Key result
1-D CNN combining frequency and RR features achieves 99% ECG classification accuracy despite noise.
Why the study?
Manual ECG classification requires experience and great skill while being time-consuming and prone to error, driving the need for machine learning models that maintain high accuracy and noise immunity for wearable applications.
A novel 1-D CNN model using hybrid features achieves 99% accuracy for ECG classification with high noise immunity, demonstrating potential for ambulatory wearable sensing applications.
May support wearable ECG monitoring; extends baseline CNNs yet leaves open prospective clinical validation.
Sudden cardiac death and arrhythmia account for a large percentage of all deaths worldwide. Electrocardiography (ECG) is the most widely used screening tool for cardiovascular diseases. Traditionally, ECG signals are classified manually, requiring experience and great skill, while being time-consuming and prone to error. Thus machine learning algorithms have been widely adopted because of their ability to perform complex data analysis. Features derived from the points of interest in ECG - mainly Q, R, and S, are widely used for arrhythmia detection. In this work, we demonstrate improved performance for ECG classification using hybrid features and three different models, building on a 1-D convolutional neural network (CNN) model that we had proposed in the past. An RR interval features based model proposed in this work achieved an accuracy of 98.98%, which is an improvement over the baseline model. To make the model immune to noise, we updated the model using frequency features and achieved good sustained performance in presence of noise with a slightly lower accuracy of 98.69%. Further, another model combining the frequency features and the RR interval features was developed, which achieved a high accuracy of 99% with good sustained performance in noisy environments. Due to its high accuracy and noise immunity, the proposed model which combines multiple hybrid features is well suited for ambulatory wearable sensing applications.
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Li et al. (2023) studied Arrhythmia. 1-D convolutional neural network (CNN) model with hybrid features vs. Baseline 1-D CNN model was evaluated on ECG classification accuracy. A 1-D convolutional neural network model combining frequency and RR interval features achieved 99% accuracy for ECG classification with good sustained performance in noisy environments.
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