Paper Info
Title
Hass: High Accuracy Spike Sorting With Wavelet Package Decomposition And Mutual Information
Abstract
Neural signal processing has been dramatically improved with the development of microfabrication technology that enables multi-channel signal recording and high-precision signal detection. However, due to the diversity of spike features generated by different neurons and the high noise level in raw data, it is challenging to distinguish the activity of neurons collected by hundreds of closely-positioned recording probes from the background electrical noise. To address this issue, we propose an accurate supervised spike sorting solution based on wavelet package decomposition and mutual information. Furthermore, we build a framework based on the proposed algorithms with a short training stage to automate the data processing. Evaluation results on the raw data from popular datasets show that our solution can provide higher clustering accuracy while maintaining good noise resistance compared to state-of-the-art methods. With our solution, the clustering accuracy can reach up to 99.76% on the dataset with highest noise level. The overall accuracy of our solution can outperform the baseline target by up to 22.35%.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621401
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
Field
DocType
Spike sorting, discrete wavelet decomposition, mutual information, clustering
Signal processing,Data processing,Pattern recognition,Detection theory,Spike sorting,Computer science,Noise (electronics),Artificial intelligence,Mutual information,Cluster analysis,Machine learning,Wavelet
Conference
ISSN
Citations 
PageRank 
2156-1125
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yao Chen15611.01
Libo Huang28225.47
Jiong He3201.08
Kunyao Zhao400.34
Ruichu Cai524137.07
Zhifeng Hao665378.36