ECG Signal Features Classification for the Mental Fatigue Recognition

Mental fatigue is a major public health issue worldwide that common among both healthy and sick people. In the literature, various modern technologies, together with artificial intelligence techniques, have been proposed. Most techniques consider complex biosignals, such as electroencephalogram, electro-oculogram or classification of basic heart rate variability parameters. Additionally, most studies focus on particular area, driving, surgery, etc. this paper, novel approach presented combines electrocardiogram (ECG) signal feature extraction, principal component analysis (PCA), using machine learning algorithms. With aim daily mental recognition, an experiment was designed wherein ECG signals were recorded twice day: in morning, i.e., state without fatigue, evening, fatigued state. PCA results show parameters, Q R wave amplitude values, well QT T intervals, largest differences between states compared to other Furthermore, random forest classifier achieved more than 94.5% accuracy. This work demonstrates feasibility extraction for automatic detection.