Feature extraction and classification of EEG for automatic seizure detection

Rafiuddin, Nidal; Khan, Yusuf Uzzaman; Farooq, Omar

doi:10.1109/mspct.2011.6150470

Cited by 63 publications

(24 citation statements)

References 8 publications

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“…The amplitude and percentile features provided important descriptors of the time domain of each input, while the percentiles also deliver a more representative measure of statistical dispersion (Rafiuddin et al, 2011). The spectral density and bandwidth features provided important descriptors relating to central mass and the frequency domain (obtained via fast Fourier transformation).…”

Section: Data Processingmentioning

confidence: 99%

Classification of team sport activities using a single wearable tracking device

Wundersitz

Josman

Gupta

et al. 2015

Journal of Biomechanics

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Section: Data Processingmentioning

confidence: 99%

Classification of team sport activities using a single wearable tracking device

Wundersitz

Josman

Gupta

et al. 2015

Journal of Biomechanics

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“…They developed a method which includes statistical features such as the interquartile range (IQR) and the mean absolute deviation (MAD). The method was evaluated on the same database used in our work and the results yielded an average accuracy of 96.5% [16]. Nasehi and Pourghassem, obtained a SEN = 98%, training a neural network for each patient [17].…”

Section: Discussionmentioning

confidence: 99%

Adaptive Filtering for Epileptic Event Detection in the EEG

et al. 2019

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Purpose The development of online seizure detection techniques as well as prediction methods are very critical. Patient quality of life could improve significantly if the beginning of a seizure could be predicted or detected early. Methods This paper proposes a method to automatically detect epileptic seizures based on adaptive filters and signal averaging. The process was applied to 425 h of epileptic EEG records from CHB-MIT EEG database. The developed algorithm does not require any training since it is simple and involves low processing time. Therefore, it can be implemented in real time as well as offline. Results Three thresholds were evaluated and calculated as 10, 20 and 30 times the median value of ST(n). The threshold of 20 showed the best relation between SEN and SPE. In this case, these indexes reached average values, across all the patients, of 90.3% and 73.7% respectively. Conclusions The proposed method has several strengths, for example: that no training is required due to the automatic adaptation to the threshold to each new EEG record. The algorithm could be implemented in real time. It is simple owing to its low processing time which makes it suitable for the analysis of long-term records and a large number of channels. The system could be implemented on electronic devices for warning purposes (of the seizure onset). It employs methods to process signals that were not used with epileptic seizure detection in EEG, such as in the case of adaptive predictive filters.

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“…The fourth Daubechies mother wavelet function (db4) is a widely used mother wavelet function in the field of EEG analysis [10,35,41]. The morphological characteristic of this mother wavelet function resembles EEG signals.…”

Section: Eeg Noise Removal Andmentioning

confidence: 99%

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

et al. 2020

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Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

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Feature extraction and classification of EEG for automatic seizure detection

Cited by 63 publications

References 8 publications

Classification of team sport activities using a single wearable tracking device

Classification of team sport activities using a single wearable tracking device

Adaptive Filtering for Epileptic Event Detection in the EEG

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

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