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Predicting depressed patients with suicidal ideation from ECG recordings

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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.

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Acknowledgements

This work was partially supported by a Khalifa University Internal Fund (KUIRF) awarded to Ahsan Khandoker. The authors like to thank Ms Ola Ali and other psychiatric nurses of American Center for Psychiatry and Neurology for Patients' recruitment and experimental data collection from MDD patients.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Khalifa University, PO Box 127788, Abu Dhabi, UAE

    A. H. Khandoker & N. Chowdhury

  2. Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC, 3010, Australia

    A. H. Khandoker

  3. American Center for Psychiatry and Neurology, Abu Dhabi, UAE

    V. Luthra & Y. Abouallaban

  4. United International University, Dhaka, Bangladesh

    S. Saha, K. I. Ahmed & R. Mostafa

  5. School of Community Health and Centre for Research in Complex Systems, Charles Sturt University, Albury, NSW, Australia

    H. F. Jelinek

  6. Australian School of Advanced Medicine, Macquarie University, Sydney, NSW, Australia

    H. F. Jelinek

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  1. A. H. Khandoker
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  2. V. Luthra
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  3. Y. Abouallaban
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  4. S. Saha
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  5. K. I. Ahmed
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  8. H. F. Jelinek
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Correspondence to A. H. Khandoker.

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Khandoker, A.H., Luthra, V., Abouallaban, Y. et al. Predicting depressed patients with suicidal ideation from ECG recordings. Med Biol Eng Comput 55, 793–805 (2017). https://doi.org/10.1007/s11517-016-1557-y

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  • DOI: https://doi.org/10.1007/s11517-016-1557-y

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