Heart Rate Variability Measurement Can Be a Point-of-Care Sensing Tool for Screening Postpartum Depression: Differentiation from Adjustment Disorder.

Postpartum depression (PPD) is a serious mental health issue among women after childbirth, and screening systems that incorporate questionnaires have been utilized to screen for PPD. These questionnaires are sensitive but less specific, and the additional use of objective measures could be helpful. The present study aimed to verify the usefulness of a measure of autonomic function, heart rate variability (HRV), which has been reported to be dysregulated in people with depression. Among 935 women who had experienced childbirth and completed the Edinburgh Postnatal Depression Scale (EPDS), HRV was measured in EPDS-positive women (n = 45) 1 to 4 weeks after childbirth using a wearable device. The measurement was based on a three-behavioral-state paradigm with a 5 min duration, consisting of rest (Rest), task load (Task), and rest-after-task (After) states, and the low-frequency power (LF), the high-frequency power (HF), and their ratio (LF/HF) were calculated. Among the women included in this study, 12 were diagnosed with PPD and 33 were diagnosed with adjustment disorder (AJD). Women with PPD showed a lack of adequate HRV regulation in response to the task load, accompanying a high LF/HF score in the Rest state. On the other hand, women with AJD exhibited high HF and reduced LF/HF during the After state. A linear discriminant analysis using HRV indices and heart rate (HR) revealed that both the differentiation of PPD and AJD patients from the controls and that of PPD patients from AJD patients were possible. The sensitivity and specificity for PPD vs. AJD were 75.0% and 90.9%, respectively. Using this paradigm, an HRV measurement revealed the characteristic autonomic profiles of PPD and AJD, suggesting that it may serve as a point-of-care sensing tool in PPD screening systems.

Pulse rate variability estimation method based on imaging-photoplethysmography and application to telepsychiatry.

The worldwide adoption of telehealth services may benefit people who otherwise would not be able to access mental health support. In this paper, we present a novel algorithm to obtain reliable pulse and respiration signals from non-contact facial image sequence analysis. The proposed algorithm involved a skin pixel extraction method in the image processing part and signal reconstruction using the spectral information of RGB signal in the signal processing part. The algorithm was tested on 15 healthy subjects in a laboratory setting. The results show that the proposed algorithm can accurately monitor respiration rate (RR), pulse rate (PR), and pulse rate variability (PRV) in rest conditions.Clinical Relevance- The main achievement of this study is enabling non-contact PR and RR signal extraction from facial image sequences, which has potential for future use and support for psychiatrists in telepsychiatry.

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

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.

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

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.

The development and clinical application of a novel schizophrenia screening system using yoga-induced autonomic nervous system responses.

Background: In severe cases, schizophrenia can result in suicide and social isolation. Diagnosis delay can lead to worsening symptoms, and often results in prolonged therapy. An estimated 50%-80% of patients with schizophrenia are unaware of their condition. Biomarkers for schizophrenia are important for receiving a diagnosis from a psychiatrist at an early stage. Although previous studies have investigated near-infrared spectroscopy as a biomarker for schizophrenia, the required equipment is expensive and not designed for home use. Hence, we developed a novel home-use schizophrenia screening system that uses a wearable device to measure autonomic nervous system responses induced by yoga, which is frequently adopted in rehabilitation for schizophrenia. Materials and methods: The schizophrenia screening system automatically distinguishes patients with schizophrenia from healthy subjects via yoga-induced transient autonomic responses measured with a wearable wireless electrocardiograph (ECG) using linear discriminant analysis (LDA; Z score ≥ 0 → suspected schizophrenia, Z-score < 0 → healthy). The explanatory variables of LDA are averages of four indicators: components of heart rate variability (HRV): the very low-frequency (VLF), the low-frequency (LF), HR, and standard deviation of the NN intervals (SDNN). In the current study, HRV is defined as frequency domain HRV, which is determined by integrating RRI power spectrum densities from 0.0033 to 0.04 Hz (VLF) and 0.04-0.15 Hz (LF), and as time domain HRV, SDNN of which is calculated as the mean of the standard deviations of the RR intervals. These variables were measured before (5 min), during (15 min), and after (5 min) yoga in a 15-min mindfulness-based yoga program for schizophrenia (MYS). The General Health Questionnaire-28 (GHQ28) score was used to assess the severity of mental disorders for patients with schizophrenia and healthy volunteers. Twelve patients with schizophrenia (eight female and four male, 23-60 years old) and 16 healthy volunteers (seven female and nine male, 22-54 years old) were recruited. Results: The schizophrenia screening system achieved sensitivity of 91% and specificity of 81%. Z-scores of LDA were significantly correlated with GHQ28 scores (r = 0.45, p = 0.01). Conclusion: Our proposed system appears to be promising for future automated preliminary schizophrenia screening at home.

Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU.

BACKGROUND AND OBJECTIVE: Continuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU. METHODS: A non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates. RESULTS: The results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland-Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms. CONCLUSION: The proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings.

An mHealth App for the Non-contact Measurement of Pulmonary Function Using the Smartphone's Built-in Depth Sensor.

The use of smartphones in clinical practice is referred to as mobile health (mHealth). This has attracted great interest in both academia and industry because of its potential to augment healthcare. In this study, we developed an mHealth app for the non-contact measurement of chest-wall movements using the iPhone ' s built-in depth sensor, thereby enabling a pulmonary self-monitoring function for personal use. The depth sensor provides depth values for each pixel and 2D mapping of the chest-wall movements. To extract respiratory signals from the right and left thoracic regions and abdomen, a 2D-depth image-segmentation method was implemented. The method was based on the anatomy and physiology of chest-wall movements, assuming differences in the anterior displacement in the thoracic and abdominal regions. It was observed that the differences were significant in the segmented regions of interest (ROIs) of the right and left thoracic region and abdomen. Respiratory signals extracted from each ROI were compared with the contact bio-impedance signals, which were highly correlated (r=0.94).

Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation.

Background: To conduct a rapid preliminary COVID-19 screening prior to polymerase chain reaction (PCR) test under clinical settings, including patient's body moving conditions in a non-contact manner, we developed a mobile and vital-signs-based infection screening composite-type camera (VISC-Camera) with truncus motion removal algorithm (TMRA) to screen for possibly infected patients. Methods: The VISC-Camera incorporates a stereo depth camera for respiratory rate (RR) determination, a red-green-blue (RGB) camera for heart rate (HR) estimation, and a thermal camera for body temperature (BT) measurement. In addition to the body motion removal algorithm based on the region of interest (ROI) tracking for RR, HR, and BT determination, we adopted TMRA for RR estimation. TMRA is a reduction algorithm of RR count error induced by truncus non-respiratory front-back motion measured using depth-camera-determined neck movement. The VISC-Camera is designed for mobile use and is compact (22 cm × 14 cm × 4 cm), light (800 g), and can be used in continuous operation for over 100 patients with a single battery charge. The VISC-Camera discriminates infected patients from healthy people using a logistic regression algorithm using RR, HR, and BT as explanatory variables. Results are available within 10 s, including imaging and processing time. Clinical testing was conducted on 154 PCR positive COVID-19 inpatients (aged 18-81 years; M/F = 87/67) within the initial 48 h of hospitalization at the First Central Hospital of Mongolia and 147 healthy volunteers (aged 18-85 years, M/F = 70/77). All patients were on treatment with antivirals and had body temperatures <37.5°C. RR measured by visual counting, pulsimeter-determined HR, and BT determined by thermometer were used for references. Result: 10-fold cross-validation revealed 91% sensitivity and 90% specificity with an area under receiver operating characteristic curve of 0.97. The VISC-Camera-determined HR, RR, and BT correlated significantly with those measured using references (RR: r = 0.93, p < 0.001; HR: r = 0.97, p < 0.001; BT: r = 0.72, p < 0.001). Conclusion: Under clinical settings with body motion, the VISC-Camera with TMRA appears promising for the preliminary screening of potential COVID-19 infection for afebrile patients with the possibility of misdiagnosis as asymptomatic.

Design and Evaluation of Digital Filters for Non-Contact Measuring of HRV using Medical Radar and Its Application in Bedside Patient Monitoring System.

A non-contact bedside monitoring system using medical radar is expected to be applied to clinical fields. Our previous studies have developed a monitoring system based on medical radar for measuring respiratory rate (RR) and heart rate (HR). Heart rate variability (HRV), which is essentially implemented in advanced monitoring system, such as prognosis prediction, is a more challenging biological information than the RR and HR. In this study, we designed a HRV measurement filter and proposed a method to evaluate the optimal cardiac signal extraction filter for HRV measurement. Because the cardiac component in the radar signal is much smaller than the respiratory component, it is necessary to extract the cardiac element from the radar output signal using digital filters. It depends on the characteristics of the filter whether the HRV information is kept in the extracted cardiac signal or not. A cardiac signal extraction filter that is not distorted in the time domain and does not miss the cardiac component must be adopted. Therefore, we focused on evaluating the interval between the R-peak of the electrocardiogram (ECG) and the radar-cardio peak of the cardiac signal measured by radar (R-radar interval). This is based on the fact that the time between heart depolarization and ventricular contraction is measured as the R-radar interval. A band-pass filter (BPF) with several bandwidths and a nonlinear filter, locally projective adaptive signal separation (LoPASS), were analyzed and compared. The optimal filter was quantitatively evaluated by analyzing the distribution and standard deviation of the R-radar intervals. The performance of this monitoring system was evaluated in elderly patient at the Yokohama Hospital, Japan.

Non-contact Measurement of Pulse Rate Variability Using a Webcam and Application to Mental Illness Screening System.

The COVID-19 pandemic is a global health crisis. Mental health is critical in such uncertain situations, particularly when people are required to significantly restrict their movements and change their lifestyles. Under these conditions, many countries have turned to telemedicine to strengthen and expand mental health services. Our research group previously developed a mental illness screening system based on heart rate variability (HRV) analysis, enabling an objective and easy mental health self-check. This screening system cannot be used for telemedicine because it uses electrocardiography (ECG) and contact photoplethysmography (PPG), that are not widely available outside of a clinical setting. The purpose of this study is to enable the extension of the aforementioned system to telemedicine by the application of non-contact PPG using an RGB webcam, also called imaging- photoplethysmography (iPPG). The iPPG measurement errors occur due to changes in the relative position between the camera and the target, and due to changes in light. Conventionally, in image processing, the pixel value of the entire face region is used. We propose skin pixel extraction to eliminate blinks, eye movements, and changes in light and shadow. In signal processing, the green channel signal is conventionally used as a pulse wave owing to the absorption characteristics of blood flow. Taking advantage of the fact that the red and blue channels contain noise, we propose a signal reconstruction method for removing noise and strengthening the signal in the pulse rate variability (PRV) frequency band by weighting the three signals of the RGB camera. We conducted an experiment with 13 healthy subjects, and showed that the PRV index and pulse rate (PR) errors estimated by the proposed method were smaller than those of the conventional method. The correlation coefficients between estimated values by the proposed method and reference values of LF, HF, and PR were 0.86, 0.69, and 0.96, respectively.

Non-contact heart rate variability monitoring using Doppler radars located beneath bed mattress: a case report.

BACKGROUND: Heart rate variability (HRV) has been investigated previously in autonomic nervous system-related clinical settings. In these settings, HRV is determined by the time-series heartbeat peak-to-peak intervals using electrocardiography (ECG). To reduce patient discomfort, we designed a Doppler radar-based autonomic nervous activity monitoring system (ANMS) that allows cardiopulmonary monitoring without using ECG electrodes or spirometry monitoring. CASE SUMMARY: Using our non-contact ANMS, we observed a bedridden 80-year-old female patient with terminal phase sepsis developed the daytime Cheyne-Stokes respiration (CSR) associated with the attenuation of the low frequency (LF) and high frequency (HF) of HRV components 20 days prior to her death. The patient developed a marked linear decrease in the LF and the HF of HRV components for over 3 days in a row. Furthermore, after the decrease both the LF and the HF showed low and linear values. Around the intersection of the two lines, the decreasing LF and HF lines and the constant LF and HF lines, the ANMS automatically detected the daytime CSR pathogenesis. The attenuation rate of HF (1340 ms2/day) was higher than that of LF (956 ms2/day). Heart rate increased by ∼10 b.p.m. during these 3 days. DISCUSSION: We detected CSR-associated LF and HF attenuation in a patient with terminal phase sepsis using our ANMS. The proposed system without lead appears promising for future applications in clinical settings, such as remote cardiac monitoring of patients with heart failure at home or in long-term acute care facilities.

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

Using a linear discriminant analysis of heart rate variability (HRV) indices, the present study sought to verify the usefulness of autonomic measurement in major depressive disorder (MDD) patients by assessing the feasibility of their return to work after sick leave. When reinstatement was scheduled, patients' HRV was measured using a wearable electrocardiogram device. The outcome of the reinstatement was evaluated at one month after returning to work. HRV indices including high- and low-frequency components were calculated in three conditions within a session: initial rest, mental task, and rest after task. A linear discriminant function was made using the HRV indices of 30 MDD patients from our previous study to effectively discriminate the successful reinstatement from the unsuccessful reinstatement; this was then tested on 52 patients who participated in the present study. The discriminant function showed that the sensitivity and specificity in discriminating successful from unsuccessful returns were 95.8% and 35.7%, respectively. Sensitivity is high, indicating that normal HRV is required for a successful return, and that the discriminant analysis of HRV indices is useful for return-to-work screening in MDD patients. On the other hand, specificity is low, suggesting that other factors may also affect the outcome of reinstatement.

Development of a Novel Web Camera-Based Contact-Free Major Depressive Disorder Screening System Using Autonomic Nervous Responses Induced by a Mental Task and Its Clinical Application.

BACKGROUND: To increase the consultation rate of potential major depressive disorder (MDD) patients, we developed a contact-type fingertip photoplethysmography-based MDD screening system. With the outbreak of SARS-CoV-2, we developed an alternative to contact-type fingertip photoplethysmography: a novel web camera-based contact-free MDD screening system (WCF-MSS) for non-contact measurement of autonomic transient responses induced by a mental task. METHODS: The WCF-MSS measures time-series interbeat intervals (IBI) by monitoring color tone changes in the facial region of interest induced by arterial pulsation using a web camera (1920 × 1080 pixels, 30 frames/s). Artifacts caused by body movements and head shakes are reduced. The WCF-MSS evaluates autonomic nervous activation from time-series IBI by calculating LF (0.04-0.15 Hz) components of heart rate variability (HRV) corresponding to sympathetic and parasympathetic nervous activity and HF (0.15-0.4 Hz) components equivalent to parasympathetic activities. The clinical test procedure comprises a pre-rest period (Pre-R; 140 s), mental task period (MT; 100 s), and post-rest period (Post-R; 120 s). The WCF-MSS uses logistic regression analysis to discriminate MDD patients from healthy volunteers via an optimal combination of four explanatory variables determined by a minimum redundancy maximum relevance algorithm: HF during MT (HF MT ), the percentage change of LF from pre-rest to MT (%ΔLF(Pre-R⇒MT) ), the percentage change of HF from pre-rest to MT (%ΔHF(Pre-R⇒MT) ), and the percentage change of HF from MT to post-rest (%ΔHF(MT⇒Post-R) ). To clinically test the WCF-MSS, 26 MDD patients (16 males and 10 females, 20-58 years) were recruited from BESLI Clinic in Tokyo, and 27 healthy volunteers (15 males and 12 females, 18-60 years) were recruited from Tokyo Metropolitan University and RICOH Company, Ltd. Electrocardiography was used to calculate HRV variables as references. RESULT: The WCF-MSS achieved 73% sensitivity and 85% specificity on 5-fold cross-validation. IBI correlated significantly with IBI from reference electrocardiography (r = 0.97, p < 0.0001). Logit scores and subjective self-rating depression scale scores correlated significantly (r = 0.43, p < 0.05). CONCLUSION: The WCF-MSS seems a promising contact-free MDD screening apparatus. This method enables web camera built-in smartphones to be used as MDD screening systems.

A Non-contact Spirometer with Time-of-Flight Sensor for Assessment of Pulmonary Function.

Assessment of pulmonary function is vital for early detection of chronic diseases such as chronic obstructive pulmonary disease (COPD) in home healthcare. However, monitoring of pulmonary function is often omitted owing to the heavy burden that the use of specific medical devices places on the patients. In this study, we developed a non-contact spirometer using a time-of-flight sensor that measures very small displacements caused by chest wall motion during breathing. However, this sensor occasionally failed when estimating the values from breathing waveforms because their shape depends on the subject test experience. As a result, further measurements were required to address motion artifacts. To accomplish high accuracy estimation in the face of these factors, we developed methods to estimate parameters from a part of the waveform and remove outliers from multiple-region measurements. According to laboratory experiments, the proposed system achieved an absolute error of 5.26 % and a correlation coefficient of 0.88. This study also addressed the limitations of depth sensor measurements, thereby contributing to the implementation of high-accuracy COPD screening.

Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.

Obstructive pulmonary diseases, such as diffuse panbronchiolitis (DPB), asthma, chronic obstructive pulmonary disease (COPD), and asthma COPD overlap syndrome (ACOS) trigger a severe reaction at some situations. Detecting early airflow limitation caused by diseases above is critical to stop the progression. Thus, there is a need for tools to enable self-screening of early airflow limitation at home. Here, we developed a novel non-contact early airflow limitation screening system (EAFL-SS) that does not require calibration to the individual by a spirometer. The system is based on an infrared time-of-flight (ToF) depth image sensor, which is integrated into several smartphones for photography focusing or augmented reality. The EAFL-SS comprised an 850 nm infrared ToF depth image sensor (224 × 171 pixels) and custom-built data processing algorithms to visualize anterior-thorax three-dimensional motions in real-time. Multiple linear regression analysis was used to determine the amount of air compulsorily exhaled after maximal inspiration (referred to as the forced vital capacity, FVC EAFL -SS) from the ToF-derived anterior-thorax forced vital capacity (FVC), height, and body mass index as explanatory variables and spirometer-derived FVC as the objective variable. The non-contact measurement is automatically started when an examinee is sitting 35 cm away from the EAFL-SS. A clinical test was conducted with 32 COPD patients (27/5 M/F, 67-93 years) as typical airflow limitation cases recruited at St. Marianna University Hospital and 21 healthy volunteers (10/11 M/F, 23-79 years). The EAFL-SS was used to monitor the respiration of examinees during forced exhalation while sitting still, and a spirometer was used simultaneously as a reference. The forced expiratory volume in 1 s (FEV1% EAFL -SS) was evaluated as a percentage of the FVC EAFL -SS, where values less than 70% indicated suspected airflow limitation. Leave-one-out cross-validation analysis revealed that this system provided 81% sensitivity and 90% specificity. Further, the FEV1 EAFL -SS values were closely correlated with that measured using a spirometer (r = 0.85, p < 0.0001). Hence, EAFL-SS appears promising for early airflow limitation screening at home.

Changes in Heart Rate Variability after Yoga are Dependent on Heart Rate Variability at Baseline and during Yoga: A Study Showing Autonomic Normalization Effect in Yoga-Naïve and Experienced Subjects.

BACKGROUND: Yoga therapy is widely applied to the maintenance of health and to treatment of various illnesses. Previous researches indicate the involvement of autonomic control in its effects, although the general agreement has not been reached regarding the acute modulation of autonomic function. AIM: The present study aimed at revealing the acute effect of yoga on the autonomic activity using heart rate variability (HRV) measurement. METHODS: Twenty-seven healthy controls participated in the present study. Fifteen of them (39.5 ± 8.5 years old) were naïve and 12 (45.1 ± 7.0 years old) were experienced in yoga. Yoga skills included breath awareness, two types of asana, and two types of pranayama. HRV was measured at the baseline, during yoga, and at the resting state after yoga. RESULTS: In both yoga-naïve and experienced participants, the changes in low-frequency (LF) component of HRV and its ratio to high-frequency (HF) component (LF/HF) after yoga were found to be correlated negatively with the baseline data. The changes in LF after yoga were also correlated with LF during yoga. The changes in HF as well as the raw HRV data after yoga were not related to the baseline HRV or the HRV during yoga. CONCLUSION: The results indicate that yoga leads to an increase in LF when LF is low and leads to a decrease in LF when it is high at the baseline. This normalization of LF is dependent on the autonomic modulation during yoga and may underlie the clinical effectiveness of yoga therapy both in yoga-naïve and experienced subjects.

Usefulness of heart rate variability indices in assessing the risk of an unsuccessful return to work after sick leave in depressed patients.

AIM: The present study aimed to examine whether heart rate variability (HRV) indices in depressed patients measured at return to work after sick leave are related to the outcome of reinstatement. METHODS: This study included 30 workers who took a leave of absence due to major depressive disorder. HRV was measured twice, once when participants left work and another when they returned to work. One month after returning to work, 19 participants continued their original work (successful return group), while 11 failed to perform their original work (unsuccessful return group). HRV indices including high- and low-frequency components (HF and LF) were calculated in three conditions within a session lasting for about 5 minutes, initial rest (Rest), mental task (Task), and rest after task (After), and were compared between the two participant groups. Psychological states were evaluated using Self-rating Depression Scale and State-Trait Anxiety Inventory. RESULTS: No significant differences were observed in the HRV indices on leaving work between groups. On returning to work, the "unsuccessful return group" exhibited lower HF Rest score, higher HF Task/Rest ratio, and higher LF/HF Rest score than the "successful return group." Psychological scores improved in both groups. CONCLUSION: These results indicate that autonomic dysregulations revealed by HRV measurement at return to work after a leave of absence in MDD patients were related to the outcome of reinstatement and can serve as useful information for the assessment of the risk of unsuccessful return.