An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease

Abstract

BACKGROUND:

Heart disease represents the leading cause of death globally. Timely diagnosis and treatment can prevent cardiovascular issues. An Electrocardiograms (ECG) serves as a diagnostic tool for identifying heart difficulties. Cardiovascular Disease (CVD) often gets identified through ECGs. Deep learning (DL) garners attention in healthcare due to its potential in swiftly diagnosing ECG anomalies, crucial for patient monitoring. Conversely, automatic CVD detection from ECGs poses a challenging task, wherein rule-based diagnostic models usually achieve top-notch performance. These models encounter complications in supervision vast volumes of diverse data, demanding widespread analysis and medical capability to ensure precise CVD diagnosis.

OBJECTIVE:

This study aims to enhance cardiovascular disease diagnosis by combining symptom-based detection and ECG analysis.

METHODS:

To enhance these experiments, we built a novel automated prediction method based on a Feed Forward Neural Network (FFNN) model. The fundamental objective of our method is to develop the accuracy of ECG diagnosis. Our strategy employs chaos theory and destruction analysis to combine optimum deep learning features with a well-organized set of ECG properties. In addition, we use the constant-Q non-stationary Gabor transform (CQNGT) to convert one-dimensional ECG data into a two-dimensional picture. A pre-trained FFNN processes this image. To identify significant features from the FFNN output that correspond with the ECG data, we employ pairwise feature proximity.

RESULTS:

According to experimental findings, the suggested system, FFNN-CQNGT, surpasses other state-of-the-art systems in terms of precision of 94.89%, computational efficiency of 2.114 ms, accuracy of 95.55%, specificity of 93.77%, and sensitivity of 93.99% and MSE 40.32%.

CONCLUSION:

Contributing an automated ECG-based DL system based on FFNN-CQNGT for early-stage cardiovascular disease identification and classification holds great potential for both patient care and public health.