Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Yi, Guosheng; Wang, Jiang; Deng, Bin; Wei, Xile

doi:10.1007/s11571-016-9415-z

Cited by 49 publications

(30 citation statements)

References 67 publications

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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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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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“…The combinations of nonlinear and linear features provided better class separability (Balli and Palaniappan 2010). The resting-state EEG dynamics of the early stage of Parkinson's disease (PD) revealed lower permutation entropy (PE) and higher order index (OI) for patients than controls (Yi et al 2017). Nonlinear measures extracted from EEG signals under emotional processing of PD patients with respect to motor symptom asymmetry, suggests deficiency of emotional communication in patients with right hemisphere dysfunction (Yuvaraj and Murugappan 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of long range dependence in the EEG signals of Alzheimer patients

2018

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Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

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“…It measures the electrical potential detected by each electrode. EEG can be used in monitoring the brain during anesthesia [ 28 ], surgical procedures [ 29 ], and investigations of brain disorders (psychoses [ 30 ], meningoencephalitis [ 31 ], Parkinson [ 9 ], Alzheimer [ 32 , 33 , 34 , 35 , 36 , 37 ], dementia [ 38 ], epilepsy [ 39 , 40 , 41 , 42 ], central motor neuron syndrome [ 43 ], cerebral palsy [ 44 , 45 , 46 ], and muscular dystrophy [ 47 ]). Mainly, EEG systems are used to diagnose and monitor patients with neuropathology, especially in diagnosis of epilepsy and in studying the seizures, as well as the monitoring of treatment and evolution.…”

Section: Introductionmentioning

confidence: 99%

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

Aileni

Pasca

Florescu

2020

Sensors

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Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.

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Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Cited by 49 publications

References 67 publications

Symbolic Analysis of Brain Dynamics Detects Negative Stress

Symbolic Analysis of Brain Dynamics Detects Negative Stress

Analysis of long range dependence in the EEG signals of Alzheimer patients

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

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