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- W3136803510 abstract "According to the World Health Organization, stress is a considerable problem that affects both the mental well-being and physical health of people, so stress detection becomes an important task. Various stress detection methods based on the human brain and human behavior exist, but none of them uses both brain signals and heart signals together to detect stress. In this paper, a novel approach to detect stress using EEG and ECG signals is proposed. The proposed Stress Recognition by Neuroanalysis (Se.Re.Ne.) method is validated for k-nearest neighbors (KNN) and decision tree (DT) using the correlation method. Results evaluated using Se.Re.Ne. with KNN detect stress with a precision of 0.87, recall of 0.71, and f1-score of 0.78 with total accuracy of 68%, whereas Se.Re.Ne. with DT detects stress with a precision of 0.85, recall of 0.84, and f1-score of 0.84 with a total accuracy of 75%." @default.
- W3136803510 created "2021-03-29" @default.
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- W3136803510 date "2021-01-01" @default.
- W3136803510 modified "2023-10-09" @default.
- W3136803510 title "Se.Re.Ne.: Stress Detection Using EEG and ECG" @default.
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- W3136803510 doi "https://doi.org/10.1007/978-981-15-9712-1_16" @default.
- W3136803510 hasPublicationYear "2021" @default.
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