博碩士論文 102521120 詳細資訊




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姓名 黃銘浩(Ming-Hao Huang)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 應用於心電訊號壓縮感測之感測器端設計與特徵擷取
(Compressed Sensing Encoder Design and Feature Extraction for ECG Signals)
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摘要(中) 我們提出應用於無線體域網路(Wireless body sensor network)偵測心電訊號(Electrocardiogram, ECG)之低複雜度壓縮感測技術(Compressed sensing),本論文主要針對編碼器端(encoder)進行設計。將心電圖經過離散小波轉換(discrete wavelet transform, DWT),利用在小波域(wavelet domain)上的稀疏特性,以大量的心電訊號資料庫統計轉換後的結果,建立心電訊號轉換後的特性圖,幫助搜尋小波訊號數值較大的代表點。繼而乘上二元稀疏矩陣(binary sparse matrix)將訊號壓縮兩倍,就可以用來無線傳輸。硬體和記憶體設計方面,盡量減少記憶體的使用數量而且讓每個時脈都進行運算,這樣可以降低硬體面積以及運算時間。接著我們將編碼端進行硬體實現,驗證硬體運作功能與演算法相符合之後,利用台積電0.18μmCMOS製程(T18)進行硬體佈局(layout),驗證DRC和LVS通過後,以手動接線方式將數位和類比的佈局進行整合,完成整個心電感測系統的晶片。數位晶片的部分使用面積(core area)為2.75 mm^2且gate-count為83.8K,電壓輸入1.8V與操作頻率為1.2KHz時,功率消耗為0.718μW。
另一方面,我們提出應用於編碼端的心電訊號萃取演算法,參考許多文獻的方法加以改善和整合。用微分(differentiation)和平方(squaring)的運算找出心電圖QRS波群(QRS complex)區域,尋找最大值為R波峰(R peak),如果搜尋失敗則利用Gabor小波對正(Gabor wavelet correlation)輔助搜尋,再使用Gabor小波轉換萃取QRS波群特性。接著用前兩個心電訊號的已知資訊與可變得臨界值,以複雜度較低的方法搜尋之後的心電圖特性點,此方式可兼具正確性和低運算複雜度。最終比較手動標記和演算法搜尋特性點位置的誤差,相對於其他文獻,效能可得到改善。
摘要(英)
We present low complexity compressed sensing techniques for monitoring Electrocardiogram (ECG) signals in wireless body sensor network. This thesis focus on the design of the encoder part. First, by discrete wavelet transform (DWT), ECG wavelet shows sparsity in the wavelet domain. We then take adventage of the statistics property of ECG signals in the wavelet domain for searching the most significant nonzero components. Next, with the binary sparse sensing matrix, the compression ratio of 2 is obtained and the quantity of the output signals is reduced to a half. In order to decrease chip area and processing time, we try to reduce the memory size and enhance the hardware efficiency. The design is implemented in 0.18μm CMOS technology. After DRC and LVS verification, the analog layout and digital layout are intergrated to complete the SoC chip. As to our digital signal processing part, the core area is 2.75 mm^2 and the gate-count is 83.8K gates. When input voltage is 1.8V and operating frequency is 1.2KHz, power consumption is 0.718μW.
On the other hand, we design ECG feature extraction algorithm. First, the QRS complex region is identified by differentiation and squaring to remove baseline wondering, and then the peak of the result is marked as R peak. If the peak search fails, the Gabor wavelet correlation is adopted at the second step. Also, variable threshold is used according to the adjacent ECG signals to strengthen the feature extraction results with low complexity. Finally compared to the prior works, our proposed scheme shows good detection results.
關鍵字(中) ★ 無線體域網路
★ 心電訊號
★ 離散小波轉換
★ 壓縮感測
關鍵字(英) ★ Wireless body sensor network
★ Electrocardiogram
★ Discrete wavelet transform
★ Compress sensing
論文目次
摘要 I
Abstract II
目錄 III
圖示目錄 VI
表格目錄 X
第一章 緒論 1
1.1 研究動機 1
1.2 研究方法 1
1.3 論文組織 2
第二章 心電訊號壓縮感測相關知識介紹 3
2.1 心電圖(Electrocardiogram, ECG)介紹 3
2.2 偵測心電訊號無線體域網路 4
2.3 心電訊號資料庫 5
第三章 心電訊號感測壓縮 7
3.1 心電訊號轉換方法 8
3.2 離散小波轉換介紹 9
3.3 逆離散小波轉換(Inverse Discrete Wavelet Transform) 13
3.4 基於小波轉換部分已知集合(Wavelet Based Partially Know Support) 14
3.5 K個非零點之選擇方法 16
3.6 演算法模擬 18
3.7 模擬參數設定與結果 18
3.8 應用於演算法之堆積排序(heap sort) 20
3.9 堆積排序之整合 (Merge) 22
3.10 稀疏二元矩陣感測(Sparse Binary Sensing) 23
第四章 硬體設計與實現 26
4.1 硬體設計流程 26
4.2 硬體方塊圖 27
4.2.1 輸入端的記憶體和頻率的切換 27
4.2.2 離散小波轉換硬體設計 29
4.2.3 離散小波轉換的記憶體設計 33
4.2.4 離散小波轉換記憶體調整和資料流 38
4.2.5 K個非零點的選擇 40
4.2.6 二元稀疏矩陣 42
4.3 量化方法以及整體壓縮量 44
4.4 硬體整體架構圖與多工輸出設計 45
4.5 硬體佈局(layout)的設計與實作 48
4.6.1 硬體實現與設計流程 48
4.6.2 硬體實現結果 50
4.6.3 合成結果 51
第五章 心電圖特性萃取 56
5.1 心電圖特性點 57
5.2 文獻提出之R波與QRS波群偵測 57
5.2.1 Pan和Tompkins 提出之偵測QRS波群演算法 58
5.2.2 基於Pan和Tompkins的QRS波群偵測模擬 59
5.2.3 Gabor 小波轉換搜尋QRS波群 62
5.2.4 Gabor小波轉換之QRS波群偵測演算法模擬 63
5.3 P波與T波偵測 66
5.3.1 以可變動的R-R區間計算搜尋P、T波 67
5.3.2 以高斯函數比對搜尋P、T波 67
5.4 改善的QRS波群偵測演算法 67
5.4.1 初始R波偵測 69
5.4.2 QRS波群偵測 72
5.4.3 心電圖區間擷取 74
5.4.4 以可變的臨界值偵測R波位置 75
5.4.5 R-R區間更新 76
5.5 效能比較 76
第六章 結論 79
參考文獻 80
參考文獻

[1] J. W. Jhuang and H. P. Ma, “A Patch-sized Wearable ECG/Respiration Recording Platform with DSP Capability,” 17th International Conference on E-health Networking, Application & Services (HealthCom), 2015.
[2] Y. C. Chueng, P. Y. Tsai, and M. H. Huang, “Matrix-Inversion-Free Compressed Sensing With Variable Orthogonal Multi-Matching Pursuit Based on Prior Information for ECG Signals,” IEEE Transaction on Biomedical Circuit and Systems, volume 10, pp. 864-873, 2016.
[3] Y. C. Chueng, P. Y. Tsai, “Low-Complexity Compressed Sensing with Variable Orthogonal Multi-Matching Pursuit and Partially Known Support for ECG Signals,” Master Thesis, National Central University, 2016.
[4] http://research.vet.upenn.edu/smallanimalcardiology/ECGTutorial/tabid/4930/Default.aspx
[5] https://en.wikipedia.org/wiki/Discrete_wavelet_transform
[6] https://en.wikipedia.org/wiki/Electrocardiography
[7] N. Bayasi, T. Tekeste, H. Saleh, B. Mohammad, M. Ismail, “A 65-nm Low Power ECG Feature Extraction System,” 2015 IEEE International Symposium on Circuit and Systems (ISCAS), pp. 746-749, 2015.
[8] J. Pan, W. J. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering, pp. 230-236, 1985.
[9] G. G. C. Lee, Z. J. Huang, C.Y. Chen, C. F Chen, “Implementation of Gabor Feature Extraction Algorithm for Electrocardiogram on FPGA,” 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 798-801, 2015.
[10] G. G. Lee, J. Y. Hu, C. F. Chen, H. H. Lin, “Gabor Feature Extraction for Electrocardiogram Signals,” 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 304-307, 2012.
[11] T. Tekeste, N. Bayasi, H. Saleh, A. Khandoker, B. Mohammad, M. AI-Qutayri, and M. Ismail, “Adaptive ECG Interval Extraction,” 2015 IEEE International Symposium on Circuits and Systems (ISCAS), p.p. 998-1001, 2015
[12] M. Abo-Zahhad, “ECG Signal Compression Using Discrete Wavelet Transform,” InTech, Available from: https://www.intechopen.com/books/discrete-wavelet-transforms-theory-and-applications/ecg-signal-compression-using-discrete-wavelet-transform
[13] A. L. Goldberger et al. “Physiobank, Physiotoolkit, and Physionet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215-e220, 2000.
[14] G.B. Moody, R.G. Mark. “The impact of the MIT-BIH Arrhythmia Database”. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID:11446209)
[15] P. Laguna, R.G. Mark, A. Goldberg, GB Moody, “A Database for Evaluation of Algorithms for Measurement of QT and Other Waveform Intervals in the ECG,” in Computers in Cardiology 1997, pp. 673-676, 1997.
[16] Hyejung Kim et al., “A Configurable and Low-Power Mixed Signal SoC for Portable ECG Monitoring Applications,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 8, No. 2, pp. 257-267, Apr. 2014.
[17] Y. H. Tu, K. W. Yao, M. H. Huang, Y. Y. Lin, H. Y. Chi, P. M. Cheng, P. Y. Tsai, M. T. Shiue, C. N. Liu, K. H. Cheng, J. S. Fu, “A Body Sensor Node SoC for ECG/EMG Applications with Compressed Sensing and Wireless Powering,” accepted by 2017 VLSI-DAT, 2017
[18] M. Pallavi, H.M. Chandrashekar, “Study and Analysis of ECG Compression Algorithms,” 2016 International Conference on Communication and Signal Processing (ICCSP), 2016
[19] http://www.medicine-on-line.com/html/ecg/e0001ct.htm#202
指導教授 蔡佩芸(Pei-Yun Tsai) 審核日期 2017-5-11
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