成功大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	莊婷喻 Chuang, Ting-Yu
論文名稱：	比較與分析用以量化癲癇特徵之心電圖參數 The Comparison and Analysis of ECG based Parameters for Characterizing the Epilepsy Seizure
指導教授：	鄭國順 Cheng, Kuo-Sheng
學位類別：	碩士 Master
系所名稱：	工學院 - 生物醫學工程學系 Department of BioMedical Engineering
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	78
中文關鍵詞：	癲癇、心電圖、近似熵
外文關鍵詞：	Epilepsy, ECG, Approximate Entropy
相關次數：	點閱：64 下載：2
分享至:	分享至facebook 分享至twitter

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

研究目的：癲癇發作是由於大腦細胞突發性不正常放電，造成患者短暫失去意識或全身性抽搐等其他發作情形。建立癲癇警示系統可及早偵測或是預測可能性癲癇，照護者或相關照護機構可以及時給藥或急救處理，並改善患者和家屬的生活品質。相關研究表示，癲癇發作時自主神經系統的調控會影響心血管系統的回饋機制，因而實驗中以心率變異度作為辨識癲癇訊號變化的指標。本研究著重於特徵化所觀察之心率變異度於癲癇發作前之異常表現。
研究方法：研究只用Physiobank資料庫中的癲癇ECG資料，含五位患者10段癲癇發作資料。分析資料長度含發作前15分鐘之區段。藉由Pan 和Tompkins提出之方法，偵測QRS複合波位置以計算R波之間的時間差。使用重疊率95%的滑動視窗以計算視窗內RRI的近似熵和其他心率變異度分析方法。統計分析心率變異分析參數之標準差值做為癲癇警示閾值設定用。
研究結果：以前三個心率變異分析參數之標準差值為基準建立癲癇警示閾值。大多數參數之閾值警示正確率為0.9，除ApEn為0.8，SDNN為0.6。同類型之心率變異分析參數，統計分析之曲線變化相近。ApEn癲癇警示結果中，在5/8段有警示的片段中，相較其他參數早出現警示。
研究結論：依結果顯示，近似熵可作為癲癇研究之心率變異度參數。因不同病理狀況有不同的規律性，癲癇偵測或預測之閾值盡量避免統一設定。心電圖相關分析參數可用於未來發展設計癲癇預測或偵測之指標。

Objectives: Epileptic seizure is an illness which patients become unconscious or develops violent movement due to the abrupt abnormal electrical activity in the brain. Establishing a warning system to predict or early detect the epilepsy seizure could lead caretakers to prompt response or emergency medical service and help patients and their family to improve the quality of life. Numerous researches have revealed the autonomic nervous system (ANS) alteration progresses to cardiac change in epilepsy, and heart rate variability (HRV) are potential biomarkers to denote the ictal change in epilepsy. This study focus on characterizing the ECG based HRV features to detect abnormality ahead seizure onset.
Methods: Overall 10 epileptic seizures ECG data were analyzed from 5 patients with epilepsy from PhysioBank Databases. Segments, covering 15 minutes before seizure onset, were chosen for analysis. By means of Pan and Tompkins’ method, the QRS complex was detected, and the time differences between consecutive R peaks were then calculated. A 95% overlapping sliding window was applied to RR interval (RRI) for further analysis, approximate entropy (ApEn), and other HRV parameters. The seizure alarm threshold was established based on standard deviation (SD) value of statistical analysis.
Results: The seizure alarm thresholds were established based on the first 3 SD value of the HRV values. The performance of the threshold mostly reached the accuracy of 0.9, except for ApEn (0.8) and SDNN (0.6). In the same group of HRV parameters, statistical analysis trends fluctuated with nearly identical patterns. The earliest alarm showed in 5 out of 8 ApEn result, better than other HRV parameters.
Conclusions: According to the result, ApEn and SampEn could examine the randomness of seizure HRV data. A one-size-fits-all threshold for seizure prediction or detection should be avoided due to the variation in pathological condition. The possibility of ECG based features becomes reliable for early seizure detection and a marker or indicator for the future warning system.

Table of Contents
中文摘要	I
Abstract	III
誌謝	V
Table of Contents	VI
LIST OF FIGURES	VIII
LIST OF TABLES	IX
Chapter 1 Introduction	1
Chapter 2 Literature Review	4
2.1 Epileptic seizure	4
2.1.1 Definition	4
2.1.2 Classification	7
2.1.3 Diagnosis and Treatment	12
2.2 Detection and Prediction	14
2.2.1 Seizure detection and prediction system	14
2.2.2 Heart Rate Variability (HRV) as an Epilepsy Biomarker	17
Chapter 3 Methods and Materials	22
3.1 Methodological Framework	22
3.2 Database	23
3.3 Experiment Flowchart	24
3.4 Feature Extraction	25
3.4.1 Pre-processing & R Peak Detection	25
3.4.2 Heart Rate Variability (HRV)	28
Approximate Entropy (ApEn)	30
Sample Entropy (SampEn)	32
Time Domain Analysis	34
Frequency Domain Analysis	34
3.5 Characterization	34
3.6 Features Comparison	36
Chapter 4 Results	37
4.1 Seizure Alarm Threshold	37
4.2 Sliding Window Analysis	41
4.3 Tolerance Value (r) for ApEn	42
Chapter 5 Discussion	44
5.1 Seizure Alarm Threshold	44
5.2 Sliding window size	47
5.3 ApEn and SampEn	48
5.4 Limitations	51
Chapter 6 Conclusion	53
References	54
Appendixes	59
Appendix A.	Seizure Alarm Thresholding	59
Appendix B.	Seizure Alarm Outcome on HRV Trend	62
Appendix C.	Seizure Alarm Outcome on Statistical Analysis Trends	64
Appendix D.	Approximate Entropy Analysis	66
Appendix E.	Sample Entropy Analysis	67
Appendix F.	SDNN Analysis	68
Appendix G.	RMSSD Analysis	69
Appendix H.	pNN50 Analysis	71
Appendix I.	Low Frequency Analysis	72
Appendix J.	High Frequency Analysis	73
Appendix K.	LF/HF ratio	74
Appendix L.	Sliding Window Sizes Comparison for HRV Analysis	76
Appendix M.	Sliding Window Sizes Comparison for Statistical Analysis	77

LIST OF FIGURES
Figure 2.1 The basic ILAE 2017 operational classification of seizure types. 1. Definitions, other seizure types, and descriptors are listed in the accompanying paper and glossary of terms. 2. Due to inadequate information or inability to place in other categories [5].	12
Figure 2.2 Components of the closed-loop warning system [17]	16
Figure 3.1 Methodological Framework	22
Figure 3.2 Experiment flowchart	24
Figure 3.3 Pan and Tompkins real-time QRS detection algorithm	27
Figure 3.4 R peak detection on health subject and epileptic patient ECG records	27
Figure 3.5 The 95% overlapping sliding window for HRV analysis	29
Figure 3.6 The 30 Hz sine wave with or without noise and the ApEn calculation result of the generated data	31
Figure 3.7 The 30 Hz sine wave with or without noise, and the SampEn calculation result of the generated data	33
Figure 3.8 The 50% overlapping sliding window for statistical analysis	36
Figure 4.1 Seizure alarm outcome on HRV trends with seizure 1	39
Figure 4.2 Seizure alarm outcome on statistical analysis trends with seizure 1	40
Figure 4.3 Seizure alarm performance	40
Figure 4.4 Sliding window sizes comparison for HRV analysis	41
Figure 4.5 Sliding window sizes comparison for statistical analysis	42
Figure 4.6 Applying Chon's method for finding rmax from maximal ApEn	43

LIST OF TABLES
Table 2.1 Definition of epilepsy revised by the ILAE in 2014 [3]	4
Table 2.2 Glossary of terms [5]	6
Table 2.3 Glossary of terms (continued) [5]	7
Table 2.4 Distribution of HRV measurement on epileptic seizure detection or prediction	18
Table 3.1 Heart rate variability parameters	26
Table 3.2 Approximate Entropy Calculation	28
Table 3.3 Sample entropy calculation	30
Table 4.1 Setting of seizure alarm threshold based on HRV parameters and the performance	34


                                    

References
[1] “WHO | Epilepsy,” WHO. [Online]. Available: http://www.who.int/mediacentre/factsheets/fs999/en/. [Accessed: 07-Sep-2017].
[2] R. S. Fisher et al., “Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE),” Epilepsia, vol. 46, no. 4, pp. 470–472, 2005.
[3] R. S. Fisher et al., “ILAE Official Report: A practical clinical definition of epilepsy,” Epilepsia, vol. 55, no. 4, pp. 475–482, Apr. 2014.
[4] M. J. Brodie, S. M. Zuberi, I. E. Scheffer, and R. S. Fisher, “The 2017 ILAE classification of seizure types and the epilepsies: what do people with epilepsy and their caregivers need to know?,” Epileptic Disorders, vol. 20, no. 2, pp. 77–87, 2018.
[5] R. S. Fisher et al., “Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology,” Epilepsia, vol. 58, no. 4, pp. 522–530, Apr. 2017.
[6] R. S. Fisher et al., “Instruction manual for the ILAE 2017 operational classification of seizure types,” Epilepsia, vol. 58, no. 4, pp. 531–542, 2017.
[7] I. E. Scheffer et al., “ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classification and Terminology,” Epilepsia, vol. 58, no. 4, pp. 512–521, 2017.
[8] “The Epilepsies: Diagnosis And Management.” [Online]. Available: http://epubseago.info/ernst-niedermeyer-the-epilepsies-diagnosis-and-management-epub-sea-full-lib.pdf. [Accessed: 26-Dec-2018].
[9] “Treating Seizures and Epilepsy,” Epilepsy Foundation. [Online]. Available: https://www.epilepsy.com/learn/treating-seizures-and-epilepsy. [Accessed: 28-May-2019].
[10] J. S. Duncan, “Epilepsy surgery,” Clin Med (Lond), vol. 7, no. 2, pp. 137–142, Apr. 2007.
[11] J. Engel, “Surgery for Seizures,” New England Journal of Medicine, vol. 334, no. 10, pp. 647–653, Mar. 1996.
[12] J. de Tisi et al., “The long-term outcome of adult epilepsy surgery, patterns of seizure remission, and relapse: a cohort study,” The Lancet, vol. 378, no. 9800, pp. 1388–1395, Oct. 2011.
[13] L. D. Iasemidis, “Epileptic seizure prediction and control,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 5, pp. 549–558, May 2003.
[14] D. R. Freestone, P. J. Karoly, and M. J. Cook, “A forward-looking review of seizure prediction,” Current Opinion in Neurology, vol. 30, no. 2, pp. 167–173, Apr. 2017.
[15] M. Amengual-Gual, I. Sánchez Fernández, and T. Loddenkemper, “Patterns of epileptic seizure occurrence,” Brain Research, vol. 1703, pp. 3–12, Jan. 2019.
[16] S. Ramgopal et al., “Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy,” Epilepsy & Behavior, vol. 37, pp. 291–307, Aug. 2014.
[17] A. Ulate-Campos, F. Coughlin, M. Gaínza-Lein, I. S. Fernández, P. L. Pearl, and T. Loddenkemper, “Automated seizure detection systems and their effectiveness for each type of seizure,” Seizure - European Journal of Epilepsy, vol. 40, pp. 88–101, Aug. 2016.
[18] J. Engel et al., “EPILEPSY BIOMARKERS,” Epilepsia, vol. 54, no. 0 4, pp. 61–69, Aug. 2013.
[19] S. Cerutti, A. L. Goldberger, and Y. Yamamoto, “Recent Advances in Heart Rate Variability Signal Processing and Interpretation,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 1, pp. 1–3, Jan. 2006.
[20] T. F. of the E. S. of C. the N. A. S. of P. Electrophysiology, “Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use,” Circulation, vol. 93, no. 5, pp. 1043–1065, Mar. 1996.
[21] “New Human Physiology Ch 6.” [Online]. Available: http://www.zuniv.net/physiology/book/chapter6.html. [Accessed: 30-Apr-2019].
[22] “Overview of the Autonomic Nervous System - Brain, Spinal Cord, and Nerve Disorders,” MSD Manual Consumer Version. [Online]. Available: https://www.msdmanuals.com/home/brain,-spinal-cord,-and-nerve-disorders/autonomic-nervous-system-disorders/overview-of-the-autonomic-nervous-system. [Accessed: 30-Apr-2019].
[23] C. Varon, K. Jansen, L. Lagae, and S. Van Huffel, “Can ECG monitoring identify seizures?,” Journal of Electrocardiology, vol. 48, no. 6, pp. 1069–1074, Nov. 2015.
[24] Sevcencu Cristian and Struijk Johannes J., “Autonomic alterations and cardiac changes in epilepsy,” Epilepsia, vol. 51, no. 5, pp. 725–737, Apr. 2010.
[25] K. Jansen et al., “Autonomic effects of refractory epilepsy on heart rate variability in children: influence of intermittent vagus nerve stimulation,” Developmental Medicine & Child Neurology, vol. 53, no. 12, pp. 1143–1149, Dec. 2011.
[26] K. S. Eggleston, B. D. Olin, and R. S. Fisher, “Ictal tachycardia: The head–heart connection,” Seizure, vol. 23, no. 7, pp. 496–505, Aug. 2014.
[27] S. Behbahani, N. J. Dabanloo, A. M. Nasrabadi, C. A. Teixeira, and A. Dourado, “Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses,” Anadolu Kardiyoloji Dergisi/The Anatolian Journal of Cardiology, Oct. 2013.
[28] H. Hashimoto et al., “Heart rate variability features for epilepsy seizure prediction,” in 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013, pp. 1–4.
[29] M. K. Moridani and H. Farhadi, “Heart rate variability as a biomarker for epilepsy seizure prediction,” Bratislava Medical Journal, vol. 118, no. 01, pp. 3–8, 2017.
[30] A. Ghosh, A. Sarkar, T. Das, and P. Basak, “Pre-ictal epileptic seizure prediction based on ECG signal analysis,” in 2017 2nd International Conference for Convergence in Technology (I2CT), 2017, pp. 920–925.
[31] J. Pavei et al., “Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics,” Front. Physiol., vol. 8, 2017.
[32] T. D. Cooman, E. Carrette, P. Boon, A. Meurs, and S. V. Huffel, “Online seizure detection in adults with temporal lobe epilepsy using single-lead ECG,” in 2014 22nd European Signal Processing Conference (EUSIPCO), 2014, pp. 1532–1536.
[33] T. De Cooman, C. Varon, B. Hunyadi, W. Van Paesschen, L. Lagae, and S. Van Huffel, “Online Automated Seizure Detection in Temporal Lobe Epilepsy Patients Using Single-lead ECG,” Int. J. Neur. Syst., vol. 27, no. 07, p. 1750022, Feb. 2017.
[34] G. B. Moody, R. G. Mark, and A. L. Goldberger, “PhysioNet: a research resource for studies of complex physiologic and biomedical signals,” in Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163), 2000, pp. 179–182.
[35] Goldberger Ary L. et al., “PhysioBank, PhysioToolkit, and PhysioNet,” Circulation, vol. 101, no. 23, pp. e215–e220, Jun. 2000.
[36] I. C. Al-Aweel et al., “Postictal heart rate oscillations in partial epilepsy,” Neurology, vol. 53, no. 7, pp. 1590–1590, Oct. 1999.
[37] J. Pan and W. J. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering, vol. BME-32, no. 3, pp. 230–236, Mar. 1985.
[38] U. R. Acharya, Advances in Cardiac Signal Processing. Berlin Heidelberg: Springer-Verlag, 2007.
[39] M. Peltola, “Role of editing of R-R intervals in the analysis of heart rate variability,” Front. Physiol., vol. 3, 2012.
[40] F. Shaffer and J. P. Ginsberg, “An Overview of Heart Rate Variability Metrics and Norms,” Front Public Health, vol. 5, Sep. 2017.
[41] S. Pincus, “Approximate entropy (ApEn) as a complexity measure,” Chaos, vol. 5, no. 1, pp. 110–117, Mar. 1995.
[42] C. C. Mayer, M. Bachler, M. Hörtenhuber, C. Stocker, A. Holzinger, and S. Wassertheurer, “Selection of entropy-measure parameters for knowledge discovery in heart rate variability data,” BMC Bioinformatics, vol. 15, no. 6, p. S2, May 2014.
[43] J. M. Yentes, N. Hunt, K. K. Schmid, J. P. Kaipust, D. McGrath, and N. Stergiou, “The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets,” Ann Biomed Eng, vol. 41, no. 2, pp. 349–365, Feb. 2013.
[44] S. M. Pincus, “Approximate entropy as a measure of system complexity.,” Proceedings of the National Academy of Sciences, vol. 88, no. 6, pp. 2297–2301, Mar. 1991.
[45] J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039–H2049, Jun. 2000.
[46] J. S. Richman, D. E. Lake, and J. R. Moorman, “Sample Entropy,” in Methods in Enzymology, vol. 384, Academic Press, 2004, pp. 172–184.
[47] W. He, P.-J. Rong, L. Li, H. Ben, B. Zhu, and G. Litscher, “Auricular Acupuncture May Suppress Epileptic Seizures via Activating the Parasympathetic Nervous System: A Hypothesis Based on Innovative Methods,” Evidence-Based Complementary and Alternative Medicine, 2012. [Online]. Available: https://www.hindawi.com/journals/ecam/2012/615476/abs/. [Accessed: 03-Jul-2019].
[48] P. A. Lotufo, L. Valiengo, I. M. Benseñor, and A. R. Brunoni, “A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs,” Epilepsia, vol. 53, no. 2, pp. 272–282, Feb. 2012.
[49] K. Jansen and L. Lagae, “Cardiac changes in epilepsy,” Seizure, vol. 19, no. 8, pp. 455–460, Oct. 2010.
[50] E. Ronkainen, “Suppressed circadian heart rate dynamics in temporal lobe epilepsy,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 76, no. 10, pp. 1382–1386, Oct. 2005.
[51] Y. Oda, T. Sato, I. Nambu, and Y. Wada, “Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis,” Applied Sciences, vol. 8, no. 1, p. 149, Jan. 2018.
[52] U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, “Heart rate variability: a review,” Med Bio Eng Comput, vol. 44, no. 12, pp. 1031–1051, Dec. 2006.
[53] U. R. Acharya, H. Fujita, V. K. Sudarshan, S. Bhat, and J. E. W. Koh, “Application of entropies for automated diagnosis of epilepsy using EEG signals: A review,” Knowledge-Based Systems, vol. 88, pp. 85–96, Nov. 2015.
[54] V. S. Vijith, J. E. Jacob, T. Iype, K. Gopakumar, and D. G. Yohannan, “Epileptic seizure detection using non linear analysis of EEG,” in 2016 International Conference on Inventive Computation Technologies (ICICT), 2016, vol. 3, pp. 1–6.
[55] N. Arunkumar, K. Ramkumar, and V. Venkataraman, “A moving window approximate entropy in wavelet framework for automatic detection of the onset of epileptic seizures,” Biomedical Research, vol. 29, no. 0, pp. 161–170, Jun. 2017.
[56] K. H. Chon, C. G. Scully, and S. Lu, “Approximate entropy for all signals,” IEEE Engineering in Medicine and Biology Magazine, vol. 28, no. 6, pp. 18–23, Nov. 2009.
[57] S. Lu $^*$, X. Chen, J. K. Kanters, I. C. Solomon, and K. H. Chon, “Automatic Selection of the Threshold Value$r$for Approximate Entropy,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 1966–1972, Aug. 2008.
[58] C. Liu et al., “Comparison of different threshold values r for approximate entropy: application to investigate the heart rate variability between heart failure and healthy control groups,” Physiol. Meas., vol. 32, no. 2, p. 167, 2011.
[59] K. Gadhoumi, J.-M. Lina, F. Mormann, and J. Gotman, “Seizure prediction for therapeutic devices: A review,” Journal of Neuroscience Methods, vol. 260, pp. 270–282, Feb. 2016.

校內：2023-09-01公開
校外：2023-09-01公開

簡易檢索 / 詳目顯示

相關論文