Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis

Faust, Oliver; Acharya, U. Rajendra; Adeli, Hojjat; Adeli, Amir

doi:10.1016/j.seizure.2015.01.012

Cited by 487 publications

(197 citation statements)

References 107 publications

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“…Specifically, the fourth-order Daubechies (db4) wavelet is selected due to its good local approximated performance for nonstationary signals [19,24]. Five frequency sub-bands of clinical interest are then obtained by using the wavelet decomposition and reconstruction: delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14)(15)(16), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and gamma . Herein, wavelet features of its good localizing properties are extracted from each sub-band in the time-frequency domain, followed by a well-known PCA algorithm of the dimensionality reduction in order to remove the irrelevant or spurious features.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, the original EEG signal (0-64 Hz) is firstly decomposed into its higher frequency part (32-64 Hz) and lower frequency part (0-32 Hz), i.e., the detail and the approximation of the signal at the first level. Then, the approximation of the first decomposition level is additionally divided into its higher (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and lower (0-16 Hz) frequency part, i.e., the detail and approximation at the second level. Thus, the wavelet threshold method can perform well in denoising nonstationary EEG signals, which is defined by [26]:…”

Section: Signal Pre-processing: Wavelet Threshold De-noisingmentioning

confidence: 99%

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Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

223

106

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Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

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Section: Methodsmentioning

confidence: 99%

Section: Signal Pre-processing: Wavelet Threshold De-noisingmentioning

confidence: 99%

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

223

106

View full text Add to dashboard Cite

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“…2). Then the approximation coefficients are further decomposed into next level of approximation and detail coefficients (26,27).…”

Section: Methodsmentioning

confidence: 99%

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Öztürk¹,

Türe²,

Kıylıoğlu³

et al. 2018

meandros

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Objective: Electroencephalogram (EEG) signals have been broadly utilized for the diagnosis of epilepsy. Expert physicians must monitor long-term EEG signals that is sometimes difficult and time consuming process for epilepsy diagnosis. In this study, classification performances of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), which are widely used in computer supported epilepsy diagnosis, were compared by using wavelet-based features of extracted from EEG signals which were derived in either normal or inter-ictal periods. In addition, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to determine the effects of dimension reduction on classification success. Materials and Methods:The EEG data were sampled from the EEG laboratory of the Department of Neurology and Clinical Neurophysiology in Adnan Menderes University. Study was approved by local ethics committee with protocol number 2016/873. Ten patients with epilepsy (male -female, age range) and 10 normal (male -female, age range) were the study group. EEG signals of patients with epilepsy were contains only seizure free-epochs. EEG signals were first decomposed into frequency sub-bands by using Discrete Wavelet Transform (DWT) and then some statistical features were calculated from those to classify it's as normal or epileptic. Results: In classification of the EEG signals, it's as normal or epileptic, we achieved %88.9 accuracy rate using SVM with Radial Basis Function (RBF) kernel without dimension reduction. Conclusion: Results showed that, SVM was a powerful tool in classifying EEG signals if it's normal or epileptic.Amaç: Bu çalışmada, epileptik ve epileptik olmayan EEG (Elektroensefelografi) sinyallerinden elde edilen özniteliklerin boyutlarının Temel Bileşenler Analizi (TBA) ve Bağımsız Bileşenler Analizi (BBA) yöntemleri ile indirgenmesinin sınıflandırma başarısı üzerine etkilerinin belirlenmesi ve Doğrusal Ayırma Analizi (DAA) ile Destek Vektör Makinesi (DVM) yöntemlerinin sınıflandırma performanslarının karşılaştırılması amaçlandı. Gereç ve Yöntem: Çalışmaya 10 kontrol ve uzman hekim tarafından epilepsi tanısı konmuş 10 hasta olmak üzere toplam 20 kişi dahil edildi. Epilepsi tanısı konmuş hastalardan alınan EEG kayıtları nöbet geçirmedikleri sırada alınan kayıtlardı. Epileptik

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“…Selection of suitable wavelet is crucial for the analysis of signals through wavelet transform. Based on the biomedical signal to be analyzed, the mother wavelet is chosen [14] [15].…”

Section: Wavelet Familymentioning

confidence: 99%

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Martin¹,

Sujatha²,

Swapna³

2018

IJCA

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In biomedical engineering, many attempts are being reported over the years for automated diagnosis of various brain disorders by classifying EEG (Electroencephalography) signals. Various machine learning algorithms are adopted to address different scenarios in EEG classifications. Feature engineering is playing a vital role in order to enhance the classification efficiency particularly in signal processing applications. This paper elucidates multi-resolution analysis (MRA) of feature engineering and demonstrates how the distinctive features are being engineered in wavelet domain. The implementation results are placed in the form of feature distribution diagrams and provide clear indications in feature selection for epilepsy seizure detection through classification.

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Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis

Cited by 487 publications

References 107 publications

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

The Comparison of Different Dimension Reduction and Classification Methods in Electroencephalogram Signals

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

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