Predictive value of ECG derived heart rate variability measures for the occurrence of depression and suicide
Approved Research ID: 79510
Approval date: November 30th 2021
The project "Predictive value of electrocardiogram (ECG) derived heart rate variability measures for the occurrence of depression and suicide" aims at the identification of electrophysiological markers of the autonomic nervous system (ANS). These surrogat markers for the autonomic nervous system function are associated with psychiatric disorders such as major depression and severe symptoms such as suicidal ideations and suicide attempts. The project's main aim is to clarify the association between HRV markers at baseline and current or later occurring symptoms of major depression or suicide. It is hypothesiszed that lower parasympathetic activity and increased sympathetic activity during rest have a predictive value for depressive conditions or suicides. Besides classical statistical analysis of the HRV parameters using regression models, additionally machine learning and deep learning models will be applied to increasethe accuracy of the predictions.
It is intended to retrieve all data, preprocess all data, calculate HRV parameters and do the statistical analysis before publishing the results within 24-36 months. The results may help to identify persons at risk for depression or suicide and may facilitate options for early interventions or even preventive activities.