Artifact removal from ECG signals using online recursive independent component analysis

The diagnosis of cardiac abnormalities and monitoring of heart health heavily rely on Electrocardiogram (ECG) signals. Unfortunately, these signals frequently encounter interference from diverse artifacts, impeding precise interpretation and analysis. To overcome this challenge, we suggest a novel method for real-time artifact removal from ECG signals through the utilization of Online Recursive Independent Component Analysis (ORICA). Our study outlines a systematic preprocessing pipeline, adaptively estimating the mixing matrix and demixing matrix of the ICA model while streaming data is processed. Additionally, we explore the selection of appropriate ICA components and the use of relevant feature extraction techniques to enhance the quality of extracted cardiac signals. This research presents a promising solution for removing artifacts from ECG signals in real-time, paving the way for improved cardiac diagnostics and monitoring systems. Comparative analyses demonstrate significant improvements in the accuracy of subsequent ECG analysis and interpretation following the application of our ORICA-based preprocessing.