Heart rate-based window segmentation improves accuracy of classifying posttraumatic stress disorder using heart rate variability measures

Abstract

Objective Heart 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 Results Using 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.

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

“…Reinertsen et al used a machine learning approach to dichotomize subjects with PTSD from healthy controls using features such as LF power, statistical moments, and acceleration and deceleration capacity (Reinertsen et al 2017a). 24-hour single-channel ECG recordings were obtained from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD.…”

Implantable and Wearable Neuroengineering Education: A Review of Postgraduate Programmes

“…Wearable devices measuring heart rate variability can be used to classify post-traumatic stress disorders [28], subclinical depression [29], stress [30] and, in combination with accelerometer, to identify subjects with schizophrenia [31]. Wearable devices and sensors monitoring vital parameters and physical activity, and detecting falls could help avoiding emergency situations and be used in healthcare for the aging population [32], [33].…”

Variabilidade da Frequência Cardíaca como biomarcador do estresse: revisão integrativa

“…Também foram estudados o estresse metal e a aritmética mental (Sánchez-Hechavarría, et al, 2019;Zanetti, et al, 2019), o protocolo de estresse: linha de base, teste Go/NoGo, recuperação, posição supina e ortostase (Mestanikova, et al, 2019) e o Teste de Simulação de Falar em Público (TSFP) (Rimes, et al, 2017). O estresse pós-traumático foi identificado por meio de avaliação clínica (Reinertsen, et al, 2017).…”

“…A VFC foi registrada usando um aparelho de eletrocardiograma (ECG) (Borchini, et al, 2015;Grantcharov, et al, 2018;Javorka, et al, 2017;Markov, et al, 2016;Mestanikova, et al, 2019;Peçanha, et al, 2017a;Peçanha, et al, 2017b;Reinertsen, et al, 2017;Sánchez-Hechavarría, et al, 2019;Tonhajzerova, et al, 2016;Visnovcova, et al, 2015;Zanetti, et al, 2019). A duração da gravação, nesses estudos, variou duas sessões de monitoramento de ECG de 24 horas.…”