應用混合編碼技術處理多頻道之生醫訊號壓縮

生醫訊號的發展非常快速,從基本的心跳訊號到越來越複雜的人體神經訊號的量測,只靠傳統的普通訊號處理已經無法完成更進一步的分析,對於科學的演進與發展有重大的困難.而數位訊號處理恰好能夠提供快速而且完整的分析結果,其中視訊的訊號與多頻道的生醫神經訊號有非常多的雷同之處,所以應用視訊技術壓縮概念在生醫訊號是一個創新的技術. 生醫訊號可以分成自發性的(spontaneous)與誘發性的(evoked)兩種情況.前者的SNR較好,每一個訊號可以獨立簡單處理,後者的SNR比較差,而且在短時間之內訊號大量的重疊出現,造成後續的分析十分困難.但是兩者都有根本上的壓縮需求.前者雖然可以簡單的利用分析的技巧將其分離,但是目前的所有的SPIKE SORTING的演算法都無法提供非常好的正確率,因此仍有很多的應用需要靠人工的方式來作訊號的處理.因此需要完整的訊號記錄來達成此目的.後者需要壓縮的理由更是顯而易見,因為太過於複雜的處理模式是沒有辦法在前端作處理,必須要把訊號真實的完整記錄之後,才能在後端作訊號的分析.因此這壓縮對於生醫訊號而言是有其非常重要的必要性的. 本論文利用視訊訊號與生醫訊號的相關性,在關鍵的basis transform的選擇上用多種方式作了實驗(Discrete Cosine Transform ,Discrete Sine Transform, Hadamard transform, 5-3 Discrete Wavelet Transform) 在其中作了完整的分析後選擇出最好的transform來作壓縮.另外在生醫訊號如何排成視訊訊號上的空間-時間軸的選擇上也用了理論以及物理意義來說明後作了最佳的選擇.成功的使用的視訊壓縮技術中最關鍵的motion vectors (MVs)找出生醫訊號中的重複可壓縮的多餘部. 接下來更利用的混合式的編碼壓縮,在各種領域上(時間,頻率等等)找到更多的訊號的重複性質,進而使用更多樣化的編排方式來壓縮到更好的效果.對於訊號的特色分析也找到其與一般的視訊訊號不同處並加以最優化.種種的分析可以使得訊號壓縮的成果在CR=16之下,達到27.8db的好效果,並且比之前的成果提高4db之多.

關鍵字

生醫訊號；壓縮；信號處理

並列摘要

Multichannel neural recording is one of the most important topics in the field of biomedical engineering. This is because there is a need to considerably reduce large amounts of data without degrading the data quality for easy transfer through wireless transmission. Both spontaneous data and evoked data need compression. Spontaneous data has good SNR, and it can be easily detected by a simple threshold. But some further analysis need very high accuracy. Nowadays spike sorting technique cannot afford such high accuracy(95% up). So we still need compression method to get the original data for manual sorting. Evoked data has low SNR. It cannot be easily detected. Original data is the only way to analyze. Video compression technology is of considerable importance in the field of signal processing. There are many similarities between multichannel neural signals and video signals. In this study, we propose a signal compression method that employs motion vectors (MVs) to reduce the redundancy between successive video frames and between successive channels. We also try different transforms to get the best results. We try DCT (Discrete Cosine Transform), DST (Discrete Sine Transform), 5-3DWT(Discrete Wavelet Transform), Hadamard transform. Finally we discover the best performance is DCT. Further we utilize the hybrid coding method to get better performance. Although there is no evidence showing that there is correlation or prediction between time domain or cross-experiments. But if we look inside the DCT (Discrete Cosine Transform), there is very highly concentrated in time domain. The energy is compact. Also, cross-experiments figure shows that there are many similarities between different experiments. During this stage, we also change the scan mode from "zig-zag" to straight. This is because there is different energy distribution between nature signal and neural signal. This changing of scan mode contributes 2 db gain without algorithm changing. Thus, we perform hybrid-coding to reduce the different part of redundancy. In intra-mode, we perform the channel and time domain correlation to reduce the data. In inter-mode, we use cross-experiment redundancy to do the compression. We also try 3 different frame setting to compare. Channel-mode, time-mode, and event-mode has different settings and results. Event-mode utilizes the most redundancy, so it has best performance. Under CR(compression ratio)=16, the event-mode setting can get SNR=27.8db, compared the previous work there is 4 db improvement.

並列關鍵字

neural signal ； compression ； brain machine interface

參考文獻

1. Sodagar, A. A., Wise, K. D., and Najafi, K., A fully integrated mixed-signal neural processor for implantable multichannel cortical recording. Ieee Transactions on Biomedical Engineering, 2007. 54(6): p. 1075-1088.

2. Casson, A. J. and Rodriguez-Villegas, E. Data reduction techniques to facilitate wireless and long term AEEG epilepsy monitoring. in Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on. 2007.

3. Wise, K. D., Sodagar, A. M., Yao, Y., Gulari, M. N., Perlin, G. E., and Najafi, K., Microelectrodes, microelectronics, and implantable neural microsystems. Proceedings of the Ieee, 2008. 96(7): p. 1184-1202.

4. Perelman, Y. and Ginosar, R., An integrated system for multichannel neuronal recording with spike/LFP separation, integrated A/D conversion and threshold detection. Ieee Transactions on Biomedical Engineering, 2007. 54(1): p. 130-137.

5. Kamamoto, Y., Harada, N., and Moriya, T. Interchannel dependency analysis of biomedical signals for efficient lossless compression by MPEG-4 ALS. in Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on. 2008.

國際替代計量

應用混合編碼技術處理多頻道之生醫訊號壓縮

全文下載

主題瀏覽