IEEE Engineering in Medicine and Biology Society. Conference Proceedings vol:1 pages:6608-11
International Conference of the IEEE Engineering in Medicine and Biology Society edition:31 location:Minneapolis date:2-6 September 2009
The monitoring of epileptic seizures is mainly done by means of video/EEG-monitoring. Although this method is considered as the golden standard, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient's movement. This makes long term home monitoring not feasible. A detection system with accelerometers attached to the wrists and ankles can solve this problem. Nocturnal frontal lobe seizures often include bicycle pedaling movements or uncontrolled movements with the arms which are clearly visible in the accelerometer signals. Data from three patients suffering from nocturnal frontal lobe seizures is used in this paper for the development of an automatic detection algorithm for this type of seizure. First movement epochs are detected as a preprocessing step by calculating the standard deviation of a sliding window. Afterwards a moving average filter is applied to the data and thresholds are set to the signals of the arms and legs to detect the seizures. This resulted in an algorithm with a sensitivity of 91.67% and a specificity of 83.92%.