中文摘要
現今無線監測裝置越來越流行，特別是心臟監測器。心電圖ECG (electrocardiogram, ECG)是一種在心臟活動的監測中廣泛的方法之一。心臟監測器一般而言會先從ECG中取到QRS複和波，隨後換算RR心跳間隔。QRS 複合波的偵測對於計算心率變異率(heart rate variability, HRV)來說是必須的。透過心率變異度，心臟專家能夠診斷出多種心臟病症。而隨著VLSI的發展越來越蓬勃，將生醫電子裝置實現在積體電路上變成一種潮流。
在本篇，我們會提供一個低功耗、低成本的QRS 複合波偵測架構。QRS複合波偵測器的準確度會直接地反映到心率變異率的正確性。然而在準確度、成本跟功率消耗間需要做取捨，這與演算法的本身運算複雜度有關連。我們意圖降低QRS複合波偵測器的運算複雜度，是為了同時降低功率消耗及成本。 在本篇論文中，QRS複合波偵測法是基於小波包分解。起先，我們用了一個低通濾波器以降低高頻雜訊的影響，接者用小波包分解分離出ECG中QRS復合波的特徵值。拆解後，我們會依據雜訊程度選出細節係數1~4的其中兩個。決定階段會先用適應性閥值會選出候選者們，後續再決定最後的QRS複合波。最後驗證的部份，我們用MIT-BIH的ECG資料庫來驗證QRS複合波偵測器的準確度，其中SE跟+P分別可達到99.57%與99.59%。






關鍵字: QRS複合波偵測、小波轉換、適應性閥值、心電圖

Abstract
Nowadays wearable devices become more and more popular, especially cardiac monitor. Electrocardiogram (ECG) is one of general approaches to monitor the cardiac activity. First the cardiac monitor must obtain the QRS complex and then calculate the RR internal from the ECG. Detection of QRS complex is essential for calculation of heart rate variability (HRV). Based on HRV, cardiac experts can diagnose many kinds of cardiac disease. With the progress of Very Large Scale Integration (VLSI), electronic biomedical devices implemented with integrated circuit (IC) technology become a trend.
In this work, we present an low-power and low-cost architecture for QRS complex detection. The accuracy of the QRS complex detector would directly reflect on validity of HRV. However trade-off between the accuracy, cost and power consumption is associated with the algorithm’s computational complexity. In order to reduce both cost and power consumption, we propose to reduce computational complexity of the QRS detection. In this paper, the QRS detection is based on the wavelet decomposition. First, we apply a low pass filter to reduce the high frequency noise and then use wavelet decomposition to extract the characteristic of ECG. After the decomposition, we would select two from the detail coefficient 1~4 according to noise level and get the product of them. In the decision stage, threshold adjustment will find out candidates and then only remain the final QRS complex. The accuracy of QRS detector is verified with all recordings from the MIT-BIH arrhythmia database, respectively Se = 99.57%, +P = 99.59%.




Keywords: Adaptive threshold、QRS complex detection、wavelet transform、ECG

中文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 viii
第一章 緒論 1
1.1 研究動機 1
1.2 研究背景 2
1.3 論文架構 3
第二章 心電圖與MIT-BIH心律不整資料庫簡介 4
2.1 心電圖 4
2.1.1 心電圖之功能 7
2.1.2 QRS複合波型態介紹 7
2.2 MIT-BIH 心律不整資料庫 9
2.2.1 MIT-BIH 心律不整資料庫使用說明 10
2.2.2 MIT-BIH 心律不整資料庫各類跳動註解介紹 11
第三章 小波轉換 21
3.1 前言 21
3.2 連續小波轉換 22
3.3 離散小波轉換 24
3.4 多重解析度分析 25
3.5 Quadratic Spline Wavelet 頻率響應分析 29
第四章 QRS複合波偵測演算法 33
4.1 低通濾波器的設計 34
4.2 小波包分解的電路設計 38
4.3 雜訊程度偵測器與乘法器設計 40
4.3.1 雜訊程度偵測器 40
4.3.2 乘法器 43
4.4 適應性閥值偵測 45
4.5 分析結果 52
4.6 晶片佈局與量測 58
4.7 結果分析 62
第五章 結論與未來展望 67
5.1 結論 67
5.2 未來展望 68
第六章 參考文獻 69

[1] J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng., vol. 32, no. 3, pp. 230–236, Mar. 1985.
[2] H. M. Wang, Y. L. Lai, M. C. Hou, S. H. Lin, B. S. Yen, Y. C. Huang, L. C. Chou, S. Y. Hsu, S. C. Huang, and M. Y. Jan, “A ±6 ms-accuracy, 0.68 mm2 and 2.21 μW QRS detection ASIC,” Proc. IEEE Int. Symp. Circuits Syst. pp. 1372–1375, June, 2010.
[3] D. Saadi et al., "Automatic real-time embedded QRS complex detection for a novel patch-type electrocardiogram recorder," IEEE J. Transl. Eng. Health Med., vol. 3, May, 2015.
[4] C. Li, C. Zheng, C. Tai, “Detection of ECG characteristic points using wavelet transforms,” IEEE Trans. Biomed. Eng., vol. 42, no 1, pp. 21–28, 1995
[5] J. N. Rodrigues, T. Olsson, L. Sornmo, and V. Owall, “Digital implementation of a wavelet-based event detector for cardiac pacemakers,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 52, no. 12, pp. 2686–2698, Dec. 2005.
[6] Y. J. Min, H. K. Kim, Y. R. Kang, G. S. Kim, J. Park, and S. W Kim, “Design of wavelet-based ECG detector for implantable cardiac pacemakers,” IEEE Trans. Biomed. Circuits Syst., vol. 7, no. 4, pp. 426–436, Aug. 2013.
[7] P.E. Trahanias, “An approach to QRS complex detection using mathematical morphology,” IEEE Trans. Biomed. Eng., vol. 40, no. 2, pp. 201-205, February 1993.
[8] Y. L. Chen and H. L. Duan, “A QRS complex detection algorithm based on mathematical morphology and envelope,” in Proc. 27th Annu. Int. Conf. IEEE EMBS, Shanghai, China, Sep. 2005, pp. 4654–4657.
[9] F. Zhang and Y. Lian, “QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks,” IEEE Trans. Biomed. Circuits Syst., vol. 3, no. 4, pp. 220–228, Aug. 2009.
[10] C. F. Zhang., and T. W Bae, ”VLSI friendly ECG QRS complex detector for body sensor networks,” IEEE Journal On Emerging And Selected Topics In Circuits And Systems, vol. 2, no. 1, March 2012
[11] D. L. Donoho, “Compressed sensing” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, April, 2006.
[12] Da Poian G, Bernardini R, Rinaldo R and Clifford G D, “Matched filtering for heart rate estimation on compressive sensing ECG measurements,” IEEE Trans. Biomed. Eng., vol. 65, no. 6, June 2018
[13] C. Deepu, Y. Lian "A joint QRS detection and data compression scheme for wearable sensors," IEEE Trans. Biomed. Eng., vol. 62, no. 1, July, 2014
[14] G.M. Friesen, T.C. Jannett, M.A. Jadallah, S.L. Yates, S.R. Quint and H.T. Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Trans. Biomed. Eng., vol. 37, pp. 85-98, Jan., 1990.
[15] S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, pp. 710–732, July , 1992
[16] Schamroth, L., ‘An Introduction to Electrocardiography’ (Blackwell Science ltd, Oxford, 1990, 7th edn.)
[17] J. P. Martinez R. Almeida S. Olmos A. P. Rocha P. Laguna, “A wavelet-based ECG delineator: Evaluation on standard databases,” IEEE Trans. Biomed. Eng., vol. 51, no. 4, pp. 570-581, April, 2004.
[18] Mark RG, Schluter PS, Moody GB, Devlin, PH, Chernoff, D. “An annotated ECG database for evaluating arrhythmia detectors,” IEEE Transactions on Biomedical Engineering, Vol. 29, no. 8, pp. 205-210, 1982.
[19] Moody GB, Mark RG., “The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it,” Proceedings Computers in Cardiology, pp.185-188, Sept. 1990
[20] Moody GB, Mark RG., “The impact of the MIT-BIH Arrhythmia Database,” IEEE Eng. in Med and Biol., vol. 20, no. 3, pp. 45-50, June, 2001
[21] A. L. Goldberger A. N. Amaral L. Glass J. M. Hausdorff P. C. Ivanov R. G. Mark J. E. Mietus G. B. Moody C. K. Peng H. E. Stanley "Physiobank physiotoolkit and physionet" Circulation, vol. 101, no.23, pp. 215-220, June, 2000.
[22] P. Goupillaud, A. Grossman, and J. Morlet., “Cycle-Octave and Related Transforms in Seismic Signal Analysis.” Geoexploration, vol. 23, no.1, pp. 85-102, April, 1984
[23] S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674-693, July, 1989.
[24] Ingrid Daubechies, “The wavelet transform, time frequency localization and signal analysis,” IEEE Transactions on Information Theory, vol. 36, vo. 5, pp.961-1005, Sept., 1990.
[25] The PyWavelets Developers (2008~2018,Mar,16) “Explore Wavelets” Available:
https://pywavelets.readthedocs.io/en/latest/#
[26] N. V. Thakor, J. G. Webster and W. J. Tompkins, " Estimation of QRS complex power spectra for design of a QRS filter, " IEEE Trans. Biomed. Eng. vol. BME-31, pp. 702-706, Nov., 1984.
[27] J. A. van Alsté and T. S. Schilder, "Removal of base-line wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps," IEEE Trans. Biomed. Eng. vol. BME-32, no. 12, pp. 1052-1060, Dec., 1985.
[28] N. Ravanshad H. Rezaee-Dehsorkh R. Lotfi Y. Lian "A level-crossing based QRS-detection algorithm for wearable ECG sensors" IEEE J. Biomed. Health Inform. vol. 18 no. 1 pp. 183-192 Jan. 2014.
[29] C. I. Ieong, P. I. Mak, C. P. Lam, C. Dong, M. I. Vai, P. U. Mak, S. H. Pun, F. Wan and R. P. Martins, "A 0.83-μW QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35-μm CMOS," IEEE Trans. Biomed. Circuits Syst. vol. 6 no. 6 pp. 586-595 Dec. 2012.
[30] C. J. Deepu, X. Zhang, C.H. Heng and Yong Lian., "A 3-lead ECG-on-chip with QRS detection & lossless compression for wireless sensors," IEEE Trans. Circuits Syst. II Exp. Briefs 2016.