The present work develops a novel hybrid method for ocular and muscular artifact removal from electroencephalography (EEG) signals, EFICA-TQWT. It is a combination of efficient fast independent component analysis (EFICA) method with the tunable Q-factor wavelet transform (TQWT). The main contribution of this paper is to apply the 3D interpolation method in the filtering system. Three EEG datasets are used in this work, two healthy and one epileptic. The choice of subjects for each dataset is made with the help of an expert in physiology. The selection criterion adopted is the presence of muscular and ocular artifacts in the processed recordings. First, a noisy channel automatic classification is performed by the support vector machine (SVM) with radial basis function in order to delete the signal(s) corresponding to the noisiest channel(s) from each EEG recording. The results of the automatic classification by the SVM were compared with those found by the expert’s classification. An accuracy of 97.45%, a sensitivity of 86.66% and a 100% specificity are provided by the SVM classification. The hybrid method of artifact removal will be applied on the rest of the EEG channels of international 10/20 system for each subject. Then, a reconstruction of the eliminated channel signal(s) will be performed in order to obtain a well-filtered signal. The proposed filtering process is evaluated by calculating the mean squared error (MSE) and the signal to noise ratio (SNR). Both for the healthy and pathological EEG datasets, a comparative study of the proposed method (EFICA-TQWT) and other filtering techniques (Fast-ICA, DWT, TQWT and EFICA) is generated. The EFICA-TQWT method gave the best results with a minimum of MSE and a maximum of SNR, more particularly in the case of the application of the 3D interpolation method. Besides, in order to optimize the computing time of the proposed system, a parallel implementation of this filtering system is developed based on graphical processing units using compute unified device architecture.

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This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation (STD) of two Heart rate variability features (The number of heartbeats per two minutes and approximate entropy) computed to ensure prediction and quantification of the pre-ictal state. We analyzed clinical data taken from two epileptic public databases, Siena Scalp EEG and Post-Ictal Heart Rate Oscillations in Partial Epilepsy and a local database. By testing the proposed approach on the Siena scalp EEG database, we achieved a sensitivity of 100%, specificity of 95%, and an accuracy of 96.4% whereas using acquisitions from the post-Ictal database, we achieved a sensitivity of 100%, specificity of 91% and an accuracy of 94% and using the local database we achieved a sensitivity of 100%, a specificity of 97% and an accuracy of 97.5%. Furthermore, the proposed approach predicted 58.7%, 57.2, and 40% of the seizures before the onset by more than 10 min for the data taken from post-ictal, local and Siena database, respectively. Using the automatic threshold technique, we were able to achieve a sensitivity, specificity, and accuracy of 85%, 81%, 82% using our local database respectively, whereas using acquisitions take from the Siena Scalp EEG database, we achieved a sensitivity of 75%, specificity of 85% and an accuracy of 82%. Besides, using the post-ictal database, we achieved a sensitivity of 90%, a specificity of 83% and an accuracy of 85%.