Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network

Abstract

Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose a method to fuse the time and frequency domain information in ECG signals by convolutional neural network (CNN). First, we adapt multi-scale wavelet decomposition to filter the ECG signal; Then, R-wave localization is used to segment each individual heartbeat cycle; And then, the frequency domain information of this heartbeat cycle is extracted via fast Fourier transform. Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. The experimental results show that the proposed method has the highest recognition accuracy (99.43%) of ECG singles compared with state-of-the-art methods.

Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework

“…Fast Fourier transform (FFT), a discrete Fourier transform algorithm, solves a wide range of problems, including data filtering, digital signal processing, and partial differential equations. It has been used in many applications such as speech enhancement [31], radar signal processing [32], and ECG classification [12,33]. In this study, fast Fourier transform (FFT), will be used to extract all the frequency components that are contributing to the heartbeat signal including linear and nonlinear components.…”

“…This research focuses on developing an automated deep learning model that can classify various classes using a convolutional neural network (CNN) and long short-term memory (LSTM). It has been proven that CNNs can be used for complex applications such as ECG classification [12,33,34]. On the other hand, LSTM is an advanced model that was developed from the recurrent neural networks (RNN) by Hochreiter and Schmidhuber [35].…”

Single and Multi-Lead ECG Heartbeat Classification Using XGBoost on Pre-trained CNN Feature Layer

“…Kumari and Rao's [14] work on ECG beat classification using a hybrid classifier based on adaptive wavelet and pattern recognition techniques offered insights into the effectiveness of combining traditional signal processing with modern classification methods. Aziz et al [15] and Bocheng et al [16] both provided evidence of the efficacy of machine learning algorithms in heartbeat classification, with the latter introducing a novel approach based on time-frequency domain fusion and CNNs. Sadad et al [17] proposed an efficient classification method using a lightweight CNN with an attention module, optimized for IoT devices, indicating the potential for real-time, remote cardiac monitoring.…”

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.…”