Spike detection algorithm improvement, spike waveforms projections with PCA and hierarchical classification

Definition of single spikes from multiunit spike trains plays a critical role in neurophysiology and in neuroengineering. Moreover, long period analysis are needed to study synaptic plasticity effects and observe the long and medium term development on which all central nervous system (CNS) learning functions are based. Therefore, the increasing importance of long period recordings makes necessary on-line and real time analysis, memory use optimization and data transmission rate improvement. A threshold-amplitude spikes detection method is chosen and 5 noise level estimate methods were developed. Than APs are bundled to group using principal component analysis and classified (hierarchical classifier). The system has lot of applications like high-throughput pharmacological screening and monitoring effects.


Published in:
Proceedings of the 4th IET International Conference on Advances in Medical, Signal and Information Processing, 122-122
Presented at:
4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008), Santa Margherita Ligure, Italy, July 14-16, 2008
Year:
2008
Publisher:
IEEE
Laboratories:




 Record created 2017-02-05, last modified 2018-01-28

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