Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data

Automated Epileptic Seizure Detection Methods: A Review Study

“…ANNs have been trained using either raw data (Ko & Chung, 2000;Ozdamar & Kalayci, 1998;Pang, Upton, Shine, & Kamath, 2003;Webber, et al, 1994) or select features (Acir, Oztura, Kuntalp, Baklan, & Guzelis, 2005;Castellaro et al, 2002;Gabor & Seyal, 1992;Liu, Zhang, & Yang, 2002;Pang, et al, 2003;Tzallas, Karvelis, et al, 2006;Webber, et al, 1994) to detect spikes. In the first case, windows of raw EEG data are fed into an ANN.…”

“…Knowledge-based reasoning in addition to the aforementioned methods is widely used (Tzallas, Karvelis, et al, 2006). This arises from the need to incorporate knowledge of neurophysiologists that adopt spatial and temporal rules (Acir, et al, 2005;Dingle, Jones, Carroll, & Fright, 1993;Edwards, James, Coakley, & Brown, 1976;Glover, Raghavan, Ktonas, & Frost, 1989;James, 1997;James, et al, 1999;Liu, et al, 2002;Ozdamar, Yaylali, Jayakar, & Lopez, 1991;Tzallas, Karvelis, et al, 2006;Webber, et al, 1994). More specifically, Glover and coauthors (1989), Dingle and coauthors (1993), and Liu and coauthors (2002) used a knowledge-based system with a high degree of success, taking advantage of both spatial and temporal information.…”

“…Gotman and Gloor (1976) decomposed the waveform into two half-waves with opposite directions. Similar methods for decomposing the EEG waveform into half-waves have been used by many authors (Davey, Fright, Carroll, & Jones, 1989;Faure, 1985;Webber, Litt, Wilson, & Lesser, 1994). Faure (1985) introduced a concept where the duration, amplitude, and slope attributes of half-waves were used to classify them into states.…”

Automatic Detection of Paroxysms in EEG Signals Using Morphological Descriptors and Artificial Neural Networks

“…The length variation coefficient -used to measure the regularity of the signal -is the ratio of standard deviation and the mean value of the signal. The crest factor is the difference between the maximum and minimum amplitudes, divided by the standard deviation (Webber et al, 1994). The entropy, reported in several studies -e.g.…”

Large scale mining and evidence combination to support medical diagnosis

“…Some focus on discriminating EEGs between normal EEGs and EEGs of a particular condition: dementia in [14], epilepsy in [12,13]. Others try to detect specific patterns in EEGs such as epileptiform discharges in [31,32,33,34], seizure activity ( [35]), EEG background activity in [36] or sleep stages in [37,38]. Quantitative EEG analysis is also being used to obtain prognosis information for patients with ischaemic stroke( [39,40]).…”