Improving deep-learning electrocardiogram classification with an effective coloring method

• Published in: Artificial intelligence in medicine Vol. 149; p. 102809
• Main Authors: Chen, Wei-Wen, Tseng, Chien-Chao, Huang, Ching-Chun, Lu, Henry Horng-Shing
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2024
• Subjects: Atrial fibrillation; Cardiovascular Diseases - diagnosis; Deep Learning; Demographic information; ECG colorization; Electrocardiography; Feature coloring; Humans; Precision Medicine; PTB-XL; Deep learning; PTB-XL; Feature coloring; Atrial fibrillation; Demographic information; ECG colorization
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2024.102809

Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal role in cardiovascular disease diagnosis. Although previous studies have focused on waveform analysis and model training, integrating additional clinical information, especially demographic data, remains challenging. In this study, we present an innovative approach to ECG classification by incorporating demographic information from patients’ medical histories through a colorization technique. Our proposed method maps demographic features onto the (R, G, B) color space through normalized scaling. Each demographic feature corresponds to a distinct color, allowing for different ECG leads to be colored. This approach preserves the relationships between data by maintaining the color correlations in the statistical features, enhancing ECG analytics and supporting precision medicine. We conducted experiments with PTB-XL dataset and achieved 1%–6% improvements in the area under the receiving operator characteristic curve performance compared with other methods for various classification problems. Notably, our method excelled in multiclass and challenging classification tasks. The combined use of color features and the original waveform shape features enhanced prediction accuracy for various deep learning models. Our findings suggest that colorization is a promising avenue for advancing ECG classification and diagnosis, contributing to improved prediction and diagnosis of cardiovascular diseases and ultimately enhancing clinical outcomes. •ECG signals can be colorized by the related clinical factors.•Colorization incorporates clinical information to ECG signals.•Colorization ECG can be used with various deep learning models.•Integrating clinical information by colorizing ECG improves analytics.•Feasible for precision medicine based on ECG and clinical data.