Predicting depressed patients with suicidal ideation from ECG recordings

Ahsan H. Khandoker, V. Luthra, Y. Abouallaban, S. Saha, K. I. Ahmed, R. Mostafa, N Chowdhury, H. F. Jelinek

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)793-805
Number of pages13
JournalMedical and Biological Engineering and Computing
Volume55
Issue number5
DOIs
Publication statusPublished - 2017

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    Khandoker, A. H., Luthra, V., Abouallaban, Y., Saha, S., Ahmed, K. I., Mostafa, R., Chowdhury, N., & Jelinek, H. F. (2017). Predicting depressed patients with suicidal ideation from ECG recordings. Medical and Biological Engineering and Computing, 55(5), 793-805. https://doi.org/10.1007/s11517-016-1557-y