實踐經驗模態分解於高度非穩態生理訊號之訊號特徵擷取

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：19

、訪客IP：3.144.109.5

姓名

王子文(Zih-Wen Wang) 查詢紙本館藏

畢業系所

生物醫學工程研究所

論文名稱

實踐經驗模態分解於高度非穩態生理訊號之訊號特徵擷取
(Harnessing Empirical Mode Decomposition on Feature Extraction of Highly Nonstationary Biomedical Signals)

相關論文

★ 不同麻醉深度之相位-振幅耦合量測及強度比較	★ 設計及製作可攜式非侵入性心搏輸出量監測系統
★ 開發可攜式十二導程心電圖和聲學雙功能系統於居家分析心臟電生理訊號	★ 應用非侵入性方法來探究透析過程中血流動力學變化及心血管疾病之預後
★ 開發具深度學習應用於自動追蹤耳膜功能之數位耳鏡於中耳炎輔助系統	★ 反覆編曲結構音樂對人體生理訊號之影響
★ 設計具低功耗無線傳輸及結合人工智慧判讀之長時間聽診監測系統	★ Exploring Beat-to-Beat Photoplethysmography Features at the Upper and Lower Extremities as Potential Biomarkers for Early Diagnosis of Peripheral Arterial Occlusive Disease: A Comparative study with Ultrasound Doppler and Ankle-Brachial Index
★ 應用稀疏時頻表現式解析生理系統間非線性耦合機轉	★ 基於功能性近紅外光腦光譜與腦電圖發展多模態腦活動無線監測系統
★ 脂質奈米顆粒在mRNA疫苗技術應用發展綜述	★ 自12導程心電圖擷取P波特徵辨識竇性心律下之心房顫動高風險病患
★ 探討以非侵入方式的影像心衝擊圖形態特徵來評估左心室射血分數 : 基於數學模型與臨床驗證的初步研究	★ 發展可用於肺部疾病患者呼吸音監測之高解析加速度陣列感測器
★ 以光體積變化描記儀作為男性勃起功能及陰莖血液動力學及性功能客觀指標之探討

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著科技的進步與蓬勃發展，穿戴式裝置、微型化裝置以及智慧型手機已然成為普及世界的存在，藉由這些便攜型的非侵入式與智慧型裝置來長期量測生物醫學訊號是未來醫療領域的發展主軸之一，除有利於長期監測及個人化醫療，對於遠距醫療與居家檢測的助益也十分可觀；有鑑於日常生活中擷取的生理訊號具有非穩態與非線性特質，因此對該類訊號進行分析甚至是特徵擷取與指標量化計算有其難度，本研究目的在於應用經驗模態分解於數種生物醫學訊號相關主題，包含：(1)發展普適性峰值檢測演算法並應用於心電圖、光體積變化描記圖、動脈血壓波以及遠端光體積變化描記圖與 (2) 應用日常手機使用量於作息節律監控與睡眠分析。
經驗模態分解是一種多尺度、自適應性的訊號處理技巧，由於該方法的基底函數取決於數據本身的波動特性，所以可保留訊號的局部特性，因此被廣泛應用於非穩態與非線性訊號解析；透過經驗模態分解，訊號可被解構成數個在不同時間尺度下的本質模態函數，而每一個本質模態函數都具有不同的物理意義，針對不同主題的研究而言，則可進一步篩選出特定的本質模態函數以利後續分析。
傳統上既有的峰值檢測演算法大多奠基於設定自適應性閾值並結合繁複的篩選條件，而現今以機器學習為基底的演算法會受到訓練資料集多樣性與跨資料庫問題；此外，現有演算法中仍無可適用於各類生理訊號峰值檢測的統一框架，因此本研究回歸生理訊號的類週期性本質，將此特質與經驗模態分解結合以發展出能夠應用於多種生理訊號的檢測演算法。而手機使用行為分析則是以手機為媒介紀錄日常手機使用行為，並量化該紀錄成數位訊號，透過經驗模態分解來萃取出隱藏在資料中的晝夜節律與行為模式，從中推算出睡眠相關指標。
本方法克服了以往訊號特徵擷取時會遇到的斷點問題，在提取主要成分的過程中不須仰賴繁複的閾值設定與條件式，而是自適應性的決定濾波器頻帶並擷取出訊號的主要成分，此方法除可去蕪存菁的萃取出特定頻帶的成分外，也比現有方法更能完整呈現該成分在頻率與振幅的非穩態特性，此獨特性使本研究中的兩個主題在臨床上的應用價值與助益大幅增加。

摘要(英)

With the global prevalence of the wearable and portable devices in recent years, we can achieve long-term monitoring, telemedicine or even individualized treatment with the assistance of cloud computing. Nevertheless, the biomedical signals gathered from these wearable and portable gadgets are prone to be contaminated by various kinds of perturbation, such as motion artifacts, intermittency, baseline wandering or white noise, thereby causing further analysis, including feature extraction and finding indicators, to be more complicated.
In this study, we develop a distinctive method based on the quasi-periodicity and Empirical Mode Decomposition (EMD)-based method, Uniform Phase EMD (UPEMD), to adopt the nonlinearity and adaptiveness on biomedical signal analysis. This study aims at applying our proposed method in two biomedical topics, the first one is to develop a universal beat detection algorithm that is suitable for different types of biomedical signals, such as electrocardiography (ECG), photoplethysmography (PPG), arterial blood pressure (ABP) and remote photoplethysmography (Remote PPG). The second topic is harnessing our proposed technique on the smartphone usage records to estimate the sleep-wake cycles as well as the sleep parameters.
In view of the non-stationary property in the physiological signals, EMD tends to be a satisfactory choice that can accommodate the unpredicted variations. The rationale of EMD is to segregate the signal into a finite number of Intrinsic Mode Function (IMF) through the iterative sifting processes, meanwhile considering the time scale characteristics of the data. To put it bluntly, EMD serves as a data-driven technique rather than relying on a priori basis. Our proposed method not merely avoid the cumbersome and rigorous statements and threshold settings of the existing algorithms but also automatically and adaptively decide the filter bank to extract the specific component, meanwhile retaining the nonstationary and nonlinear properties of the physiological signal without being interfered by the intermittency.

關鍵字(中)

★ 經驗模態分解
★ 峰值檢測
★ 自適應性
★ 晝夜節律
★ 睡眠分析

關鍵字(英)

★ Empirical Mode Decomposition (EMD)
★ beat detection
★ adaptive
★ circadian rhythm
★ sleep analysis

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 viii
第一章緒論 1
1-1 序言 1
1-2 本文架構 1
第二章經驗模態分解 2
2-1 前言 2
2-2 經驗模態分解 2
2-3 自適應性特質解析 3
2-4 模態混疊與模態分裂 5
2-5 EMD延伸方法 5
2-5-1均相經驗模態分解法(Uniform Phase EMD, UPEMD) 6
第三章普適性峰值檢測演算法 8
3-1 研究動機 8
3-2 相關研究 9
3-2-1 Pan Tompkins 演算法與Wavelet-based 演算法 9
3-2-2 深度學習應用於峰值檢測 11
3-3 研究方法 12
3-3-1 預處理(Preprocessing) 13
3-3-2 雙層均相經驗模態分解(Two-fold UPEMD) 14
3-3-3 相位校準(Phase calibration) 17
3-4 實驗結果 20
3-4-1 前言 20
3-4-2 強度檢測 21
3-4-3 相位校準運作原理之驗證 27
3-4-4 四種臨床訊號的峰值檢測效能測試(ECG, PPG, ABP, Remote PPG) 28
3-5 結論與未來展望 37
第四章基於手機使用量實踐作息節律監控與睡眠分析 39
4-1 研究動機 39
4-2 活動量常見分析法 40
4-2-1 餘弦分析法 (Cosinor analysis) 40
4-2-2 無母數分析 (Nonparametric analysis) 42
4-2-3 經驗模態分解法 (EMD) 45
4-3 研究方法 46
4-3-1 前言與實驗設計 46
4-3-2 量化手機使用記錄為時序生理訊號—手機使用量 47
4-3-3 基於手機使用量實踐雙層均相經驗模態分解於晝夜節律推估 49
4-3-4 基於晝夜節律推算睡眠相關參數 51
4-4 研究結果 53
4-4-1 睡眠相關參數之統計分析 53
4-4-2 睡眠時間疊合度分析 56
4-5 結論與未來展望 59
參考文獻 61

參考文獻

[1] V. Sygouni et al., "Capillary pressure spectrometry: Toward a new method for the measurement of the fractional wettability of porous media," Physics of Fluids, vol. 18, pp. 053302-053302-15, May 01, 2006 2006, doi: 10.1063/1.2203667.
[2] H. Sedghamiz, "Matlab implementation of Pan Tompkins ECG QRS detector," Code Available at the File Exchange Site of MathWorks, 2014.
[3] N. V. Thakor et al., "Estimation of QRS complex power spectra for design of a QRS filter," IEEE Transactions on biomedical engineering, no. 11, pp. 702-706, 1984.
[4] D. A. Guzmán et al., "The fractal organization of ultradian rhythms in avian behavior," Scientific reports, vol. 7, no. 1, pp. 1-13, 2017.
[5] J. P. Martínez et al., "A wavelet-based ECG delineator: evaluation on standard databases," IEEE Transactions on biomedical engineering, vol. 51, no. 4, pp. 570-581, 2004.
[6] L. A. Brown et al., "Telling the Time with a Broken Clock: Quantifying Circadian Disruption in Animal Models," Biology, vol. 8, no. 1, p. 18, 2019. [Online]. Available: https://www.mdpi.com/2079-7737/8/1/18.
[7] Y.-H. Wang et al., "The inner structure of empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, vol. 462, pp. 1003-1017, 2016/11/15/ 2016, doi: https://doi.org/10.1016/j.physa.2016.06.112.
[8] N. E. Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, vol. 454, no. 1971, pp. 903-995, 1998.
[9] P. Flandrin et al., "Empirical mode decomposition as a filter bank," IEEE Signal Processing Letters, vol. 11, no. 2, pp. 112-114, 2004, doi: 10.1109/LSP.2003.821662.
[10] G. Rilling and P. Flandrin, "One or Two Frequencies? The Empirical Mode Decomposition Answers," IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 85-95, 2008, doi: 10.1109/TSP.2007.906771.
[11] H. Yang et al., "A Minimum Arclength Method for Removing Spikes in Empirical Mode Decomposition," IEEE Access, vol. 7, pp. 13284-13294, 2019, doi: 10.1109/ACCESS.2019.2892622.
[12] A. Aldroubi et al., "Cardinal spline filters: Stability and convergence to the ideal sinc interpolator," Signal Processing, vol. 28, no. 2, pp. 127-138, 1992/08/01/ 1992, doi: https://doi.org/10.1016/0165-1684(92)90030-Z.
[13] Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise-assisted data analysis method," Advances in adaptive data analysis, vol. 1, no. 01, pp. 1-41, 2009.
[14] R. Deering and J. F. Kaiser, "The use of a masking signal to improve empirical mode decomposition," in Proceedings. (ICASSP ′05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 23-23 March 2005 2005, vol. 4, pp. iv/485-iv/488 Vol. 4, doi: 10.1109/ICASSP.2005.1416051.
[15] J.-R. Yeh et al., "Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method," Advances in adaptive data analysis, vol. 2, no. 02, pp. 135-156, 2010.
[16] M. E. Torres et al., "A complete ensemble empirical mode decomposition with adaptive noise," in 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2011: IEEE, pp. 4144-4147.
[17] M. A. Colominas et al., "Improved complete ensemble EMD: A suitable tool for biomedical signal processing," Biomedical Signal Processing and Control, vol. 14, pp. 19-29, 2014.
[18] Y. Yang et al., "An improved empirical mode decomposition by using dyadic masking signals," Signal, Image and Video Processing, vol. 9, no. 6, pp. 1259-1263, 2015.
[19] W.-C. Shen et al., "Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT," Digital Signal Processing, vol. 24, pp. 170-186, 2014.
[20] Y. H. Wang et al., "Uniform Phase Empirical Mode Decomposition: An Optimal Hybridization of Masking Signal and Ensemble Approaches," IEEE Access, vol. 6, pp. 34819-34833, 2018, doi: 10.1109/ACCESS.2018.2847634.
[21] W. Majeed et al., "Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans," Neuroimage, vol. 54, no. 2, pp. 1140-1150, 2011.
[22] P. Kligfield et al., "Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology," Journal of the American College of Cardiology, vol. 49, no. 10, pp. 1109-1127, 2007.
[23] M. Elgendi, "On the analysis of fingertip photoplethysmogram signals," Current cardiology reviews, vol. 8, no. 1, pp. 14-25, 2012.
[24] K. H. Shelley, "Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate," Anesthesia & Analgesia, vol. 105, no. 6, pp. S31-S36, 2007.
[25] A. P. Avolio et al., "Arterial blood pressure measurement and pulse wave analysis--their role in enhancing cardiovascular assessment," (in eng), Physiol Meas, vol. 31, no. 1, pp. R1-47, Jan 2010, doi: 10.1088/0967-3334/31/1/r01.
[26] E. G. Daoud et al., "Effect of an irregular ventricular rhythm on cardiac output," The American journal of cardiology, vol. 78, no. 12, pp. 1433-1436, 1996.
[27] T. Ma and Y.-T. Zhang, "A correlation study on the variabilities in pulse transit time, blood pressure, and heart rate recorded simultaneously from healthy subjects," in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2006: IEEE, pp. 996-999.
[28] M. J. Drinnan et al., "Relation between heart rate and pulse transit time during paced respiration," Physiological measurement, vol. 22, no. 3, p. 425, 2001.
[29] K. Sutton-Tyrrell et al., "Elevated aortic pulse wave velocity, a marker of arterial stiffness, predicts cardiovascular events in well-functioning older adults," Circulation, vol. 111, no. 25, pp. 3384-3390, 2005.
[30] X. Li et al., "Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information," PLoS biology, vol. 15, no. 1, p. e2001402, 2017.
[31] O. Pahlm and L. Sörnmo, "Software QRS detection in ambulatory monitoring—a review," Medical and Biological Engineering and Computing, vol. 22, no. 4, pp. 289-297, 1984.
[32] R. Balda and G. Diller, "The HP ECG Analysis Program. Trends in Computer Processing Electrocardiograms," ed: North-Holland, Amsterdam, 1977.
[33] I. Duskalov et al., "Developments in ECG acquisition, preprocessing, parameter measurement, and recording," IEEE Engineering in Medicine and Biology Magazine, vol. 17, no. 2, pp. 50-58, 1998.
[34] I. S. Murthy and M. R. Rangaraj, "New concepts for PVC detection," IEEE Transactions on Biomedical Engineering, no. 7, pp. 409-416, 1979.
[35] H. S. Shin et al., "Adaptive threshold method for the peak detection of photoplethysmographic waveform," Computers in biology and medicine, vol. 39, no. 12, pp. 1145-1152, 2009.
[36] I. I. Christov, "Real time electrocardiogram QRS detection using combined adaptive threshold," Biomedical engineering online, vol. 3, no. 1, pp. 1-9, 2004.
[37] J. Pan and W. J. Tompkins, "A real-time QRS detection algorithm," IEEE transactions on biomedical engineering, no. 3, pp. 230-236, 1985.
[38] P. S. Hamilton and W. J. Tompkins, "Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database," IEEE transactions on biomedical engineering, no. 12, pp. 1157-1165, 1986.
[39] Y. Xiang et al., "Automatic QRS complex detection using two-level convolutional neural network," Biomedical engineering online, vol. 17, no. 1, pp. 1-17, 2018.
[40] A. Y. Hannun et al., "Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network," Nature medicine, vol. 25, no. 1, pp. 65-69, 2019.
[41] D. Lai et al., "Single Lead ECG-based Ventricular Repolarization Classification for Early Identification of Unexpected Ventricular Fibrillation*," in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 20-24 July 2020 2020, pp. 5567-5570, doi: 10.1109/EMBC44109.2020.9176355.
[42] W. J. Tompkins, "Biomedical digital signal processing," Editorial Prentice Hall, 1993.
[43] R. Courant and D. Hilbert, Methods of Mathematical Physics: Partial Differential Equations. John Wiley & Sons, 2008.
[44] D. S. Benitez et al., "A new QRS detection algorithm based on the Hilbert transform," in Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163), 24-27 Sept. 2000 2000, pp. 379-382, doi: 10.1109/CIC.2000.898536.
[45] Y. Maki et al., "Inter-Beat Interval Estimation from Facial Video Based on Reliability of BVP Signals," in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 23-27 July 2019 2019, pp. 6525-6528, doi: 10.1109/EMBC.2019.8857081.
[46] W. Wang et al., "Algorithmic Principles of Remote PPG," IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1479-1491, 2017, doi: 10.1109/TBME.2016.2609282.
[47] C. Vetter et al., "Association Between Rotating Night Shift Work and Risk of Coronary Heart Disease Among Women," (in eng), Jama, vol. 315, no. 16, pp. 1726-34, Apr 26 2016, doi: 10.1001/jama.2016.4454.
[48] J. Bass and J. S. Takahashi, "Circadian integration of metabolism and energetics," (in eng), Science, vol. 330, no. 6009, pp. 1349-54, Dec 3 2010, doi: 10.1126/science.1195027.
[49] S. Davis et al., "Night shift work, light at night, and risk of breast cancer," (in eng), J Natl Cancer Inst, vol. 93, no. 20, pp. 1557-62, Oct 17 2001, doi: 10.1093/jnci/93.20.1557.
[50] E. S. Musiek et al., "Circadian rest-activity pattern changes in aging and preclinical Alzheimer disease," JAMA neurology, vol. 75, no. 5, pp. 582-590, 2018.
[51] E. S. Musiek and D. M. Holtzman, "Mechanisms linking circadian clocks, sleep, and neurodegeneration," Science, vol. 354, no. 6315, pp. 1004-1008, 2016.
[52] A. Wirz-Justice, "Biological rhythm disturbances in mood disorders," International clinical psychopharmacology, vol. 21, pp. S11-S15, 2006.
[53] C. A. McClung, "Circadian genes, rhythms and the biology of mood disorders," Pharmacology & therapeutics, vol. 114, no. 2, pp. 222-232, 2007.
[54] L. D. Grandin et al., "The social zeitgeber theory, circadian rhythms, and mood disorders: review and evaluation," Clinical psychology review, vol. 26, no. 6, pp. 679-694, 2006.
[55] J.-P. Onnela and S. L. Rauch, "Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health," Neuropsychopharmacology, vol. 41, no. 7, pp. 1691-1696, 2016/06/01 2016, doi: 10.1038/npp.2016.7.
[56] N. Bidargaddi et al., "Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies," Molecular psychiatry, vol. 22, no. 2, pp. 164-169, 2017.
[57] R. J. Cole et al., "Automatic sleep/wake identification from wrist activity," (in eng), Sleep, vol. 15, no. 5, pp. 461-9, Oct 1992, doi: 10.1093/sleep/15.5.461.
[58] G. Cornelissen, "Cosinor-based rhythmometry," Theoretical Biology and Medical Modelling, vol. 11, no. 1, pp. 1-24, 2014.
[59] W. Nelson et al., "Methods for cosinor-rhythmometry," (in eng), Chronobiologia, vol. 6, no. 4, pp. 305-23, Oct-Dec 1979.
[60] W. Witting et al., "Alterations in the circadian rest-activity rhythm in aging and Alzheimer′s disease," Biological psychiatry, vol. 27, no. 6, pp. 563-572, 1990.
[61] E. J. W. Van Someren et al., "Long-Term Fitness Training Improves the Circadian Rest-Activity Rhythm in Healthy Elderly Males," Journal of Biological Rhythms, vol. 12, no. 2, pp. 146-156, 1997, doi: 10.1177/074873049701200206.
[62] E. J. Van Someren et al., "Bright light therapy: improved sensitivity to its effects on rest-activity rhythms in Alzheimer patients by application of nonparametric methods," (in eng), Chronobiol Int, vol. 16, no. 4, pp. 505-18, Jul 1999, doi: 10.3109/07420529908998724.
[63] J. L. Wang et al., "Suprachiasmatic neuron numbers and rest-activity circadian rhythms in older humans," (in eng), Ann Neurol, vol. 78, no. 2, pp. 317-22, Aug 2015, doi: 10.1002/ana.24432.

指導教授

林澂(Chen Lin)

審核日期

2021-7-23

推文