Revisiting the Disequilibrium Issues in Tackling Heart Disease Classification Tasks

TL;DR

This paper addresses core issues in heart disease classification by proposing simple methods to handle data imbalance and high dimensionality, significantly improving model accuracy on ECG datasets.

Contribution

It introduces Channel-wise Magnitude Equalizer (CME) and Inverted Weight Logarithmic Loss (IWL), novel techniques to mitigate dataset imbalance and feature redundancy in ECG classification.

Findings

IWL increases SOTA model accuracy by up to 5%.

CME combined with IWL surpasses baseline models by 5-10%.

Proposed methods effectively improve heart disease classification performance.

Abstract

In the field of heart disease classification, two primary obstacles arise. Firstly, existing Electrocardiogram (ECG) datasets consistently demonstrate imbalances and biases across various modalities. Secondly, these time-series data consist of diverse lead signals, causing Convolutional Neural Networks (CNNs) to become overfitting to the one with higher power, hence diminishing the performance of the Deep Learning (DL) process. In addition, when facing an imbalanced dataset, performance from such high-dimensional data may be susceptible to overfitting. Current efforts predominantly focus on enhancing DL models by designing novel architectures, despite these evident challenges, seemingly overlooking the core issues, therefore hindering advancements in heart disease classification. To address these obstacles, our proposed approach introduces two straightforward and direct methods to enhance the classification tasks. To address the high dimensionality issue, we employ a Channel-wise Magnitude Equalizer (CME) on signal-encoded images. This approach reduces redundancy in the feature data range, highlighting changes in the dataset. Simultaneously, to counteract data imbalance, we propose the Inverted Weight Logarithmic Loss (IWL) to alleviate imbalances among the data. When applying IWL loss, the accuracy of state-of-the-art models (SOTA) increases up to 5% in the CPSC2018 dataset. CME in combination with IWL also surpasses the classification results of other baseline models from 5% to 10%.