On the Impact of ECG Data Quality for Arrhythmia Detection Using Convolutional Neural Networks and Wearable Devices

Cardiovascular diseases are the leading cause of death in the world, with arrhythmias being a significant symptom and risk factor. Advancements in technologies such as low-cost and low-power wearable devices, and machine learning techniques for analyzing big volumes of data offer opportunities to address this issue. However, low-cost devices may have limitations, including reduced data quality due to lower sampling rates, bit depth, and the number of leads recorded. These limitations might produce a significant decrease in machine learning models' performance in detecting arrhythmias. This study investigates the impact of data quality reduction on arrhythmia classification using deep neural network models. High-quality ECG data with 12 leads, 500Hz sampling rate, and 32-bits resolution were transformed into low-quality versions with varying leads (from one to six), 100Hz sampling rate, and 8-bits resolution. Training a state-of-the-art deep learning arrhythmia detection model on both high-quality and low-quality datasets revealed a decrease in performance from 95.3% to 93.9% in the worst case, which is concerning given the critical nature of the domain. To mitigate this performance loss, we propose an ensembling method that compensates for 42% of the loss, achieving an accuracy of 94.5% even with the low-quality dataset. The analysis also identifies the leads with the most promising classification performances. These results can aid in making better design decisions when creating cost-effective wearable ECG devices.