Electroencephalographic comparison of veterans with combat-related post-traumatic stress disorder and healthy subjects

Begić, Dražen; Hotujac, Ljubomir; Jokić‐Begić, Nataša

doi:10.1016/s0167-8760(00)00153-7

Cited by 86 publications

(78 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Obviously, considering stress as a reaction to preserve organism's integrity, a concentration increase has mandatorily to occur during this emotion [34,63]. In addition, other previous works have also suggested a similar neural activation of most brain regions in subjects exposed to very distressfull [64] as well as chronic hyperarousal and post-traumatic experiences [65]. Similarly, a low and regular neuronal activity during an emotional state of calm has also been previously reported by works dealing with different relaxing therapies.…”

Section: Discussionsupporting

confidence: 57%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

View full text Add to dashboard Cite

Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain's behavior under this negative emotion.

show abstract

Section: Discussionsupporting

confidence: 57%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

View full text Add to dashboard Cite

show abstract

“…Indeed, according to AAPEn, CD has elucidated a more complex activity from most of the brain regions for calm than for distress [37,63]. Contrarily, in line with QSEn, an increased spectral power in some EEG channels has been noticed for patients suffering from stressful situations, post-traumatic stress disorders and chronic hyperarousal [64]. Other nonlinear indices, like Lyapunov exponents and SEn, have also reported a complexity reduction in many brain time series for stressed subjects undergoing relaxing treatments based on meditation, restful music or foot reflexology [65,66].…”

Section: Discussionmentioning

confidence: 97%

Symbolic Analysis of Brain Dynamics Detects Negative Stress

García-Martínez

Martínez-Rodrigo

Zangróniz

et al. 2017

Entropy

View full text Add to dashboard Cite

Abstract:The electroencephalogram (EEG) is the most common tool used to study mental disorders. In the last years, the use of this recording for recognition of negative stress has been receiving growing attention. However, precise identification of this emotional state is still an interesting unsolved challenge. Nowadays, stress presents a high prevalence in developed countries and, moreover, its chronic condition often leads to concomitant physical and mental health problems. Recently, a measure of time series irregularity, such as quadratic sample entropy (QSEn), has been suggested as a promising single index for discerning between emotions of calm and stress. Unfortunately, this index only considers repetitiveness of similar patterns and, hence, it is unable to quantify successfully dynamics associated with the data temporal structure. With the aim of extending QSEn ability for identification of stress from the EEG signal, permutation entropy (PEn) and its modification to be amplitude-aware (AAPEn) have been analyzed in the present work. These metrics assess repetitiveness of ordinal patterns, thus considering causal information within each one of them and obtaining improved estimates of predictability. Results have shown that PEn and AAPEn present a discriminant power between emotional states of calm and stress similar to QSEn, i.e., around 65%. Additionally, they have also revealed complementary dynamics to those quantified by QSEn, thus suggesting a synchronized behavior between frontal and parietal counterparts from both hemispheres of the brain. More precisely, increased stress levels have resulted in activation of the left frontal and right parietal regions and, simultaneously, in relaxing of the right frontal and left parietal areas. Taking advantage of this brain behavior, a discriminant model only based on AAPEn and QSEn computed from the EEG channels P3 and P4 has reached a diagnostic accuracy greater than 80%, which improves slightly the current state of the art. Moreover, because this classification system is notably easier than others previously proposed, it could be used for continuous monitoring of negative stress, as well as for its regulation towards more positive moods in controlled environments.

show abstract

“…As there were no differences between the original 16 and the presently added 13 schizophrenic patients in the outcome .measures, _t~T -_' were pooled in the present analysis. Harris et al, 1999Harris et al, , 2001Koshino et al, 1993), dementia (Buchan et al, 1997, Dierks et al, 2000Berendse et al, 2000;Franciotti et al, 2002), and even posttraumatic stress disorder (PTSD;Teicher et al, 2002;Begic et al, 2001). …”

mentioning

confidence: 99%

Source distribution of neuromagnetic slow wave activity in schizophrenic and depressive patients

Wienbruch

Moratti

Elbert

et al. 2003

Clinical Neurophysiology

View full text Add to dashboard Cite

Electroencephalographic comparison of veterans with combat-related post-traumatic stress disorder and healthy subjects

Cited by 86 publications

References 27 publications

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

Symbolic Analysis of Brain Dynamics Detects Negative Stress

Source distribution of neuromagnetic slow wave activity in schizophrenic and depressive patients

Contact Info

Product

Resources

About