Name
Abstract | ||
|---|---|---|
It is difficult to identify the spikes to different classifications especially when the neurons have many similar spike waveforms or lots of overlapped spikes. Our previous study proposed the window-slope representation (WSR), and it improved the classification accuracy of high similar spike waveforms. The classification accuracy of the method, however, will be affected by lots of overlapped spikes or low signal-to-noise ratio. In this paper, the secondorder difference method is introduced to solve those problems. First the second-order difference of every spike is calculated to describe the convexity-concavity of the waveform. Then we use the window-slope representation to describe the tendency of waveform at each time. The method is tested at various signal-to-noise ratio levels based on simulation data coming from the Wave_clus. The experiment results show that the accuracy of classification can be improved by using the method together with K-means Cluster. In our experiments, the classification accuracy is above 95% on all datasets in Wave_clus, even under the low signal-to-noise ratio. It shows that this method has a strong robustness. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4304/jcp.9.3.733-740 | JOURNAL OF COMPUTERS |
Keywords | Field | DocType |
spike sorting, second-order difference, window-slope representation, overlapped spikes, K-means clustering | k-means clustering,Pattern recognition,Spike sorting,Computer science,Waveform,Algorithm,Robustness (computer science),Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 3 | 1796-203X |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||