Algorithm and implementation of multi-channel spike sorting using GPU in a home-care surveillance system
Intensive home-care surveillance programs are associated with a marked decrease in the need for hospitalization. They can improve the functional statuses of elderly patients with severe congestive diseases. The GPU-based home-care surveillance system is effective and has a major impact on health expenditure than traditional surveillance equipments. In this work, we propose a spike sorting technique as a specific case for the GPU-based home surveillance system. Spike sorting is the procedure of classifying spikes corresponding to the firing neurons. In neuroscience research, spike sorting is adopted to analyze neural activities, brain functions and sensation. It is also a key component in cortically-controlled neuro-prosthetics for patients. In order to efficiently distinguish different neural spike activities, a robust spike sorting algorithm is required for above applications. To improve accuracy, multi-channel spike sorting is necessary. In addition, real-time monitoring for a home-care system is required. Therefore, we exploit a CUDA implementation using GPU for acceleration.