Electroencephalogram signals processing for topographic brain mapping and epilepsies classification

Arab, Mohammad Reza; Suratgar, Amir Abolfazl; Ashtiani, Alireza Rezaei

doi:10.1016/j.compbiomed.2010.06.001

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…Sleep fragmentation and frequent awakening in OSA patients lead to daytime sleepiness, reduced working and learning efficiency, cognitive dysfunction, as well as increased risks of diseases such as hypertension, stroke, diabetes, and thus reduced overall quality of life (8). At present, several major problems remain unsolved regarding the pathophysiology as well as the clinical diagnosis and treatment of OSA: (1) clinicians face significant difficulties in evaluating the disease burden of OSA due to its highly heterogeneous clinical manifestations (9), with symptoms ranging from morning fatigue to memory impairment (see Table 1 for the lack of inter-group differences in reported symptoms); (2) Polysomnography is the gold standard for OSA diagnosis (10,11), but the current measures of disease severity (AHI, ODI, the proportions of each sleep stage) do not strongly (15). The literature has much on the physiological significance of each frequency band during different sleep stages:…”

Section: Introductionmentioning

confidence: 99%

EEG Power Spectral Analysis of Abnormal Cortical Activations During REM/NREM Sleep in Obstructive Sleep Apnea

Liu

Shen

et al. 2021

Front. Neurol.

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Objective: To characterize electroencephalogram (EEG) power in different frequency bands during rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with obstructive sleep apnea (OSA).Methods: Retrospective data on 151 patients were collected and divided into three groups: primary snoring group (AHI < 5/h), mild-moderate OSA group (6 ≤ AHI < 30/h), and severe OSA group (AHI ≥ 30/h). EEG recordings in the frontal, central, and occipital regions were extracted from both REM and NREM sleep, to compute the normalized spectral power densities in the delta, theta, alpha, sigma, beta, and gamma frequency bands, using Fast Fourier Transform. Correlations between the computed EEG power and PSG parameters were analyzed.Results: In NREM sleep, elevated normalized power spectral density (PSD) in the delta band was observed in the severe OSA group compared to the other two groups. In contrast, the PSD of the other frequency bands showed a corresponding decrease in the severe OSA group. In REM sleep, similar changes were observed in the frontal region. Delta band PSD was positively correlated with Apnea Hypopnea Index (AHI) (r = 0.33), longest time of apnea, oxygen desaturation index (ODI) (r = 0.34), percent sleep time below 90% SaO2 (T90%) (r = 0.30), Arousal Index (ArI) (r = 0.29), and negatively correlated with N3%, minimum oxygen saturation (minSaO2).Conclusion: Our findings provide neurophysiological evidence for pathological cortical activation during REM/NREM sleep, which may be associated with the arousals and cognitive impairments in OSA. The technique of power spectral analysis could prove a potentially useful tool in complementing traditional PSG parameters in assessing disease burden to guide therapeutic decisions.

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Section: Introductionmentioning

confidence: 99%

EEG Power Spectral Analysis of Abnormal Cortical Activations During REM/NREM Sleep in Obstructive Sleep Apnea

Liu

Shen

et al. 2021

Front. Neurol.

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“…One other group has also tried to detect seizures based on a combination of accelerometers and sEMG [7]. Other authors have used electroencephalography (EEG) [8], [9], [10], electrocardiography (ECG) [11] or accelerometers [12], [13], [14], [15], [16] to develop a seizure detection system for tonic-clonic seizures. One group have even tried to discriminate tonicclonic seizures from other seizures based on accelerometers [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate

Conradsen

Beniczky

Hoppe

et al. 2012

IEEE Trans. Biomed. Eng.

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Patients are not able to call for help during a generalized tonic-clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic-clonic seizures, based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizures were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of ±50 μV . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100% with a mean detection latency of 13.7 s, while the rate of false detection was limited to 1 false alarm per 24 h. The overall performance of the presented generic algorithm is adequate for clinical implementation.

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“…A few papers recently published have reported on the effectiveness of WT applied to the EEG signal for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective (Adeli et al, 2003;Asaduzzaman et al, 2010;Guo et al, 2009;Lessa, 2011), but there are still some problems with classical EEG analysis and classification (Arab et al, 2010;Bauer et al, 2008;Oehler et al, 2009). It is important to emphasize the algorithm for classification of EEG signals based on WT and Patterns Recognize Techniques.…”

Section: Introductionmentioning

confidence: 99%

Energy Distribution of EEG Signal Components by Wavelet Transform

Omerhodžić¹,

Avdaković²,

Nuhanovic³

et al. 2012

Wavelet Transforms and Their Recent Applications in Biology and Geoscience

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Electroencephalogram signals processing for topographic brain mapping and epilepsies classification

Cited by 12 publications

References 8 publications

EEG Power Spectral Analysis of Abnormal Cortical Activations During REM/NREM Sleep in Obstructive Sleep Apnea

EEG Power Spectral Analysis of Abnormal Cortical Activations During REM/NREM Sleep in Obstructive Sleep Apnea

Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate

Energy Distribution of EEG Signal Components by Wavelet Transform

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