Reliable peak detection and feature extraction for wireless electrocardiograms

The electrocardiogram (ECG) is a vital device to examine the electrical activities of the heart. It is useful for diagnosing cardiovascular diseases, which often manifest themselves through alterations in the ECG signals' characteristics. These alterations are primarily observed in the signals' key components: the Q, R, S, T, and P peaks. At present, cardiologists typically rely on manual inspection of ECG measurements taken in controlled environments, such as hospitals and clinics, but most cardiac conditions reveal themselves outside clinical settings, when patients freely move and exert. In this paper, we dynamically identify and extract prominent ECG features in measurements taken outside clinical settings by subjects who have no medical training. The activities we consider are typical activities cardiac patients carry out in residential and rehabilitation environments, such as sitting, climbing up and down stairs, and standing up. To achieve accurate feature extraction, we employ adaptive thresholding and localization techniques. Our approach achieves promising results, with an average% for R peak detection and 92% for Q and S peaks detection. Similarly, our approach enables the detection of T and P peaks with an average accuracy of 87% and 84%, respectively.