Offline Spike Sorting Using Approximate Entropy

Analysis of neuronal activities is essential in studying nervous system mechanisms. True interpretation of such mechanisms relies on detecting and sorting neuronal activities, which appear as action potentials or spikes in the recorded neural data. So far, several algorithms have been developed for spike sorting. In this paper, spike sorting was addressed using entropy measures. A method based on a modified version of approximate entropy was proposed for feature extraction, which captured the local variations in spike waveforms as well as global variation to create the feature space. Results showed that the entropy-based feature extraction method created more distinguishing features, which reduces spike sorting errors. The proposed method was capable of separate different spikes in small-scale structures, where the technique such as principal component analysis fails.