Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural...

Area of Science:

• Cardiology and Medical Informatics
• Computational Biology and Machine Learning

Background:

• Sudden cardiac death (SCD) poses a significant global health challenge, necessitating improved methods for early risk identification.
• Electrocardiogram (ECG) analysis, particularly heart rate variability (HRV), offers potential for detecting preclinical indicators of cardiac events.

Purpose of the Study:

• To investigate the efficacy of advanced heart rate variability (HRV) analysis using Convolutional Neural Networks (CNNs) for early detection of sudden cardiac death (SCD) risk factors.
• To compare the predictive performance of HRV features combined with linear analysis and deep learning (DL) across various cardiac conditions.

Main Methods:

• Acquisition of 30-minute ECG signals from five distinct groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD.
• Segmentation of ECG data into 5-minute intervals for comprehensive HRV feature extraction.
• Application and optimization of a Convolutional Neural Network (CNN) model, including hyperparameter tuning (layers, learning rate, batch size), for HRV signal analysis.

Main Results:

• The integrated approach utilizing HRV, linear features, and a deep learning (DL) method demonstrated high predictive performance.
• Achieved an average accuracy of 99.30%, with a sensitivity of 97%, specificity of 99.60%, and precision of 97.87% in identifying SCD risk factors.
• CNN model optimization significantly enhanced the prediction accuracy for cardiac conditions.

Conclusions:

• The combination of HRV analysis, linear features, and DL, particularly CNNs, provides a highly accurate method for early SCD risk factor detection.
• This study highlights the potential of advanced computational methods in improving cardiovascular risk stratification and potentially reducing SCD mortality.
• Further research into refining DL techniques for HRV analysis is recommended to enhance the prediction of sudden cardiac death.