; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102779
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A Comprehensive CIGRU-ELM model for ECG signal processing, data sampling, feature extraction and classification. Robust ECG recognition and classification tool for cardiovascular disease diagnosis. An extensive experimental analysis is performed and overcomes the problem of data unavailability. Superiority of the proposed model is highlighted in terms of accuracy, sensitivity, specificity, kappa, and Hamming loss. Abstract: Cardiovascular disease (CVD) becomes a significant risk to human lives. Electrocardiogram (ECG) is a commonly employed non-invasive physiological signal used to screen and diagnose CVD. During recent years, remarkable advances in automated ECG interpretation techniques have been witnessed. In particular, deep learning (DL) models find useful to increase the disease diagnostic performance of CVD using the ECG signals. To explore and achieve effective ECG recognition, this paper presents a Class Imbalance handing with DL based Gated Recurrent Unit (GRU) and Extreme Learning Machine (ELM) for ECG Signal Recognition, named as CIGRU-ELM model. The presented CIGRU-ELM model involves preprocessing, data sampling, feature extraction, and classification. Primarily, data preprocessing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, Adaptive Synthetic (ADASYN) based data sampling process takes place to handle the class imbalance problem. Besides, GRU based feature extractor is applied to extract a useful set of feature vectors. Finally, ELM model based classification model is employed to determine the appropriate class label of the test ECG signal. An extensive experimental analysis is performed on the benchmark PTB-XL dataset. A detailed comparative results analysis of the proposed CIGRU-ELM model takes place to highlight the superiority of the proposed model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.