Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: A supervised machine learning approach

McDonald, Anthony D.; Sasangohar, Farzan; Jatav, Ashish; Rao, Arjun H.

doi:10.1080/24725579.2019.1583703

Cited by 38 publications

(31 citation statements)

References 33 publications

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“…Machine learning studies evaluating HR have primarily focused on energy expenditure [ 87 , 88 ]. An exception is McDonald et al [ 11 ] who evaluated several machine learning algorithms—neural networks, decision trees, support vector machines, convolutional neural networks, and random forests—to predict the onset of PTSD symptoms in the veteran population. This study used HR data with a 1 Hz frequency (1 observation per second) as the input of these algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In the statistical analysis of data in the PTSD domain, fit assessments have been used to show the efficiency of the results. For instance, McDonald et al [ 11 ] used ROC curves along with accuracy to show that random forest works better than other machine learning methods to predict hyperarousal moments in people with PTSD. Shalev et al [ 125 ] used sensitivity and specificity to predict the development of PTSD based on their instant responses to trauma.…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation of this review is that although the identified models have been applied across various domains (eg, energy expenditure and general stress prediction), to our knowledge, only 2 papers [ 11 , 57 ] directly applied these methods to data from patients diagnosed with PTSD. In particular, only 1 study [ 11 ] used a predictive approach in the PTSD domain. Other studies were primarily limited to linear descriptive statistics such as the t test or ANOVA [60,65–67].…”

Section: Discussionmentioning

confidence: 99%

“…mHealth apps deployed on wearable devices (eg, smartwatches) that are equipped with an array of physiological sensors (eg, heart rate [HR]) may also enable continuous remote monitoring of signs and symptoms of PTSD. Indeed, recent efforts have shown promising applications of watch-based HR sensors to detect the onset of PTSD hyperarousal events [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

2020

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Background Posttraumatic stress disorder (PTSD) is a prevalent psychiatric condition that is associated with symptoms such as hyperarousal and overreactions. Treatments for PTSD are limited to medications and in-session therapies. Assessing the way the heart responds to PTSD has shown promise in detecting and understanding the onset of symptoms. Objective This study aimed to extract statistical and mathematical approaches that researchers can use to analyze heart rate (HR) data to understand PTSD. Methods A scoping literature review was conducted to extract HR models. A total of 5 databases including Medical Literature Analysis and Retrieval System Online (Medline) OVID, Medline EBSCO, Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO, Excerpta Medica Database (Embase) Ovid, and Google Scholar were searched. Non–English language studies, as well as studies that did not analyze human data, were excluded. A total of 54 studies that met the inclusion criteria were included in this review. Results We identified 4 categories of models: descriptive time-independent output, descriptive and time-dependent output, predictive and time-independent output, and predictive and time-dependent output. Descriptive and time-independent output models include analysis of variance and first-order exponential; the descriptive time-dependent output model includes a classical time series analysis and mixed regression. Predictive time-independent output models include machine learning methods and analysis of the HR-based fluctuation-dissipation method. Finally, predictive time-dependent output models include the time-variant method and nonlinear dynamic modeling. Conclusions All of the identified modeling categories have relevance in PTSD, although the modeling selection is dependent on the specific goals of the study. Descriptive models are well-founded for the inference of PTSD. However, there is a need for additional studies in this area that explore a broader set of predictive models and other factors (eg, activity level) that have not been analyzed with descriptive models.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

2020

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“…Despite anecdotal evidence suggesting that these features can reduce maladaptive behaviors and improve the quality of life (Ulrich et al, 2018), veteran-centered approaches to elicit and document such contributors are largely absent. To address this gap, our recent efforts have used data-driven approaches to not only better understand the contexts and triggers associated with PTSD but also to propose potential technology-based interventions to support veterans (Rodriguez-Paras et al, 2017; Khanade et al, 2018; McDonald et al, 2019). As part of this broader effort, we also investigated architectural and space design considerations.…”

mentioning

confidence: 99%

Veteran-Centered Investigation of Architectural and Space Design Considerations for Post-Traumatic Stress Disorder (PTSD)

Nuamah

Rodriguez-Paras

Sasangohar

2020

HERD

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Aim: This study sought to investigate architectural and space design considerations for veterans with post-traumatic stress disorder (PTSD). Background: Anecdotal evidence suggests that urban design features could have a positive impact on the mental well-being of individuals suffering from PTSD. However, evidence-based architectural and space design guidelines for PTSD are largely absent. Methods: Semi-structured interviews were conducted with 17 veterans diagnosed with PTSD to gain insights into their personal experiences with physical indoor and outdoor spaces, and to inquire about their needs and expectations for future architectural design. Transcripts were analyzed thematically. Results: Architectural design features including windows, entrances and exits, walkways and hallways, open space, defensible space, and green space; interior design features including furnishings and color; and ambient features including light, air quality, and noise levels were identified as most influential design features. Conclusions: Our results underscore the first important step to developing comprehensive architectural and space design guidelines for veterans with PTSD. Work is in progress to solicit more feedback from veterans.

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Smart Healthcare: Rough Set Theory in Predicting Heart Disease

Singh

Misra²,

Kumar³

2022

Advances in Computing, Informatics, Networking and Cybersecurity

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Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: A supervised machine learning approach

Cited by 38 publications

References 33 publications

Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

Veteran-Centered Investigation of Architectural and Space Design Considerations for Post-Traumatic Stress Disorder (PTSD)

Smart Healthcare: Rough Set Theory in Predicting Heart Disease

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