Lower Heart Rate Variability Associated With Military Sexual Trauma Rape and Posttraumatic Stress Disorder

2012 ◽  
Vol 14 (4) ◽  
pp. 412-418 ◽  
Author(s):  
Elizabeth Ann Davis Lee ◽  
Sue A. Theus
Keyword(s):  
Heart Rate ◽  
Posttraumatic Stress Disorder ◽  
Heart Rate Variability ◽  
Stress Disorder ◽  
Normal Sinus Rhythm ◽  
Military Sexual Trauma ◽  
Female Veterans ◽  
Normal Sinus ◽  
Cardiac Symptoms ◽  
The Mean

Low heart rate variability (HRV) can occur with psychological disorders such as posttraumatic stress disorder (PTSD). The purpose of this study was to examine the association between PTSD by trauma type and decreased HRV measures in female veterans with cardiac symptoms. This secondary analysis utilized data from a previous study of female veterans ( n = 125) examined for cardiac symptoms by Holter and electrocardiogram recordings at a Veterans Affairs medical center. The mean HRV measure from three 10-s data segments with spontaneous respirations was obtained for each subject. PTSD diagnosis and type of trauma exposure were collected from mental health consult notes. Chi-square was used for frequency of subject characteristics; independent t tests and one-way analysis of variance (ANOVA) compared means of HRV measures between trauma types. Statistical significance was set at p < .05 a priori. By ANOVA, significantly lower log-transformed standard deviation of all normal sinus rhythm R-R intervals (SDNN) and log-transformed square root of the mean of the sum of the squares of differences between adjacent normal sinus rhythm R-R intervals (RMSSD) were found in the PTSD group with documented rape military sexual trauma (MST) compared to other groups including no PTSD, PTSD following MST with rape not specified, combat exposure, and nonmilitary-related trauma; lower HRV measures were not found with other PTSD types of trauma. This study suggests rape MST with concomitant PTSD may be a risk factor for decreased HRV in female veterans examined for cardiac symptoms.

scholarly journals Detection of coronary artery disease by reduced features and extreme learning machine

2018 ◽  
Vol 91 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Hidden Node ◽  
Artery Disease ◽  
Learning Machine

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from  a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

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

2017 ◽  
Vol 38 (6) ◽  
pp. 1061-1076 ◽  
Author(s):  
Erik Reinertsen ◽  
Shamim Nemati ◽  
Adriana N Vest ◽  
Viola Vaccarino ◽  
Rachel Lampert ◽  
...  
Keyword(s):  
Heart Rate ◽  
Posttraumatic Stress Disorder ◽  
Heart Rate Variability ◽  
Posttraumatic Stress ◽  
Stress Disorder

Effect of Symptom Over-Reporting on Heart Rate Variability in Veterans With Posttraumatic Stress Disorder

2015 ◽  
Vol 16 (5) ◽  
pp. 551-562 ◽  
Author(s):  
Robert E. Brady ◽  
Joseph I. Constans ◽  
Brian P. Marx ◽  
James L. Spira ◽  
Richard Gevirtz ◽  
...  
Keyword(s):  
Heart Rate ◽  
Posttraumatic Stress Disorder ◽  
Heart Rate Variability ◽  
Posttraumatic Stress ◽  
Stress Disorder

scholarly journals Circadian Contrasts in Heart Rate Variability Associated With Posttraumatic Stress Disorder Symptoms in a Young Adult Cohort

2016 ◽  
Vol 29 (5) ◽  
pp. 415-421 ◽  
Author(s):  
Michelle B. Rissling ◽  
Paul A. Dennis ◽  
Lana L. Watkins ◽  
Patrick S. Calhoun ◽  
Michelle F. Dennis ◽  
...  
Keyword(s):  
Heart Rate ◽  
Posttraumatic Stress Disorder ◽  
Heart Rate Variability ◽  
Young Adult ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Posttraumatic Stress Disorder Symptoms

Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure

2016 ◽  
Author(s):  
Syed Hassan Zaidi ◽  
Imran Akhtar ◽  
Syed Imran Majeed ◽  
Tahir Zaidi ◽  
Muhammad Saif Ullah Khalid
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Time Series Data ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Normal Sinus

This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.

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