Related Experiment Video
Updated: Jun 9, 2025
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and
Hardik Telangore1, Victor Azad2, Manish Sharma1
1Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, Gujarat, 380026, India; Centre of Advanced Defence Technology, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, Gujarat, 380026, India.
Insights
This study introduces a deep learning model for early sudden cardiac death (SCD) prediction using electrocardiography (ECG) signals, achieving 98.81% accuracy up to 30 minutes prior to the event. The model offers a significant advancement for monitoring high-risk patients.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Signal Processing
Background:
- Sudden cardiac death (SCD) is a critical health issue, often caused by ventricular fibrillation (VF).
- Current SCD prediction methods offer limited intervention time, typically only minutes before onset.
- Early prediction of SCD is crucial for timely interventions and improving patient outcomes.
Purpose of the Study:
- To develop a deep learning-based model for early prediction of SCD using electrocardiography (ECG) signals.
- To enhance the prediction window for SCD events, allowing for more effective interventions.
- To utilize multimodal ECG signal analysis for improved SCD detection.
Main Methods:
- A multimodal explainable deep learning model was developed to analyze ECG signals at 5- to 30-minute intervals before SCD onset.
- Raw ECG, 2D scalograms (wavelet transform), and 2D Hilbert spectra (HHT) were processed using 1D-CNN, LSTM, Vision Transformer, and 2D-CNN.
- The model integrated feature extraction and temporal pattern recognition for comprehensive ECG analysis.
Main Results:
- The model achieved high performance with 98.81% accuracy, precision, recall, and F1-score for predicting SCD onset 30 minutes in advance.
- The model demonstrated 100% accuracy in classifying SCD patients versus normal controls.
- The developed method significantly outperforms existing state-of-the-art approaches in early SCD prediction.
Conclusions:
- The deep learning model effectively captures diverse ECG patterns for discriminating SCD events up to 30 minutes prior.
- This approach significantly improves early SCD prediction capabilities.
- The model serves as a valuable tool for continuous ECG monitoring in high-risk individuals.
Background And Objective:
Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals.
Methods:
A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert-Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used.
Results:
The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods.
Conclusions:
The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.
Related Concept Videos
01:29Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
01:30Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...