請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 趙福杉,廖文劍 | |
| dc.contributor.author | Yi-Fu Wang | en |
| dc.contributor.author | 王詣富 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:31:04Z | - |
| dc.date.available | 2021-08-19 | |
| dc.date.copyright | 2018-08-19 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-16 | |
| dc.identifier.citation | [1] J. Sztajzel, 'Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system,' Swiss medical weekly, vol. 134, no. 35-36, pp. 514-522, 2004.
[2] S. Akselrod, D. Gordon, F. A. Ubel, D. C. Shannon, A. Berger, and R. J. Cohen, 'Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control,' science, vol. 213, no. 4504, pp. 220-222, 1981. [3] M. Malik, 'Heart rate variability: standards of measurement, physiological interpretation, and clinical use: task force of the European society of cardiology and the north American society for pacing and electrophysiology,' Annals of Noninvasive Electrocardiology, vol. 1, no. 2, pp. 151-181, 1996. [4] 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, 1991. [5] 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, 2000. [6] M. Costa, A. L. Goldberger, and C.-K. Peng, 'Multiscale entropy analysis of biological signals,' Physical review E, vol. 71, no. 2, p. 021906, 2005. [7] G. Gabella, 'Autonomic nervous system,' e LS, 2001. [8] J. B. Furness, 'The organisation of the autonomic nervous system: peripheral connections,' Autonomic Neuroscience: Basic and Clinical, vol. 130, no. 1, pp. 1-5, 2006. [9] J. L. Chen, H. W. Chiu, Y. J. Tseng, and W. C. Chu, 'Hyperthyroidism is characterized by both increased sympathetic and decreased vagal modulation of heart rate: evidence from spectral analysis of heart rate variability,' Clinical endocrinology, vol. 64, no. 6, pp. 611-616, 2006. [10] W.-C. Liao and F.-S. Jaw, 'A noninvasive evaluation of autonomic nervous system dysfunction in women with an overactive bladder,' International Journal of Gynecology & Obstetrics, vol. 110, no. 1, pp. 12-17, 2010. [11] A. N. Buch, J. H. Coote, and J. N. Townend, 'Mortality, cardiac vagal control and physical training–what’s the link?,' Experimental physiology, vol. 87, no. 4, pp. 423-435, 2002. [12] R. D. Berger, S. Akselrod, D. Gordon, and R. J. Cohen, 'An efficient algorithm for spectral analysis of heart rate variability,' IEEE Transactions on biomedical engineering, no. 9, pp. 900-904, 1986. [13] E. Kishida, Y. Kato, H. Mizuta, K. Yana, M. Kyoso, M. Ishijima, Y. Ishihara, T. Ito, K. Tamura, 'Reconstruction of underlying heart rate from integrated pulse frequency modulated heart pulses with 1/f fluctuations,' in Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE, 2003, vol. 1, pp. 244-247: IEEE. [14] S. L. Marple Jr and W. M. Carey, 'Digital spectral analysis with applications,' ed: ASA, 1989. [15] D. T. Lee and A. Yamamoto, 'Wavelet analysis: theory and applications,' Hewlett Packard journal, vol. 45, pp. 44-44, 1994. [16] J. Burggraaf, JHM. Tulen, S. Lalezari, RC. Schoemaker, PHEM. De Meyer, AE. Meinders, AF.Cohen, H. Pijl, 'Sympathovagal imbalance in hyperthyroidism,' American Journal of Physiology-Endocrinology And Metabolism, vol. 281, no. 1, pp. E190-E195, 2001. [17] T. Peçanha, M. d. Paula-Ribeiro, O. Nasario-Junior, and J. R. P. d. Lima, 'Post-exercise heart rate variability recovery: a time-frequency analysis,' Acta cardiologica, vol. 68, no. 6, pp. 607-613, 2013. [18] E. Vanoli, G. M. De Ferrari, M. Stramba-Badiale, S. S. Hull, R. D. Foreman, and P. J. Schwartz, 'Vagal stimulation and prevention of sudden death in conscious dogs with a healed myocardial infarction,' Circulation research, vol. 68, no. 5, pp. 1471-1481, 1991. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72245 | - |
| dc.description.abstract | 自律神經系統中的交感神經、副交感神經是調節全身器官、腺體等的重要系統，能透過心率變異度分析、多尺度熵分析來評估器官或自律神經系統是否異常或良好，並將分析方法植入於智慧型手機中，以提升使用者之方便性。因此，本研究先於筆記型電腦上開發其分析程式，其功能包括時域分析、頻域分析、時頻分析、多尺度熵分析等，並針對甲狀腺、膀胱、心臟進行實驗並評估個臟器之功能，確認此分析程式完善正確後，再將其移植至手機程式中，而由於手機上之運算能力不足，僅採用時域分析、頻域分析、多尺度熵分析。本研究透過實驗結果並與文獻資料進行比對，完成正確性驗證後，完成手持式生理信號分析程式。 | zh_TW |
| dc.description.abstract | The sympathetic nerves and the parasympathetic nerves in the autonomic nervous system is for regulation of the internal organs and the glands. Analysis of the heart rate variability and multi-scale entropy can be used to assess whether organ or autonomic nervous system is normal or abnormal. This study attempts to develop the analysis program on a notebook, its function include time domain analysis, frequency domain analysis, time-frequency domain analysis and multi-scale entropy. The experiments and evaluations are then compared with the thyroid, bladder, and heart to confirm the analysis program. After the analysis program is confirmed, the analysis method implement on the smart phone. Due to the lack of the computation power on the smart phone, only the time domain analysis, frequency domain analysis, and multi-scale entropy analysis will be implemented on the smart phone program. This study compares the results of the experiment with the literatures, completing the app-based analysis program for physiological signals. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:31:04Z (GMT). No. of bitstreams: 1 ntu-107-R05548025-1.pdf: 1722223 bytes, checksum: bc83c7a4f85609730e18c98cebc71d1f (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii 一、緒論 1 1.1 研究背景與目的 1 1.2 心率變異度與甲狀腺亢進之關係 2 1.3 心率變異度與膀胱排尿之關係 2 1.4 心率變異度與心臟功能之關係 2 1.5 多尺度熵與心臟功能之關係 3 二、研究方法 4 2.1 量測方法與設備 5 2.1.1 心電圖儀 5 2.1.2 甲狀腺亢進評估實驗 5 2.1.3 心臟功能檢測實驗 6 2.1.4 膀胱排尿評估實驗 7 2.2信號分析 7 2.2.1 心率變異度(HRV)分析 8 2.2.2 心跳重新取樣 9 2.2.3 自回歸模型 10 2.2.4 小波轉換(Wavelet Transform) 10 2.2.4.1 連續小波轉換 (Continuous Wavelet Transforms, CWT) 10 2.2.4.2 離散小波轉換 (Discrete Wavelet Transform, DWT) 11 2.2.5 多尺度熵 11 2.2.5.1 近似熵 11 2.2.5.2 樣本熵 12 2.2.5.3 多尺度熵 13 2.3 實現於智慧型手機 14 三、 實驗結果 15 3.1 心律變異度與多尺度熵之分析程式 15 3.2 頻譜分析驗證 17 3.2.1 快速傅立葉轉換程式之驗證 17 3.2.2 自回歸分析驗證 18 3.3 生理功能評估實驗 19 3.3.1 甲狀腺功能評估實驗 19 3.3.2 膀胱功能評估實驗 22 3.3.3 心臟功能評估實驗 24 3.4 手持式分析程式 27 3.4.1 頻譜分析驗證 27 3.4.2 多尺度熵分析驗證 28 四、討論 29 4.1 甲狀腺功能評估實驗 29 4.2 膀胱功能評估實驗 29 4.3心臟功能評估實驗 30 4.4 手持式分析程式 30 參考文獻 31 | |
| dc.language.iso | zh-TW | |
| dc.subject | 自律神經系統 | zh_TW |
| dc.subject | 智慧型手機 | zh_TW |
| dc.subject | 心臟 | zh_TW |
| dc.subject | 多尺度熵 | zh_TW |
| dc.subject | 心律變異度 | zh_TW |
| dc.subject | 膀胱 | zh_TW |
| dc.subject | 甲狀腺 | zh_TW |
| dc.subject | cardiac | en |
| dc.subject | multi-scale entropy | en |
| dc.subject | autonomic nervous system | en |
| dc.subject | smart phone | en |
| dc.subject | thyroid | en |
| dc.subject | bladder | en |
| dc.subject | heart rate variability | en |
| dc.title | 手持式生理信號分析程式 | zh_TW |
| dc.title | App-based analysis program for physiological signals | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 謝建興 | |
| dc.subject.keyword | 心律變異度,多尺度熵,自律神經系統,智慧型手機,甲狀腺,膀胱,心臟, | zh_TW |
| dc.subject.keyword | heart rate variability,multi-scale entropy,autonomic nervous system,smart phone,thyroid,bladder,cardiac, | en |
| dc.relation.page | 32 | |
| dc.identifier.doi | 10.6342/NTU201803399 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-16 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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|---|---|---|---|
| ntu-107-1.pdf 未授權公開取用 | 1.68 MB | Adobe PDF |
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