Enhancing ECG Classification Using Systematic Cubic Spline Control Points with Deep Neural Networks

Abstract

The healthcare industry has undergone substantial growth over the past few years. Despite the development of medical technology, the diagnosis of cardiac arrhythmia is still a significant challenge for modern diagnostic methods for correct and efficient classification of electrocardiogram (ECG) signals, especially in cases of class imbalance and the complexity of the arrhythmia. This study proposes a novel deep learning model where the class imbalance problem is addressed using the Synthetic Minority Oversampling Technique (SMOTE) and the effect of signal resolution on the classification is investigated using Cubic Spline (CS) interpolation and Lightweight Deep Neural Network (CSLWDNN). Both the MIT-BIH and the INCART datasets were preprocessed and resampled with CS interpolation with several control point configurations. The Spline-187 configuration performed best on MIT-BIH data, with an F1-score of 97.35% and the accuracy of 97.35%. The model performance for INCART dataset is an F1-score of 98.56% and an accuracy of 98.27% at 20 control points. Comparative experiments showed that CS interpolation has better class-wise recall and smoothness of the signal with various input representations. The outcomes indicate that SMOTE balancing coupled with spline-based preprocessing is a valuable scientific and practical approach to build reliable, scalable, and clinically applicable ECG classification systems.