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Physiological Signal Sensing for Mental Health Monitoring and Management

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 19809

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Special Issue Editor

Dr. Toshikazu Shinba

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Guest Editor

Department of Psychiatry, Shizuoka Saiseikai General Hospital, Shizuoka 422-8527, Japan
Interests: psychophysiology; arousal; consciousness; mental disorders; EEG; ERP; heart rate variability; skin conductance; brain stimulation
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Special Issue Information

Dear Colleagues,

In the clinical practices of psychiatric disorders as well as in the management of mental health, there is an increasing need to use physiological measures. This Special Issue welcomes the researches which utilize physiological signals and process the data for understanding and treating mental disturbances. Both clinical and basic studies will be accepted.

Compact sensing systems are desirable for real-time monitoring and processing the physiological signals. Physiological signals include electroencephalogram, event-related potential, slow potential, heart rate, heart rate variability, skin condunctance, pupil size, gut movement, and cerebral blood flow.

The research topics cover mental disorders including depression, anxiety, stress-related disorders, schizophrenia, developmental disorders, dementia, sleep disturbances, and delirium. The researchs on mental health in the normal polulation are also suitable.

In the research, it is preferred to assess and discuss the interventions for ameliorating and treating the mental symptoms depending on the results of the physiological signal processing. The interventions may be physical (ex. brain stimulation), pharmacological, behavioral, and psychological. The studies using neurofeedback and brian–machine interface are welcome. Human basic studies and animal studies which aim to develop the monitoring and management systems are also appropriate.

This special issue provides the forum for the scientific researches covering clinical application of physiological sensors for monitoring and maintaining the mental health

Dr. Toshikazu Shinba
Guest Editor

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Keywords

physiological sensors
real-time monitoring
therapeutic intervention
electroencephalogram
event-related potential
slow potential
heart rate variability
skin conductance
pupil size
gut movement
cerebral blood flow
electromagnetic stimulation
mental health

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Published Papers (4 papers)

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Research

Jump to: Review, Other

13 pages, 446 KiB

Open AccessArticle

Major Depressive Disorder and Chronic Fatigue Syndrome Show Characteristic Heart Rate Variability Profiles Reflecting Autonomic Dysregulations: Differentiation by Linear Discriminant Analysis

by Toshikazu Shinba, Daisuke Kuratsune, Shuntaro Shinba, Yujiro Shinba, Guanghao Sun, Takemi Matsui and Hirohiko Kuratsune

Sensors 2023, 23(11), 5330; https://doi.org/10.3390/s23115330 - 4 Jun 2023

Cited by 5 | Viewed by 6007

Abstract

Major depressive disorder (MDD) and chronic fatigue syndrome (CFS) have overlapping symptoms, and differentiation is important to administer the proper treatment. The present study aimed to assess the usefulness of heart rate variability (HRV) indices. Frequency-domain HRV indices, including high-frequency (HF) and low-frequency (LF) components, their sum (LF+HF), and their ratio (LF/HF), were measured in a three-behavioral-state paradigm composed of initial rest (Rest), task load (Task), and post-task rest (After) periods to examine autonomic regulation. It was found that HF was low at Rest in both disorders, but was lower in MDD than in CFS. LF and LF+HF at Rest were low only in MDD. Attenuated responses of LF, HF, LF+HF, and LF/HF to task load and an excessive increase in HF at After were found in both disorders. The results indicate that an overall HRV reduction at Rest may support a diagnosis of MDD. HF reduction was found in CFS, but with a lesser severity. Response disturbances of HRV to Task were observed in both disorders, and would suggest the presence of CFS when the baseline HRV has not been reduced. Linear discriminant analysis using HRV indices was able to differentiate MDD from CFS, with a sensitivity and specificity of 91.8% and 100%, respectively. HRV indices in MDD and CFS show both common and different profiles, and can be useful for the differential diagnosis. Full article

(This article belongs to the Special Issue Physiological Signal Sensing for Mental Health Monitoring and Management)

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16 pages, 2136 KiB

Open AccessArticle

Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices

by Shohei Sato, Takuma Hiratsuka, Kenya Hasegawa, Keisuke Watanabe, Yusuke Obara, Nobutoshi Kariya, Toshikazu Shinba and Takemi Matsui

Sensors 2023, 23(8), 3867; https://doi.org/10.3390/s23083867 - 10 Apr 2023

Cited by 3 | Viewed by 3431

Abstract

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity. Full article

(This article belongs to the Special Issue Physiological Signal Sensing for Mental Health Monitoring and Management)

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Review

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18 pages, 1388 KiB

Open AccessReview

Stress and Workload Assessment in Aviation—A Narrative Review

by Giulia Masi, Gianluca Amprimo, Claudia Ferraris and Lorenzo Priano

Sensors 2023, 23(7), 3556; https://doi.org/10.3390/s23073556 - 28 Mar 2023

Cited by 24 | Viewed by 8275 | Correction

Abstract

In aviation, any detail can have massive consequences. Among the potential sources of failure, human error is still the most troublesome to handle. Therefore, research concerning the management of mental workload, attention, and stress is of special interest in aviation. Recognizing conditions in which a pilot is over-challenged or cannot act lucidly could avoid serious outcomes. Furthermore, knowing in depth a pilot’s neurophysiological and cognitive–behavioral responses could allow for the optimization of equipment and procedures to minimize risk and increase safety. In addition, it could translate into a general enhancement of both the physical and mental well-being of pilots, producing a healthier and more ergonomic work environment. This review brings together literature on the study of stress and workload in the specific case of pilots of both civil and military aircraft. The most common approaches for studying these phenomena in the avionic context are explored in this review, with a focus on objective methodologies (e.g., the collection and analysis of neurophysiological signals). This review aims to identify the pros, cons, and applicability of the various approaches, to enable the design of an optimal protocol for a comprehensive study of these issues. Full article

(This article belongs to the Special Issue Physiological Signal Sensing for Mental Health Monitoring and Management)

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