Tarek Lajnef

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Spikes and sharp waves recorded on scalp EEG may play an important role in identifying the epileptogenic network as well as in understanding the central nervous system. Therefore, several automatic and semi-automatic methods have been implemented to detect these two neural transients. A consistent gold standard associated with a high degree of agreement(More)
BACKGROUND Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep(More)
A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse(More)
Keywords: Epilepsy High-frequency oscillations (HFOs) Intracereberal EEG Empirical mode decomposition (EMD) Hilbert spectral analysis (HSA) Root mean square (RMS) a b s t r a c t Discrete high-frequency oscillations (HFOs) in the range of 80–500 Hz have previously been recorded from human epileptic brains using intracereberal EEG and seem to be a reliable(More)
Recent studies have reported that discrete high frequency oscillations (HFOs) in the range of 80-500Hz may serve as promising biomarkers of the seizure focus in humans. Visual scoring of HFOs is tiring, time consuming, highly subjective and requires a great deal of mental concentration. Due to the recent explosion of HFOs research, development of a robust(More)
Discrete High Frequency Oscillations (HFOs) in the range of 80-500 Hz have recently received attention as a promising reliable biomarkers for epileptic activity, both in scalp EEG as well as in intracranial recordings. HFOs are often characterized by variable durations (10-100 ms) and rates of occurrence (17.5 ± 9.5 / min). The total duration of HFOs(More)
Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy(More)
High-Frequency Oscillations (HFOs) in the 80-500 Hz band are important biomarkers of epileptogenic brain areas and could have a central role in the process of epileptogenesis and seizure genesis. Visual marking of HFOs is highly time consuming and tedious especially for long electroencephalographic (EEG) recordings. Automated HFO detection methods are(More)
Epileptic seizure detection requires the study of electroencephalogram (EEG) data. Visual marking of seizure onset in such EEG recordings is quite tedious, naturally subjective, extremely time consuming, and it may lead to inaccurate detection. Thus, the development of a robust framework for automatic seizure classification is necessary and can be very(More)
Epilepsy is one of the most common neurological disorders and affects almost 60 million people worldwide; many techniques were used to detect epileptic activities in the EEG recording. In this study we have used detection method based on linear prediction error. The main idea of this method is that LP filter can not track sudden changes caused by spikes and(More)