Introduction to electrocardiogram signal quality assessment and estimated accuracy for textile electrodes

As the use of wearable electrocardiogram (ECG) data for modeling purposes continues to rise, there is a pressing need for signal quality assessment (SQA) algorithms capable of identifying segments of signal from which reliable data can be obtained. Manually annotated ECG data, obtained through expert visual inspection, is often used as reference in the development of ECG SQA algorithms. In this approach, the quality of a signal segment is assessed based on the level of noise present. Yet, the data extracted from noise-corrupted ECG signal segments might still be of sufficient accuracy depending on the target application. The current work proposes a paradigm shift by presenting a SQA algorithm that performs template matching and physiological feasibility checks to determine the quality of ECG signals acquired by textile-based wearable systems. Signal segments were classified into four different quality classes based on the estimated accuracy of RR intervals extracted from the signal segments of each class. Our findings show that the proposed SQA algorithm is effective in identifying ECG signal segments from which accurate RR intervals can be derived, and that the proportion of the data across the different classes is sensitive to different factors known to have an effect on signal quality.