Automatic seizure detection in SEEG using high frequency activities in wavelet domain

Abstract

Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80-500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures.

Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals

“…Other wavelet-based systems in (Aarabi et al, 2009), (Chua et al, 2011) and (Ayoubian et al, 2013) achieved a sensitivity of 68.9%, 78%, and 72%, respectively, closed to our results (77.98%). Hence, the wavelet dyadic scalogram and artificial neural network proposed here have obtained competitive results in comparison with current expert systems for epilepsy detection.…”

“…Several techniques were explored in order to improve the performance of Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals automated systems (Arunkumar et al, 2012;Fergus et al, 2015;Gotman and Gloor, 1976;Hunyadi et al, 2011;Olejarczyk et al, 2009;Peker et al, 2016;Petersen et al, 2013;Ramgopal et al, 2014;Wang et al, 2014). In the last decades, a very powerful tool called wavelet transform was applied to solve this problem (Adeli et al, 2007;Ayoubian et al, 2013;Haydari et al, 2011). In (Hramov et al, 2015) are presented several studies in rats by applying time-frequency analysis (such as the continuous wavelet transform) to characterize non-stationary events in EEG signals.…”

Automatic determination of cutoff frequency for filter design using neuro-fuzzy systems

“…Wavelet theory is an analytical method used in many scientific fields [10][11][12][13][14][15][16][17][18]. A. Raheja introduced a wavelet based multiresolution algorithm by extending the concept of switching resolutions in both image and data spaces [10].…”

Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures

“…EEG is the gold standard for seizure diagnosis. Many research groups have investigated EEG-based detection systems [9,12,13]. Although EEG-based systems provide comparably high sensitivity, EEG-based systems have limitations due to the inconvenience of requiring an electrode to be placed on the scalp during daily life.…”