Spike sorting with support vector machines

Author

Vogelstein, R. Jacob ; Murari, Kartikeya ; Thakur, Pramodsingh H. ; Diehl, Chris ; Chakrabartty, Shantanu ; Cauwenberghs, Gert

Author_Institution

Dept. of Biomedical Eng., Johns Hopkins Univ., Baltimore, MD, USA

Volume

1

fYear

2004

fDate

1-5 Sept. 2004

Firstpage

546

Lastpage

549

Abstract

Spike sorting of neural data from single electrode recordings is a hard problem in machine learning that relies on significant input by human experts. We approach the task of learning to detect and classify spike waveforms in additive noise using two stages of large margin kernel classification and probability regression. Controlled numerical experiments using spike and noise data extracted from neural recordings indicate significant improvements in detection and classification accuracy over linear amplitude- and template-based spike sorting techniques.

Keywords

bioelectric phenomena; electrodes; learning (artificial intelligence); medical signal detection; medical signal processing; neurophysiology; signal classification; support vector machines; additive noise; linear amplitude-based spike sorting; machine learning; neural recordings; single electrode recordings; spike sorting; spike waveform classification; spike waveform detection; support vector machines; template-based spike sorting; Additive noise; Data mining; Electrodes; Humans; Kernel; Machine learning; Noise level; Sorting; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-8439-3

Type

conf

DOI

10.1109/IEMBS.2004.1403215

Filename

1403215