Abstract

Over 350 million people across the world suffer from major depressive disorder (MDD). More than 10% of MDD patients have suicide intent, while it has been reported that more than 40% patients did not consult their doctors for MDD. In order to increase consultation rate of potential MDD patients, we developed a novel MDD screening system which can be used at home without help of health-care professionals. Using a fingertip photoplethysmograph (PPG) sensor as a substitute of electrocardiograph (ECG), the system discriminates MDD patients from healthy subjects using autonomic nerve transient responses induced by a mental task (random number generation) via logistic regression analysis. The nine logistic regression variables are averages of heart rate (HR), high frequency (HF) component of heart rate variability (HRV), and the low frequency (LF)/HF ratio of HRV before, during, and after the mental task. We conducted a clinical test of the proposed system. Participants were 6 MDD patients (4 females and 2 males, aged 23–60 years) from Shizuoka Saiseikai General Hospital psychiatry outpatient unit and 14 healthy volunteers from University of Electro-Communications (6 females and 8 males, aged 21–63 years). The average PPG- and ECG (as a reference)-derived HR, HF and LF/HF were significantly correlated with each other (HR; r = 1.00, p < 0.0001, HF; r = 0.98, p < 0.0001, LF/HF; r = 0.98, p < 0.0001). Leave-one-out cross validation (LOOCV) revealed 83% sensitivity and 93% specificity. The proposed system appears promising for future MDD self-screening at home and are expected to encourage psychiatric visits for potential MDD patients.

Highlights

  • The number of patients diagnosed with major depressive disorder (MDD) has been increasing in recent years around the world and scientists predict that mood disorders induced by MDD will be the primary cause of mental disability by 2020 (Shinba, 2014; WHO, 2017)

  • We have developed an MDD screening method based on autonomic responses during a mental task, which combines heart rate variability (HRV) with logistic regression analysis

  • The HR, high frequency (HF), and low frequency (LF)/HF determined by PPG significantly correlated with those calculated from ECG signals (HR; r = 1.0, p < 0.0001, HF; r = 0.98, p < 0.0001; LF/HF; r = 0.98, p < 0.0001)

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Summary

Introduction

The number of patients diagnosed with major depressive disorder (MDD) has been increasing in recent years around the world and scientists predict that mood disorders induced by MDD will be the primary cause of mental disability by 2020 (Shinba, 2014; WHO, 2017). Delaying the start of medical treatment can lead to exacerbated symptoms and greater daily disability To overcome such incidents, we have developed an MDD screening method based on autonomic responses during a mental task, which combines heart rate variability (HRV) with logistic regression analysis. ECG measurement requires electrodes to be attached to a person’s body, supervision of medical personnel, and inconveniences associated with any trips to a clinic or a hospital. To avoid such stressful and restrained screening for MDD, we have developed a novel MDD screening system that incorporates fingertip photoplethysmographic (PPG) sensor and logistic regression analysis (Figure 1)

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