Parallel algorithm to analyze the brain signals: application on epileptic spikes.

In the current work, we have proposed a parallel algorithm for the recognition of Epileptic Spikes (ES) in EEG. The automated systems are used in biomedical field to help the doctors and pathologist by producing the result of an inspection in real time. Generally, the biomedical signal data to be processed are very large in size. A uniprocessor computer is having its own limitation regarding its speed. So the fastest available computer with latest configuration also may not produce results in real time for the immense computation. Parallel computing can be proved as a useful tool for processing the huge data with higher speed. In the proposed algorithm 'Data Parallelism' has been applied where multiple processors perform the same operation on different part of the data to produce fast result. All the processors are interconnected with each other by an interconnection network. The complexity of the algorithm was analyzed as Θ((n + δn) / N) where, 'n' is the length of the input data, 'N' is the number of processor used in the algorithm and 'δn' is the amount of overlapped data between two consecutive intermediate processors (IPs). This algorithm is scalable as the level of parallelism increase linearly with the increase in number of processors. The algorithm has been implemented in Message Passing Interface (MPI). It was tested with 60 min recorded EEG signal data files. The recognition rate of ES on an average was 95.68%.