![]() ![]() The algorithm could be implemented in real time. The proposed method has several strengths, for example: that no training is required due to the automatic adaptation to the threshold to each new EEG record. ![]() In this case, these indexes reached average values, across all the patients, of 90.3% and 73.7% respectively. The threshold of 20 showed the best relation between SEN and SPE. Three thresholds were evaluated and calculated as 10, 20 and 30 times the median value of ST(n). Therefore, it can be implemented in real time as well as offline. The developed algorithm does not require any training since it is simple and involves low processing time. The process was applied to 425 h of epileptic EEG records from CHB-MIT EEG database. This paper proposes a method to automatically detect epileptic seizures based on adaptive filters and signal averaging. Patient quality of life could improve significantly if the beginning of a seizure could be predicted or detected early. The development of online seizure detection techniques as well as prediction methods are very critical. ![]()
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