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Published on: December 11, 2019
Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model
Hamed Alqahtani1, Ghadah Aldehim2, Nuha Alruwais3
1Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia.
Insights
This study presents an automated deep learning technique for ECG signal recognition to detect cardiovascular diseases. The ADL-ECGSR method achieved 91.24% accuracy, improving upon existing approaches for arrhythmia detection.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Cardiology
Background:
- Electrocardiography (ECG) is a crucial non-invasive tool for diagnosing cardiovascular diseases (CVDs).
- Accurate and rapid detection of arrhythmias like atrial fibrillation and ventricular tachycardia is vital for patient outcomes.
- Challenges in ECG analysis include high waveform variability and noise, impacting automated recognition model performance.
Purpose of the Study:
- To introduce an automated deep learning enabled ECG signal recognition (ADL-ECGSR) technique for CVD detection and classification.
- To enhance the accuracy and efficiency of automated ECG analysis in clinical decision-making systems.
- To address the challenges of waveform variability and noise in ECG signal processing.
Main Methods:
- The ADL-ECGSR technique integrates pre-processing, feature extraction, parameter tuning, and classification.
- A bidirectional long short-term memory (BiLSTM) network serves as the feature extractor, optimized using the Adamax optimizer.
- The dragonfly algorithm (DFA) combined with a stacked sparse autoencoder (SSAE) module is employed for signal recognition and classification.
Main Results:
- The ADL-ECGSR technique demonstrated a remarkable performance of 91.24% accuracy on the PTB-XL benchmark dataset.
- Comparative analysis confirmed the enhanced ECG recognition efficiency of the proposed methodology over existing methods.
- The study validates the effectiveness of DL models in improving cardiovascular disease detection from ECG signals.
Conclusions:
- The developed ADL-ECGSR technique offers a robust and accurate solution for automated ECG signal recognition and CVD classification.
- The integration of BiLSTM, Adamax, DFA, and SSAE shows significant potential for advancing automated cardiac diagnostics.
- This deep learning approach holds promise for improving the reliability of healthcare decision-making systems in cardiology.
Abstract:
Electrocardiography (ECG) is the most non-invasive diagnostic tool for cardiovascular diseases (CVDs). Automatic analysis of ECG signals assists in accurately and rapidly detecting life-threatening arrhythmias like atrioventricular blockage, atrial fibrillation, ventricular tachycardia, etc. The ECG recognition models need to utilize algorithms to detect various kinds of waveforms in the ECG and identify complicated relationships over time. However, the high variability of wave morphology among patients and noise are challenging issues. Physicians frequently utilize automated ECG abnormality recognition models to classify long-term ECG signals. Recently, deep learning (DL) models can be used to achieve enhanced ECG recognition accuracy in the healthcare decision making system. In this aspect, this study introduces an automated DL enabled ECG signal recognition (ADL-ECGSR) technique for CVD detection and classification. The ADL-ECGSR technique employs three most important subprocesses: pre-processed, feature extraction, parameter tuning, and classification. Besides, the ADL-ECGSR technique involves the design of a bidirectional long short-term memory (BiLSTM) based feature extractor, and the Adamax optimizer is utilized to optimize the trained method of the BiLSTM model. Finally, the dragonfly algorithm (DFA) with a stacked sparse autoencoder (SSAE) module is applied to recognize and classify EEG signals. An extensive range of simulations occur on benchmark PTB-XL datasets to validate the enhanced ECG recognition efficiency. The comparative analysis of the ADL-ECGSR methodology showed a remarkable performance of 91.24 % on the existing methods.
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