ECG Signal Features Classification for the Mental Fatigue Recognition

Eglė Butkevičiūtė (), Aleksėjus Michalkovič and Liepa Bikulčienė
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Eglė Butkevičiūtė: Department of Software Engineering, Kaunas University of Technology, Studentu Str. 50, 51368 Kaunas, Lithuania
Aleksėjus Michalkovič: Department of Applied Mathematics, Kaunas University of Technology, Studentu Str. 50, 51368 Kaunas, Lithuania
Liepa Bikulčienė: Department of Applied Mathematics, Kaunas University of Technology, Studentu Str. 50, 51368 Kaunas, Lithuania

Mathematics, 2022, vol. 10, issue 18, 1-18

Abstract: Mental fatigue is a major public health issue worldwide that is 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 a particular area, such as driving, surgery, etc. In this paper, a novel approach is presented that combines electrocardiogram (ECG) signal feature extraction, principal component analysis (PCA), and classification using machine learning algorithms. With the aim of daily mental fatigue recognition, an experiment was designed wherein ECG signals were recorded twice a day: in the morning, i.e., a state without fatigue, and in the evening, i.e., a fatigued state. PCA analysis results show that ECG signal parameters, such as Q and R wave amplitude values, as well as QT and T intervals, presented with the largest differences between states compared to other ECG signal parameters. Furthermore, the random forest classifier achieved more than 94.5% accuracy. This work demonstrates the feasibility of ECG signal feature extraction for automatic mental fatigue detection.

Keywords: machine learning; ECG; mental fatigue; signal analysis; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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