A support vector machine model using single-lead ECG during sleep detected major depressive disorder in patients with obstructive sleep apnea with 78.18% accuracy and 81.25% specificity.
Observational (n=55)
Can a support vector machine model using single-lead ECG during sleep detect major depressive disorder in patients with obstructive sleep apnea?
A machine learning model using single-lead ECG during sleep shows potential for detecting major depressive disorder in patients with obstructive sleep apnea.
Objective: A large number of people with obstructive sleep apnea (OSA) also suffer from major depressive disorder (MDD), leading to underdiagnosis due to overlapping symptoms. Polysomnography has been considered to identify MDD. However, limited access to sleep clinics makes this challenging. In this study, we propose a model to detect MDD in people with OSA using an electrocardiogram (ECG) during sleep. Methods: The single-lead ECG data of 32 people with OSA (OSAD-) and 23 with OSA and MDD (OSAD+) were investigated. The first 60 min of their recordings after sleep were segmented into 30-s segments and 13 parameters were extracted: PR, QT, ST, QRS, PP, and RR; mean heart rate; two time-domain HRV parameters: SDNN, RMSSD; and four frequency heart rate variability parameters: LFₚower, HFₚower, total power, and the ratio of LFₚower/HFₚower. The mean and standard deviation of these parameters were the input to a support vector machine which was trained to separate OSAD- and OSAD+. Results: The proposed model distinguished between OSAD+ and OSAD- groups with an accuracy of 78. 18%, a sensitivity of 73. 91%, a specificity of 81. 25%, and a precision of 73. 91%. Conclusion: This study shows the potential of using only ECG for detecting depression in OSA patients.
Shaw et al. (2024) conducted an observational in Obstructive sleep apnea and major depressive disorder (n=55). Single-lead ECG recording during sleep with support vector machine model was evaluated on Detection of major depressive disorder (accuracy). A support vector machine model using single-lead ECG during sleep detected major depressive disorder in patients with obstructive sleep apnea with 78.18% accuracy and 81.25% specificity.