Globally suicidal behavior is the third most common cause of death among patients with major depressive disorder (MDD). This study presents multi-lag tone-entropy (T-E) analysis of heart rate variability (HRV) as a screening tool for identifying MDD patients with suicidal ideation. Sixty-one ECG recordings (10 min) were acquired and analyzed from control subjects (29 CONT), 16 MDD subjects with (MDDSI+) and 16 without suicidal ideation (MDDSI-). After ECG preprocessing, tone and entropy values were calculated for multiple lags (m: 1-10). The MDDSI+ group was found to have a higher mean tone value compared to that of the MDDSI- group for lags 1-8, whereas the mean entropy value was lower in MDDSI+ than that in CONT group at all lags (1-10). Leave-one-out cross-validation tests, using a classification and regression tree (CART), obtained 94.83 % accuracy in predicting MDDSI+ subjects by using a combination of tone and entropy values at all lags and including demographic factors (age, BMI and waist circumference) compared to results with time and frequency domain HRV analysis. The results of this pilot study demonstrate the usefulness of multi-lag T-E analysis in identifying MDD patients with suicidal ideation and highlight the change in autonomic nervous system modulation of the heart rate associated with depression and suicidal ideation.

Physiological and psychological underpinnings of suicidal behavior remain ill-defined and lessen timely diagnostic identification of this subgroup of patients. Arterial stiffness is associated with autonomic dysregulation and may be linked to major depressive disorder (MDD). The aim of this study was to investigate the association between arterial stiffness by photo-plethysmogram (PPG) in MDD with and without suicidal ideation (SI) by applying multiscale tone entropy (T-E) variability analysis. Sixty-one 10-min PPG recordings were analyzed from 29 control, 16 MDD patients with (MDDSI+) and 16 patients without SI (MDDSI−). MDD was based on a psychiatric evaluation and the Mini-International Neuropsychiatric Interview (MINI). Severity of depression was assessed using the Hamilton Depression Rating Scale (HAM-D). PPG features included peak (systole), trough (diastole), pulse wave amplitude (PWA), pulse transit time (PTT) and pulse wave velocity (PWV). Tone (Diastole) at all lags and Tone (PWA) at lags 8, 9, and 10 were found to be significantly different between the MDDSI+ and MDDSI− group. However, Tone (PWA) at all lags and Tone (PTT) at scales 3–10 were also significantly different between the MDDSI+ and CONT group. In contrast, Entropy (Systole), Entropy (Diastole) and Tone (Diastole) were significantly different between MDDSI− and CONT groups. The suicidal score was also positively correlated (r = 0.39 ~ 0.47; p < 0.05) with systolic and diastolic entropy values at lags 2–10. Multivariate logistic regression analysis and leave-one-out cross-validation were performed to study the effectiveness of multi-lag T–E features in predicting SI risk. The accuracy of predicting SI was 93.33% in classifying MDDSI+ and MDDSI− with diastolic T-E and lag between 2 and 10. After including anthropometric variables (Age, body mass index, and Waist Circumference), that accuracy increased to 96.67% for MDDSI+/− classification. Our findings suggest that tone-entropy based PPG variability can be used as an additional accurate diagnostic tool for patients with depression to identify SI.

Background and objectivesNeuropsychiatric disorders are of high concern and burden of disease in the United Arab Emirates (UAE). The aim of this study is to describe patient cost-sharing patterns, insurance coverage of ambulatory neuropsychiatric disorders, and utilization of neuropsychiatric services in Abu Dhabi.MethodsThe study utilized the data published by Health Authority-Abu Dhabi (HAAD) and the American Center for Psychiatry and Neurology (ACPN) records in Abu Dhabi. The data were collected from the ACPN to describe patterns of insurance coverage and patient cost-sharing. The data included information on patient visits to the ACPN from January 1, 2010 till May 16, 2013. The data also included insurance coverage, total cost of treatment for each patient and the amount of coinsurances and deductibles paid by each patient. Additionally, the study utilized data published by HAAD on health services utilization, and health insurance plans in 2014. The percentage of total costs paid by patients and insurance were calculated by insurance groups and health service. Insurance plans with different patient cost-sharing arrangements for mental health treatment benefits were divided into three groups. ANOVA and MANOVA analyses were performed to test for differences among three categories of neuropsychiatric services (neurology, psychiatry and psychotherapy) in terms of the total costs and patient cost-sharing. The data were analysed using STATA version 12.ResultsAbout 36 % of the total costs on ambulatory neuropsychiatric services was paid directly by patients; 1 % of total costs was covered by patients as co-insurances and deductibles, and 63 % of total costs was covered by insurance providers. The average cost per visit was about 485 AED ($132), including 304 AED ($83) paid by insurance and 181 AED ($49) paid by patient. About 44 % of total costs was related to psychiatry services, 28 % of total costs was related to neurology services, and 28 % of total costs was related to psychotherapy services. Using ANOVA analyses, statistical differences were found among three categories of neuropsychiatric services in terms of the total costs and patient cost-sharing. These findings provide hint on some degree of association between patient cost-sharing and neuropsychiatry services utilization.ConclusionsThe determination of parities in the coverage and finance between neuropsychiatric and physical health services will help policymakers make informed decisions on regulations of health insurance plans. Given the level of unmet need for neuropsychiatric services in Abu Dhabi, there is a need to fully include neuropsychiatric services in all basic and enhanced insurance plans. The study provided a description of patient cost-sharing and coverage of neuropsychiatric services in order for policymakers to recognize the disparities of the coverage and the degree of economic burden on households.

Major Depressive Disorder (MDD) is a serious mental disorder that if untreated not only affects physical health but also has a high risk of suicide. While the neurophysiological phenomena that contribute to the formation of Suicidal Ideation (SI) are still ill-defined, clear links between MDD and cardiovascular disease have been reported. The aim of this study is to extract suitable features from arterial pulse signals with a view to predicting SI within MDD and control groups. Sixteen unmedicated MDD patients with a history of SI (MDDSI+), sixteen without SI (MDDSI-) and twenty-nine healthy subjects (CONT) were recruited at a psychiatric clinic in the UAE. Depression severity and SI were assessed using the Hamilton Depression Rating Scale and Beck Depression Inventory. Pulse Wave Amplitude (PWA) was calculated as the difference between the peak (Systole) and the valley (Diastole) of the arterial pulse within each cardiac cycle. Then, 2D Tone-Entropy (TE) features were extracted from the Systole, Diastole and PWA time series. The TE features extracted from Diastole were the best markers for predicting MDDSI+. The overall classification accuracies of Classification and Regression Tree (CART) model by using TE features of Systole, Diastole and PWA were 88.52%, 90.2% and 88.52% respectively. When all TE features were combined, accuracy increased up to 93.44% in identifying MDDSI+/MDDSI-/Control groups.

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Major depressive disorder (MDD) is associated with a number of comorbidities including