Screening major depressive disorder in patients with obstructive sleep apnea using single-lead ECG recording during sleep.
| Title: | Screening major depressive disorder in patients with obstructive sleep apnea using single-lead ECG recording during sleep. |
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| Authors: | Shaw V; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.; CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, India.; School of Engineering, STEM College, RMIT University, Melbourne, VIC, Australia.; Ngo QC; School of Engineering, STEM College, RMIT University, Melbourne, VIC, Australia.; Pah ND; School of Engineering, STEM College, RMIT University, Melbourne, VIC, Australia.; Oliveira G; School of Engineering, STEM College, RMIT University, Melbourne, VIC, Australia.; Khandoker AH; Department of Biomedical Engineering and Biotechnology, Khalifa University, Abu Dhabi, UAE.; Mahapatra PK; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.; CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, India.; Pankaj D; CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, India.; Kumar DK; School of Engineering, STEM College, RMIT University, Melbourne, VIC, Australia. |
| Source: | Health informatics journal [Health Informatics J] 2024 Oct-Dec; Vol. 30 (4), pp. 14604582241300012. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: SAGE Publications Country of Publication: England NLM ID: 100883604 Publication Model: Print Cited Medium: Internet ISSN: 1741-2811 (Electronic) Linking ISSN: 14604582 NLM ISO Abbreviation: Health Informatics J Subsets: MEDLINE |
| Imprint Name(s): | Publication: London : SAGE Publications; Original Publication: Sheffield, UK : Sheffield Academic Press, [1997- |
| MeSH Terms: | Major Depressive Disorder*/diagnosis ; Major Depressive Disorder*/complications ; Sleep Apnea, Obstructive*/diagnosis ; Sleep Apnea, Obstructive*/complications ; Sleep Apnea, Obstructive*/physiopathology ; Electrocardiography*/methods ; Electrocardiography*/instrumentation ; Polysomnography*/methods ; Polysomnography*/instrumentation; Mass Screening/methods ; Mass Screening/instrumentation ; Heart Rate/physiology ; Humans ; Male ; Female ; Middle Aged ; Adult |
| Abstract: | 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: LF_power, HF_power, total power, and the ratio of LF_power/HF_power. 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. |
| Competing Interests: | Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Contributed Indexing: | Keywords: Electrocardiogram; major depressive disorder; obstructive sleep apnea; screening; wearable devices |
| Entry Date(s): | Date Created: 20241121 Date Completed: 20241121 Latest Revision: 20260127 |
| Update Code: | 20260130 |
| DOI: | 10.1177/14604582241300012 |
| PMID: | 39569459 |
| Database: | MEDLINE |
Journal Article