簡易檢索 / 詳目顯示

研究生: 王思淮
Szu-Huai Wang
論文名稱: 以回饋式自動模板生成為基礎 之 正規化關聯值棘波偵測系統 之設計及實現
Spike Detection Based on Normalized Correlation with Automatic Template Generation
指導教授: 黃文吉
Hwang, Wen-Jyi
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 中文
論文頁數: 55
中文關鍵詞: 棘波排序棘波偵測FPGANormalized Correlator
論文種類: 學術論文
相關次數: 點閱:305下載:28
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本論文提出了全新架構的回饋式棘波偵測演算法,主要是用來偵測一個未知棘波特色的棘波序列。此方法在初始階段使用了Block energy的棘波偵測法則,接著會把初始階段的結果輸出給Osort部份去進行分群並產生模板,最後再利用此模板來進行Matched filter的棘波偵測的動作。

    在偵測的過程中,閥值的訂定一直是我們非常困擾的問題,所以我們嘗試了多種方式來制定出理想的閥值。一開始利用直接定義閥值的方式,給閥值一個訂值,但是此閥值無法適用於各種棘波序列。所以後來利用棘波序列的中間值來自動定義閥值,且在本系統的初始階段中使用它。 同時我們也透過了將棘波序列、模板正規化來簡化系統中閥值的訂定,並提供了一個制訂閥值的依據。

    本論文還對棘波偵測系統進行加速的動作,使其不只在命中率上有更優異的表現,在產能上也能有所提升。最後也有將此棘波分類系統在FPGA上做實現
    更進一步的提升其棘波偵測的效能。

    第一章 緒論 7 1-1 研究背景與動機 7 1-2 研究目的與方法 8 第二章 棘波偵測之研究背景與演算法則 14 2-1 棘波分類的介紹 14 2-2 棘波偵測演算法則 15 2-2-1 Matched filter 15 2-2-2 Block Energy 17 2-2-3 LRT and GLRT tests 19 2-2-4 Normalized Correlator 22 2-2-5 Advance Normalized Correlator 26 2-3 DETECTION SYSTEM 33 第三章 棘波偵測系統架構 35 3-1 BLOCK ENERGY COMPUTATION UNIT 36 3-2 CORRELATOR UNIT 37 第四章 實驗結果與數據探討 38 4-1 加速運算效能實驗 40 4-2 回饋式演算法則的效能評測 42 4-3 不同閥值的偵測效果 46 4-4 不同偵測法則的效果比較 51 4-5 回饋式演算法的分群效果 53 4-6 硬體電路所耗資源 55 第五章 結論 58 REFERENCES 59

    1. S. Gibson, J. W. Judy, and D. Markovic, Spike sorting: the first step in decoding
    the brain, IEEE Signal Processing Magazine, pp.124-143, 2012.

    2. M.A. Lebedev and M.A.L. Nicolelis, Brainmachine interfaces: past, present and future, Trends in Neurosciences, Vol. 29, pp.536-546, 2006.

    3. M.S., Lewicki, A review of methods for spike sorting: The detection and classification of neural action potentials. Netw. Comput. Neural Syst., Vol. 9, pp. R53R78, 1998.

    4. S. Mukhopadhyay and G. C. Ray, A new interpretation of nonlinear energy operator and its efficacy in spike detection, IEEE Trans. Biomed. Eng., Vol. 45, pp. 180187, 1998.

    5. I. Obeid and P. D. Wolf, Evaluation of Spike-Detection Algorithms for a
    Brain-Machine Interface Application, IEEE Trans. Biomed. Eng., Vol. 51,
    pp. 905-911, 2004.

    6. S. Gibson, J. W. Judy, and D. Markovic, Technology-Aware Algorithm Design for
    Neural Spike Detection, Feature Extraction, and Dimensionality Reduction, IEEE Trans. Neural Systems and Rehabilitation Engineering, Vol. 18, pp.469-478, 2010.

    7. K. Oweiss and M. Aghagolzadeh, Detection and classification of extracellular
    action potential recordings, Chapter 2 of Statistical Signal Processing for Neuroscience, pp.15-74, 2010.

    8. K. Kim and S. Kim, A wavelet-based method for action potential detection from
    extracellular neural signal recording with low signal-to-noise ratio, IEEE Trans. Biomed. Eng., Vol. 50, pp. 999-l011,2003.

    9. R. J. Brychta, S. Tuntrakool, M. Appalsamy, N. R. Keller, D. Robertson, R. G.
    Shiavi, Wavelet Methods for Spike Detection in Mouse Renal Sympathetic Nerve Activity, IEEE Trans. Biomed.Eng., Vol. 54, pp. 82-93, 2007.

    10. R. Q. Quiroga, Z. Nadasdy, and Y. Ben-Shaul, Unsupervised spike detection and
    sorting with wavelets and superparamagnetic clustering, Neural Comp., Vol. 16, pp. 16611687, 2004.

    11. N.Mtetwa, L. S. Smith, Smoothing and thresholding in neuronal spike detection,
    Neurocomputing,Vol. 69, pp. 1366-1370, 2006.

    12. T. Sato, T. Suzuki, and K. Mabuchi, Fast Template Matching for Spike Sorting,
    Electronics and Communications in Japan, Vol. 92, pp.57-63, 2009.

    13. S. Kim and J. McNames, Automatic spike detection based on adaptive template
    matching for extracellular neural recordings, J. Neurosci. Methods, Vol.165, pp.165-174, 2007.

    14. K.D. Harris, Accuracy of tetrode spike separation as determined by simultaneous
    intracellular and extracellular measurements, J. Neurophysiol., Vol. 84, pp. 401-414, 2000.

    15. A. Oliynyk, C. Bonifazzi1, F. Montani, L. Fadiga1, Automatic online spike
    sorting with singular value decomposition and fuzzy C-mean clustering. BMC Neural Sci., Vol. 13, 2012,doi:10.1186/1471-2202-13-96.

    16. W. J. Hwang, W. H. Lee, S. J. Lin and S. Y. Lai, Efficient Architecture for Spike
    Sorting in Reconfigurable Hardware, Sensors, Vol. 13, pp.14860-14887, 2013.

    17. I.T. Jolliffe, Principal Component Analysis, 2nd ed.; Springer: Berlin, Heidelberg,
    Germany, 2002.

    18. S. Miyamoto,H. Ichihashi, and K. Honda, Algorithms for Fuzzy Clustering,
    Springer:Berlin/Heidelberg, Germany, 2010.

    19. U. Rutishauser, Online detection and sorting of extracellularly recorded action
    potentials in human medial temporal lobe recordings, in vivo, J. Neurosci. Methods, Vol.154, pp.204-224, 2006.

    20. J. Wild, Z. Prekopcsak, T. Sieger, D. Novak, and R. Jech, Performance
    comparison of extracellular spike sorting algorithms for single-channel recordings, J. Neurosci. Methods, Vol.203, pp.369-376,2012.

    21. L.S. Smith and N. Mtetwa, A tool for synthesizing spike trains with realistic
    interference. J. Neurosci. Methods Vol. 159, pp. 170-180, 2007.

    22. H. Vicent Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1988

    23. Kou-Hsuan Wu, Efficient VLSI Architecture for Spike Detection Based on Normalized Correlators,pp,10-17, 2013

    QR CODE