EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness

Thul, Alexander; Lechinger, Julia; Donis, Johann; Michitsch, Gabriele; Pichler, Gerhard; Kochs, Eberhard; Jordan, Denis; Ilg, Rüdiger; Schabus, Manuel

doi:10.1016/j.clinph.2015.07.039

Cited by 102 publications

(75 citation statements)

References 35 publications

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“…Our results add to these findings by indicating that the reactivity of these EEG frequencies to a circadian Zeitgeber, namely light, is also different across clinical entities. Similarly, earlier findings of lower signal complexity in UWS compared to MCS patients 8,22,26,27 were here extended by the observation that UWS patients do not even exhibit robust signal complexity changes across such extreme shifts as day and night. Diurnal changes in entropy across day and night are to be expected as daytime usually goes in hand with increased awareness and sensory processing.…”

Section: Discussionsupporting

confidence: 80%

Night and day variations of sleep in patients with disorders of consciousness

Wisłowska

Giudice

Lechinger

et al. 2017

Sci Rep

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Brain injuries substantially change the entire landscape of oscillatory dynamics and render detection of typical sleep patterns difficult. Yet, sleep is characterized not only by specific EEG waveforms, but also by its circadian organization. In the present study we investigated whether brain dynamics of patients with disorders of consciousness systematically change between day and night. We recorded ~24 h EEG at the bedside of 18 patients diagnosed to be vigilant but unaware (Unresponsive Wakefulness Syndrome) and 17 patients revealing signs of fluctuating consciousness (Minimally Conscious State). The day-to-night changes in (i) spectral power, (ii) sleep-specific oscillatory patterns and (iii) signal complexity were analyzed and compared to 26 healthy control subjects. Surprisingly, the prevalence of sleep spindles and slow waves did not systematically vary between day and night in patients, whereas day-night changes in EEG power spectra and signal complexity were revealed in minimally conscious but not unaware patients.

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Section: Discussionsupporting

confidence: 80%

Night and day variations of sleep in patients with disorders of consciousness

Wisłowska

Giudice

Lechinger

et al. 2017

Sci Rep

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“…Within nonlinear symbolic analysis, PEn is a very popular index that has been recently used to characterize the EEG recording from a broad variety of mental problems, including Alzheimer's disease [58], Parkinson's disease [59], epileptic seizure [60], depth of anesthesia [55], consciousness disorders [61] and obsessive compulsive disorders [62], among others. However, to the best of our knowledge, no thorough study has previously considered this entropy for automatic recognition of emotions.…”

Section: Discussionmentioning

confidence: 99%

Symbolic Analysis of Brain Dynamics Detects Negative Stress

García-Martínez

Martínez-Rodrigo

Zangróniz

et al. 2017

Entropy

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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.

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“…Our goal is to devise a strategy that can offer an EEG-based assessment of brain responsiveness in resting state, without the need for extrinsic stimulation. Although there have been previous attempts to reach this goal (Bai et al, 2017), these attempts have been mostly based on information-theoretic (Pollonini et al, 2010; Sarà & Pistoia, 2010; Gosseries et al, 2011; King et al, 2013; Marinazzo et al, 2014; Thul et al, 2016) or graph-theoretic (Lehembre et al, 2012; Leon-Carrion et al, 2012; Chennu et al, 2014; Höller et al, 2014; Varotto et al, 2014; Chennu et al, 2017; Numan et al, 2017) metrics, which do not provide direct information about the underlying neuronal dynamics. Furthermore, the majority of these studies focus on distinguishing minimally conscious patients from patients in vegetative state.…”

Section: Introductionmentioning

confidence: 99%