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. 2024 Apr 12;24(8):2484.
doi: 10.3390/s24082484.

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Shoaib Sattar  1 , Rafia Mumtaz  1 , Mamoon Qadir  2 , Sadaf Mumtaz  3 , Muhammad Ajmal Khan  1 , Timo De Waele  4 , Eli De Poorter  4 , Ingrid Moerman  4 , Adnan Shahid  4
Affiliations

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Shoaib Sattar et al. Sensors (Basel). .

Abstract

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Keywords: ECG classification; arrhythmia; deep learning; digitization; self-supervised learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Placement of electrodes on the body for a 12-lead ECG [7].
Figure 2
Figure 2
A 12-lead ECG record of a normal person showing heartbeats from a combination of different electrodes [8].
Figure 3
Figure 3
A normal ECG heartbeat showing different waves and segments [9].
Figure 4
Figure 4
Overall pipeline of the ECG classification workflow.
Figure 5
Figure 5
ECG digitization using ecg_digitize. (a) Select the lead II heartbeat for digitization. (b) Adjust the image if tilted. Set the time and voltage, then process. (c) Export the digitized ECG heartbeat in a CSV file.
Figure 6
Figure 6
Original images (black lines) and the corresponding digitized signals (blue lines).
Figure 7
Figure 7
ECG heartbeat before and after smoothing.
Figure 8
Figure 8
Proposed CNN architecture.
Figure 9
Figure 9
LSTM network.
Figure 10
Figure 10
The self-supervised learning method.
See this image and copyright information in PMC

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MeSH terms