Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 16; p. 5222
• Main Authors: Park, Yeonjae, Park, You Hyun, Jeong, Hoyeon, Kim, Kise, Jung, Ji Ye, Kim, Jin-Bae, Kang, Dae Ryong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.08.2024; MDPI
• Subjects: Algorithms; arrhythmia classification; Arrhythmias, Cardiac - diagnosis; Cardiac arrhythmia; Cardiac stress tests; Classification; Deep Learning; electrocardiogram denoising; Electrocardiography; Electrocardiography - methods; generative adversarial network; Humans; Neural Networks, Computer; Signal processing; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Wearable computers; wearable device; Wearable Electronic Devices; arrhythmia classification; generative adversarial network; wearable device; electrocardiogram denoising
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24165222

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.