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Insights
Arterial pulse signals can predict suicidal ideation in Major Depressive Disorder (MDD). Tone-Entropy features from arterial pulse wave analysis accurately identified individuals with MDD and suicidal ideation.
Area of Science:
- Neuroscience
- Cardiology
- Psychiatry
Background:
- Major Depressive Disorder (MDD) is linked to increased suicide risk.
- The neurophysiological basis of suicidal ideation (SI) in MDD is not fully understood.
- Existing research indicates a connection between MDD and cardiovascular disease.
Purpose of the Study:
- To identify predictive features from arterial pulse signals for suicidal ideation in MDD patients.
- To differentiate between MDD patients with and without suicidal ideation, and healthy controls.
- To explore the utility of Tone-Entropy (TE) features from pulse wave analysis.
Main Methods:
- Recruited 16 MDD patients with SI (MDDSI+), 16 MDD patients without SI (MDDSI-), and 29 healthy controls (CONT).
- Assessed depression and SI using standardized scales.
- Extracted 2D Tone-Entropy (TE) features from Systole, Diastole, and Pulse Wave Amplitude (PWA) time series of arterial pulse signals.
Main Results:
- TE features from the Diastole component of the arterial pulse were the most effective predictors of SI in MDD.
- Classification and Regression Tree (CART) models achieved high accuracies: 88.52% (Systole), 90.2% (Diastole), and 88.52% (PWA).
- Combining all TE features yielded a classification accuracy of 93.44% for distinguishing MDDSI+, MDDSI-, and CONT groups.
Conclusions:
- Arterial pulse wave analysis, particularly TE features from Diastole, shows significant potential for predicting suicidal ideation in MDD.
- This non-invasive method could aid in identifying at-risk individuals within the MDD population.
- Further research is warranted to validate these findings and explore clinical applications.
Abstract:
Major Depressive Disorder (MDD) is a serious mental disorder that if untreated not only affects physical health but also has a high risk of suicide. While the neurophysiological phenomena that contribute to the formation of Suicidal Ideation (SI) are still ill-defined, clear links between MDD and cardiovascular disease have been reported. The aim of this study is to extract suitable features from arterial pulse signals with a view to predicting SI within MDD and control groups. Sixteen unmedicated MDD patients with a history of SI (MDDSI+), sixteen without SI (MDDSI-) and twenty-nine healthy subjects (CONT) were recruited at a psychiatric clinic in the UAE. Depression severity and SI were assessed using the Hamilton Depression Rating Scale and Beck Depression Inventory. Pulse Wave Amplitude (PWA) was calculated as the difference between the peak (Systole) and the valley (Diastole) of the arterial pulse within each cardiac cycle. Then, 2D Tone-Entropy (TE) features were extracted from the Systole, Diastole and PWA time series. The TE features extracted from Diastole were the best markers for predicting MDDSI+. The overall classification accuracies of Classification and Regression Tree (CART) model by using TE features of Systole, Diastole and PWA were 88.52%, 90.2% and 88.52% respectively. When all TE features were combined, accuracy increased up to 93.44% in identifying MDDSI+/MDDSI-/Control groups.
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