ArticlePhysiological measurement2017
Heart rate-based window segmentation improves accuracy of classifying posttraumatic stress disorder using heart rate variability measures.
Article in Physiological measurement, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Predicting longitudinal post-traumatic stress disorder symptoms using psychological measures and heart rate variability: a machine-learning approach.European journal of psychotraumatology · 2026Article
- Developing personalized algorithms for sensing mental health symptoms in daily life.Npj mental health research · 2025Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Association of Autonomic Activation with traumatic reminder challenges in posttraumatic stress disorder: A co-twin control study.Psychophysiology · 2023Article
- Heart Rate Variability in Psychiatric Disorders: A Systematic Review.Neuropsychiatric disease and treatment · 2023Review
- Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.JMIR research protocols · 2022Article
- A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation.Biomedical engineering letters · 2022Article
- Transcutaneous Cervical Vagal Nerve Stimulation in Patients with Posttraumatic Stress Disorder (PTSD): A Pilot Study of Effects on PTSD Symptoms and Interleukin-6 Response to Stress.Journal of affective disorders reports · 2021Article
- Classification and Prediction of Post-Trauma Outcomes Related to PTSD Using Circadian Rhythm Changes Measured via Wrist-Worn Research Watch in a Large Longitudinal Cohort.IEEE journal of biomedical and health informatics · 2021Article
- Transcutaneous vagal nerve stimulation blocks stress-induced activation of Interleukin-6 and interferon-γ in posttraumatic stress disorder: A double-blind, randomized, sham-controlled trial.Brain, behavior, & immunity - health · 2020Article
- A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses.Physiological measurement · 2018Review
- Transforming Psychiatry into Data-Driven Medicine with Digital Measurement Tools.NPJ digital medicine · 2018Review
- Continuous assessment of schizophrenia using heart rate and accelerometer data.Physiological measurement · 2017Article
- Building the Thermometer for Mental Health.Cerebrum : the Dana forum on brain scienceArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveHeart rate variability (HRV) characterizes changes in autonomic nervous system function and varies with posttraumatic stress disorder (PTSD). In this study we developed a classifier based on heart rate (HR) and HRV measures, and improved classifier performance using a novel HR-based window segmentation. APPROACH: Single-channel ECG data were collected from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD over 24 h. RR intervals were derived from these data, cleaned, and used to calculate HR and HRV metrics. These metrics were used as features in a logistic regression classifier. Performance was assessed via repeated random sub-sampling validation. To reduce noise and activity-related effects, we calculated features from five non-overlapping ten-minute quiescent segments of RR intervals defined by lowest HR, as well as random ten-minute segments as a control. MAIN
resultsUsing a combination of the four most predictive features derived from quiescent segments we achieved a median area under the receiver operating curve (AUC) of 0.86 on out-of-sample test set data. This was significantly higher than the AUC using 24 h of data (0.72) or random segments (0.67). SIGNIFICANCE: These results demonstrate our segmentation approach improves the classification of PTSD from HR and HRV measures, and suggest the potential for tracking PTSD illness severity via objective physiological monitoring. Future studies should prospectively evaluate if classifier output changes significantly with worsening or effective treatment of PTSD.
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