Heart rate variability (HRV) has been well established to measure instantaneous levels of mental stress. Circadian patterns of HRV features have been reported but their use to estimate levels of mental stress were not studied thoroughly. In this study, we investigated time dependent variations of HRV features to detect subjects under chronic mental stress. Sixty eight subjects were divided into high (n=10) and low stress group (n=43) depending on their self-reporting stress scores. HRV features were calculated during three different time periods of the day. High stress group showed decreased patterns of HRV features compared to low stress group. When logistic regression analysis was performed with raw multiple HRV features, the classification was 63.2% accurate. A new % deviance score reflecting the degree of difference from normal reference patterns increased the accuracy to 66.1%. Our data suggested that HRV patterns obtained at multiple time points of the day could provide useful data to monitor subjects under chronic stress.
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http://dx.doi.org/10.1109/IEMBS.2008.4649244 | DOI Listing |
Sci Rep
January 2025
Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
Assessing cognitive load using pupillography frequency features presents a persistent challenge due to the lack of consensus on optimal frequency limits. This study aims to address this challenge by exploring pupillography frequency bands and seeking clarity in defining the most effective ranges for cognitive load assessment. From a controlled experiment involving 21 programmers performing software bug inspection, our study pinpoints the optimal low-frequency (0.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072 China.
Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
View Article and Find Full Text PDFPhysiol Meas
January 2025
Department for Psychosomatic Medicine and Psychotherapy, Universitatsklinikum Jena, Philosophenweg 3, Jena, Thüringen, 07743, GERMANY.
Heart rate variability (HRV) is an important indicator of cardiac autonomic function. Given its clinical significance, reliable HRV assessment is crucial. Here, we assessed test-retest stability, as a key aspect of reliability, quantifying the consistency of a measure when repeated under the same conditions.
View Article and Find Full Text PDFFront Neurosci
January 2025
Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Introduction: Delirium, frequently experienced by ischemic stroke patients, is one of the most common neuropsychiatric syndromes reported in the Intensive Care Unit (ICU). Stroke patients with delirium have a high mortality rate and lengthy hospitalization. For these reasons, early diagnosis of delirium in the ICU is critical for better patient prognosis.
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