Daftar Isi: OFFLINE SPIKE DETECTION USING TIME DEPENDENT ENTROPY

Daftar Isi:

ABSTRACT Analysis of the neuronal activities is essential in studying nervous system mechanisms. True interpretation of such mechanisms relies on the detection of the neuronal activities, which appear as action potentials or spikes in recorded neural data. So far several algorithms have been developed for spike detection. In this paper such issue is addressed using entropy measures. Transient events like spikes affect the entropy content of a signal. Thus, a time-dependent entropy framework can be used for spike detection where the entropy of each windowed segment of neural data is computed based on a generalized form of entropy. Detection method is tested on different signal to noise ratios. The results show that the time-dependent entropy method in comparison with available methods enables us to detect spikes in their exact time of occurrence with relatively lower false alarm rate.