The objective of this study is to develop methods to dynamically select EEG channels to reduce power consumption in seizure detection while maintaining detection accuracy. A method is proposed whereby a number of primary screening channels are predefined. Depending on the classification results of those channels, further channels are selected for analysis. This method provides savings in computational complexity of 43%. A further
method called idling is then proposed which increases the computational saving to 75%. The performance of a location-independent, decision-based method is used for comparison. The proposed method achieves better computational savings for the same performance than the
decision-based method. The decision-based method was capable of higher overall computational savings, but with a reduction in seizure detection performance. Each method was also implemented with the REACT algorithm on a Blackfin microprocessor and the average
power measured. The proposed methods gave a power saving of up to 47% with no reduction in detection performance.