Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls

Abstract

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.

Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database

“…The recent paradigm shift in ECG data analysis has gravitated towards the application of DL techniques. Unlike traditional methods, DL offers generic, non-domain-specific operation sequences directly applicable to raw input signals, including ECG records [3][4][5][16][17][18][19][20].…”

“…A prominent example of DL architecture is the convolutional neural network (CNN), which demonstrably exhibits efficacy in ECG data analysis [3][4][5][16][17][18][19][20]. The inherent strength of DL models lies in their ability to learn and discover multilevel representations from data.…”

“…Recent research has demonstrated the efficacy of wavelet-based features in ECG classification [7,44,45]. Furthermore, hybrid approaches combining DL with wavelet transforms have emerged to leverage the strengths of both methods [17,19,46].…”

“…In contrast to CML-based approaches, the alternative pipeline leverages DL, which has emerged as the prevailing paradigm for tackling machine vision tasks [54]. CNNs constitute a prevalent DL architecture frequently employed for ECG data analysis, e.g., in [3][4][5]8,[16][17][18][19][20]55,56]. Unlike CML methods that rely on hand-engineered features, CNNs directly process raw ECG data as input.…”