Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning

TL;DR

This paper compares traditional machine learning with deep learning approaches for ECG heartbeat classification, finding that hand-crafted features with models like LightGBM outperform image-based CNN methods in accuracy.

Contribution

It introduces a comprehensive comparison between feature-based machine learning and image-based deep learning methods for ECG classification, highlighting the superior performance of traditional features.

Findings

LightGBM achieved 99% accuracy and 0.94 F1 score.

Image-based CNN methods scored lower, with an F1 of 0.85.

Hand-crafted features better capture ECG variations.

Abstract

This study addresses the classification of heartbeats from ECG signals through two distinct approaches: traditional machine learning utilizing hand-crafted features and deep learning via transformed images of ECG beats. The dataset underwent preprocessing steps, including downsampling, filtering, and normalization, to ensure consistency and relevance for subsequent analysis. In the first approach, features such as heart rate variability (HRV), mean, variance, and RR intervals were extracted to train various classifiers, including SVM, Random Forest, AdaBoost, LSTM, Bi-directional LSTM, and LightGBM. The second approach involved transforming ECG signals into images using Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plots (RP), with these images subsequently classified using CNN architectures like VGG and Inception. Experimental results demonstrate that the LightGBM model achieved the highest performance, with an accuracy of 99% and an F1 score of 0.94, outperforming the image-based CNN approach (F1 score of 0.85). Models such as SVM and AdaBoost yielded significantly lower scores, indicating limited suitability for this task. The findings underscore the superior ability of hand-crafted features to capture temporal and morphological variations in ECG signals compared to image-based representations of individual beats. Future investigations may benefit from incorporating multi-lead ECG signals and temporal dependencies across successive beats to enhance classification accuracy further.