PTSD Classification via ECG Quiescent Segments and RR Interval Features
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Solution Overview
Problem
Current methods for diagnosing and managing post-traumatic stress disorder (PTSD) lack objective standards, relying on subjective physician evaluations and patient self-reports, leading to unreliable diagnoses and treatments.
Innovation Solution
A system and method using electrocardiography (ECG) data to classify PTSD status by identifying quiescent segments in heart activity, extracting features such as RR intervals, and applying machine learning to determine a PTSD indicator, providing an objective assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subjective physician evaluation and patient self-reports are used for PTSD diagnosis, then the diagnostic process is simple and quick, but the reliability and objectivity of diagnosis deteriorates
Solution Approach 1:
The patent replaces the mechanical system of subjective human evaluation with an automated computational system that processes electrocardiography data. Machine learning algorithms analyze RR interval information and quiescent segment features to objectively determine PTSD status, substituting physician judgment with data-driven classification that eliminates subjective bias while maintaining diagnostic reliability.
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a mediator between raw physiological data and clinical diagnosis. The machine learning classifier processes ECG features and RR interval patterns, serving as an objective intermediary that translates physiological measurements into PTSD status determination, thereby improving diagnostic reliability without requiring complex clinical judgment.
2Measurement precision
If electrocardiography data analysis with machine learning is implemented, then the objectivity and accuracy of PTSD classification improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent extracts specific relevant features from complex electrocardiography data, focusing on RR interval information and quiescent segment characteristics. By isolating and analyzing only the most discriminative features rather than processing entire ECG waveforms, the system achieves high classification accuracy while reducing computational complexity and data processing requirements.
Solution Approach 2:
The patent segments the ECG data into quiescent segments for targeted analysis. By dividing the continuous ECG signal into specific temporal segments and analyzing features within these segments, the system improves measurement precision for PTSD classification while managing computational complexity through focused rather than exhaustive data processing.
3Reliability
If comprehensive ECG feature analysis is performed, then the diagnostic accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by analyzing only the most informative subset of ECG features rather than comprehensively processing all possible parameters. By focusing on RR interval information and quiescent segment features that provide sufficient discriminatory power for PTSD classification, the system achieves high diagnostic accuracy while minimizing processing time and computational resource requirements.
Data Source
AI summary
Systems, methods, and computer-readable media for classifying a PTSD status. In an embodiment, an example method for using a classifier can comprise receiving information from electrocardiography performed on an individual; determining features from the information; comparing the features to the a logistic regression classifier trained using features determined from median quiescent segments of RR interval information from individuals with and without PTSD, wherein the median quiescent segments are non-overlapping time periods of lowest median HR for each individual, and the features include one or more of the following: deceleration capacity (DC), low frequency (LF) power, very low frequency (VLF) power, and standard deviation of all normal RR intervals (SDNN); and determining a posttraumatic stress disorder (PTSD) status of the individual based on the comparison of the features to the classifier, wherein the PTSD status is a severity of PTSD based on a probability of PTSD.