|
|
|
| Novel Algorithm to Suppress Random Pulse Interference in Spikes |
| LIU Xing-yu1, WANG Yong-yi2, WAN Hong1, SHANG Zhi-gang1 |
1. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450052, China;
2. School of Information Science and Engineering, Central South University, Changsha 410012, China |
|
|
|
|
Abstract Spikes detection and sorting play an important role in study of neural information coding. Spikes were generally obtained by threshold detection after filtered in traditional detection, which failed to suppress the random pulse interference (RPI), In this paper, a novel algorithm was provided to suppress RPI using integrated feature. The raw neural signals from the primary visual cortex in rats were detected with microelectrode array. After the feature differences between spikes and RPls were compared, the features which include waveform and non-waveform features were extracted respectively, and then the integrated feature was established based on Fisher's discrimi nant ratio to separate between spikes and RPls. The test results of simulation and experiment show that the separability capability of the integrated feature is nearly two times greater than the individual feature, the average correct recognition rate of spikes and RPls is over 93%, and the detection rate of spike is effectively improved.
|
|
Received: 05 October 2019
|
|
|
| Fund:National Natural Science Foundation of China; grant number: 60841004, 60971110 |
|
Corresponding Authors:
WAN Hong. E-mail: wanhong @zzu.edu.cn
|
|
|
|
[1] Quiroga RQ.What is the real shape of extracellular spikes[J]? Journal of Neuroscience Methods, 2009, 177:194-198.
[2] Hill DN, Mehta SB, Kleinfeld D.Quality metrics to accompany spike sorting of extracellular signals[J]. Journal of Neuroscience, 2011, 31: 8699-8705.
[3] Wan H, Liu XY, Niu XK, et al.The design and implementation of anti-interference system in neural electrophysiological experiments[C]. EEIC Proceedings on International Conference on Electric and Electronics;2(Nanchang,China), 2011: 605-611.
[4] Chor JH, Jung HK, Kim T.A new action potential detector using the MTEO and its effects on spike sorting systems at low signal-to-noise ratios[J]. IEEE Transactions on Biomedical Engineering. 2006, 53:738-746.
[5] Diedrich A, Charoensuk W, Brychta RJ, et al.Analysis of raw Microneurographic recordings based on wavelet De-noising technique and classification algorithm: Wavelet analysis in microneurography[J]. IEEE Transactions on Biomedical Engineering, 2003, 50: 41-50.
[6] Kamavuako EN, Yoshida K, Jensen W.Variance-based signal conditioning technique: Comparison to a wavelet-based technique to improve spike detection in multiunit intrafascicular recordings[J]. Biomedical Signal Processing, 2009, 4:118-126.
[7] Adamos DA, Kosmidis EK, Theophilidis G.Performance evaluation of PCA-based spike sorting algorithms[J]. Computer Methods and Programs in Biomedicine, 2008, 91: 232-244.
[8] Mukhopadhyay S, Ray GC.A new interpretation of nonlinear energy operator and its efficacy in spike detection[J]. IEEE Transactions on Biomedical Engineering, 1998, 45:180-187.
[9] Theodoridis S, Koutroumbas K.Pattern recognition (Fourth edition). San Diego: Elsevier Inc, 2009. |
| [1] |
Li Baoming, Hu Jiarui, Xu Haijun, Wang Cong, Jiang Yanni, Zhang Zhihong, Xu Jun. Deep Cascaded Network for Automated Detection of Cancer MetastasisRegion from Whole Slide Image of Breast Lymph Node[J]. Chinese Journal of Biomedical Engineering, 2020, 39(3): 257-264. |
| [2] |
Xu Jie, Wang Xunheng, Li Lihua. Investigating Brain Networks for ADHD Children Based on Phase Synchronization of Resting State fMRI[J]. Chinese Journal of Biomedical Engineering, 2020, 39(3): 265-270. |
|
|
|
|
