Quality Assessment of Ambulatory ECG Using Wavelet Entropy of the HRV Signal.

Data in recordings obtained from ambulatory patients using wearable sensors are often corrupted by motion artefact and are, in general, noisier than the data obtained from the nonmobile patients. Identifying and ignoring erroneous measurements from these data is very important, if wearable sensors are to be incorporated into clinical practice. In this paper, we propose a novel Signal Quality Index, intended to assess whether reliable heart rates can be obtained from a single channel of ECG collected from ambulatory patients, using wearable sensors. The proposed system is based on wavelet entropy measurements of the heart rate variability signal. The system was trained and tested on expert-labeled data from a particular wearable sensor and was also tested on labeled data from a different sensor. The sensitivities and specificities achieved were 94% and 98%, respectively, on data from the same sensor as the training set, and 91% and 97%, respectively, on data from a different sensor, indicating the potential of the system to generalize across different sensors. Because the system relies on a single channel of ECG, it has the potential for inclusion in applications using wearable sensors and in the most basic clinical environments.