This work describes a novel signal quality index (SQI), i.e. higher-order-statistics-SQI (hosSQI), for the real-time evaluation of electrocardiogram (ECG) recording quality. The hosSQI formula combines two already known SQls, kurtosis (kSQI) and skewness (sSQI), exploiting the related properties to improve their performance. We validated hosSQI using 1000 human pre-labelled twelve-lead ECGs and compared its performance with the state-of-the-art indexes in the literature. Our index outperformed four existing indexes (kSQI, sSQI, basSQI iorSQJ), reaching an accuracy up to 90.38% in the signal quality discrimination. Afterwards, we employed these indexes to compare signal quality of ECGs acquired by two different monitoring systems (red-dot and textile electrode based), in unfavourable conditions in terms of motion artifacts, adhesion and mechanical firmness of electrodes. The existing four SQIs and hosSQI were updated each second, using equine ECGs recorded during submaximal treadmill test. Wilcoxon nonparametric statistical test showed that all the SQIs were significantly higher for textile than for red-dot electrodes. A pattern recognition algorithm was implemented to test a real-time discrimination of three activity conditions (walk, trot, and gallop) based on the SQIs. Given that hosSQI values computed for red-dot were under the acceptability threshold in more than 63% of signals, we used only the textile data. We employed a C-Support Vector Classification and we found the highest accuracy value in the discrimination of walk and gallop (84.91%). Even if these results are preliminary, we proposed a promising tool for the real-time assessment of ECG signal quality and physical activity, also during intense exercise. (C) 2019 Elsevier Ltd. All rights reserved.
