Entropy Analysis of Electrocardiograms for Cognitive Performance Evaluation

In recent times, we have witnessed a significant expansion of the Internet of Things (IoT) and the development of machine learning algorithms for various applications. In this context, selecting relevant features is crucial. Cognitive load tests affect physiological signals such as electrocardiogram (ECG) signals. This paper deals with an initial collecting of ECGs during cognitive effect examination using a low-cost system. The signals correspond to states before and after the volunteer performs a cognitive test. Besides heart rate variability, in this paper entropy analysis is performed. The obtained effects and possibilities of distinguishing between the states before and after the cognitive test are also considered.