Return-to-Work Screening by Linear Discriminant Analysis of Heart Rate Variability Indices in Depressed Subjects

Shinba, Toshikazu; Murotsu, Keizo; Usui, Yosuke; Andow, Yoshinori; Terada, Hiroshi; Kariya, Nobutoshi; Tatebayashi, Yoshitaka; Matsuda, Yoshiki; Mugishima, Go; Shinba, Yujiro; Sun, Guanghao; Matsui, Takemi

doi:10.3390/s21155177

Cited by 7 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is hoped that noninvasive, unrestrained, objective MDD screening can be achieved using wearable devices, which may also promote early medical consultations and self-management. In particular, the data shown in Figure 6 related to MDD risk can be easily understood by patients and will benefit those seeking to return to work after consultation and treatment [65,66].…”

Section: Discussionmentioning

confidence: 99%

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

Sato

Hiratsuka

Hasegawa

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Sato

Hiratsuka

Hasegawa

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…It should be important to understand the differences and similarities to evaluate the pathophysiology of depression and anxiety in clinical practices. Differentiation of depressive and anxious conditions using HRV measures can be useful for adequate pharmacological, psychological, and behavioral therapies [14].…”

Section: Discussionmentioning

confidence: 99%

Characteristic Profiles of Heart Rate Variability in Depression and Anxiety

Shinba¹

2022

Biomedical Engineering

Self Cite

View full text Add to dashboard Cite

This chapter on heart rate variability (HRV) presents a view based on our published data that HRV profiles in depression and anxiety show differences and can be used for the differentiation of these two disorders in clinical practices. Characteristic HRV profiles in depression and anxiety are revealed by measurements incorporating task load. Analysis of two frequency-domain HRV parameters, low frequency (LF) and high frequency (HF), together with heart rate (HR) itself measured at rest (Rest), during the task load (Task), and at rest after the task (After) enables the evaluation of the autonomic regulation in response to behavioral changes with different stress levels. LF is the heart rate modulation related to blood pressure changes to stabilize circulation. HF is related to breathing rhythm and reflects parasympathetic activity. It has been indicated that LF, HF, their ratio LF/HF and HR in depression and anxiety show characteristic dysregulations during Rest, Task and After. These HRV profiles are useful for understanding the pathophysiology of the disorders.

show abstract

“…In order to further examine the usefulness of HRV indices for differentiating MDD, CFS, and control groups, linear discriminant analysis was employed in the present study. The details can be found in our previous publication [35]. Discriminant analysis was performed to make a linear equation composed of the HRV indices multiplied by coefficients and a constant (Equation ( 1)).…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

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

Shinba

Kuratsune

Shinba³

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Return-to-Work Screening by Linear Discriminant Analysis of Heart Rate Variability Indices in Depressed Subjects

Cited by 7 publications

References 23 publications

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

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

Characteristic Profiles of Heart Rate Variability in Depression and Anxiety

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

Contact Info

Product

Resources

About