Towards PTSD Diagnosis Through ECG Anomaly Detection based on Autoencoders

Vasileios Skaramagkas; Ioannis Kyprakis; Georgia Karanasiou; Dimitrios Fotiadis; Manolis 	Tsiknakis

doi:10.4108/eetpht.11.9463

Authors

Vasileios Skaramagkas Hellenic Mediterranean University
Ioannis Kyprakis Hellenic Mediterranean University
Georgia Karanasiou University of Ioannina
Dimitrios Fotiadis University of Ioannina
Manolis Tsiknakis Hellenic Mediterranean University

DOI:

https://doi.org/10.4108/eetpht.11.9463

Keywords:

PTSD Diagnosis, autoencoder, anomaly detection, ECG, deep learning in healthcare

Abstract

INTRODUCTION: Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition that can develop after exposure to traumatic events, often resulting in symptoms that severely impair daily functioning. Current diagnostic methods largely rely on subjective assessments, highlighting the need for objective, non-invasive tools to improve diagnostic precision.
OBJECTIVES: This study aims to develop and validate an innovative deep learning approach using autoencoder neural networks to detect PTSD through analysis of electrocardiography (ECG) signals. The goal is to provide a reliable and sophisticated diagnostic method that bridges computational and clinical domains.
METHODS: We employed autoencoder neural networks to analyze ECG data collected from wearable heart zone sensors. This unsupervised learning model was trained to detect subtle anomalies in the ECG signals that may serve as biomarkers for PTSD. The methodology was evaluated using data collected from individuals with and without PTSD symptoms.
RESULTS: The proposed model demonstrated strong potential as an objective diagnostic tool, successfully identifying patterns in ECG signals associated with PTSD. The analysis confirmed the model’s ability to distinguish PTSD-related anomalies with 83% accuracy.
CONCLUSION: This research introduces a novel, non-invasive diagnostic methodology for PTSD using deep learning and wearable ECG data. The findings support the model's value as a potential objective biomarker, contributing to more precise psychiatric diagnostics and expanding the role of machine learning in healthcare.

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References

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