Study of Compressed Sensing and Predictor Techniques for the Compression of Neural Signals under the Influence of Noise
Study of Compressed Sensing and Predictor Techniques for the Compression of Neural Signals under the Influence of Noise
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DOI：
10.1109/embc.2018.8512469
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Matteo Pagin;M. Ortmanns
中科院分区：
文献类型：
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作者：
Matteo Pagin;M. Ortmanns
本文分析了基于压缩感知（CS）和预测器技术的神经信号压缩方案。重点是压缩算法可以减少多少数据，同时不影响后续的信号处理。由于神经信号是通过尖峰排序算法来处理的，因此评估不是微不足道的，也不是很好定义的，因为实际上存在许多不同的方法来检测和聚类尖峰。在实现这种压缩技术之前，评估压缩方案对尖峰信号排序程序结果的影响是至关重要的一步。在分析中，评估了两个用例：在第一个用例中，检测和提取尖峰，然后才进行压缩。在第二种情况下，没有关于尖峰的信息可用，并且整个原始信号被压缩。当只处理尖峰帧时，CS几乎没有损失地提供了很大的压缩，在整个记录的情况下，其性能大大受损，并且增量压缩在数据减少和尖峰排序结果方面优于它。在这种情况下，减少率是适度的，但是显著的，是数据减少的103 - 4倍，并且整个信号被保留，避免了信息的大的永久损失。
In this paper an analysis of compression schemes based on compressed sensing (CS) and predictor techniques for neural signals is presented. The focus is on how much a compression algorithm can reduce data while not affecting the subsequent signal processing. Since neural signals are processed by means of spike sorting algorithms the evaluation is not trivial and not well defined, since there exists in fact many different ways to detect and cluster the spikes. Evaluating how much a compression scheme affects the result of spike sorting programs is a crucial step before implementing such compression technique. In the analysis two use cases are evaluated: in the first, spikes are detected and extracted and only thereafter compressed. In the second case, no information on the spikes is available and the whole raw signal is compressed. When dealing only with spike frames CS offers great compression at almost no loss, in the case of the whole recording its performances are greatly impaired and delta compression outperforms it in terms of data reduction and spike sorting results. In this case the reduction rates are modest but significant, ≈3 – 4 times data reduction and the whole signal is preserved avoiding big permanent losses of information.