運用小波卡爾曼濾波及擬合參數峰自動偵測聽性腦幹反應第五波

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：52

、訪客IP：3.145.176.131

姓名

楊明修(Ming-Xiu Yang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

運用小波卡爾曼濾波及擬合參數峰自動偵測聽性腦幹反應第五波
(Auto-detection of Auditory Brainstem Response (ABR) Wave V with Wavelet-Kalman Filter and Fitted Parametric Peak Method)

相關論文

★ 獨立成份分析法於真實環境中聲音訊號分離之探討	★ 口腔核磁共振影像的分割與三維灰階值內插
★ 數位式氣喘尖峰氣流量監測系統設計	★ 結合人工電子耳與助聽器對中文語音辨識率的影響
★ 人工電子耳進階結合編碼策略的中文語音辨識成效模擬--結合助聽器之分析	★ 中文發聲之神經關聯性的腦功能磁振造影研究
★ 利用有限元素法建構3維的舌頭力學模型	★ 以磁振造影為基礎的立體舌頭圖譜之建構
★ 腎小管之草酸鈣濃度變化與草酸鈣結石關係之模擬研究	★ 口腔磁振影像舌頭構造之自動分割
★ 微波輸出窗電性匹配之研究	★ 以軟體為基準的助聽器模擬平台之發展-噪音消除
★ 以軟體為基準的助聽器模擬平台之發展-回饋音消除	★ 模擬人工電子耳頻道數、刺激速率與雙耳聽對噪音環境下中文語音辨識率之影響
★ 用類神經網路研究中文語音聲調產生之神經關聯性	★ 教學用電腦模擬生理系統之建構

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]

至系統瀏覽論文 (2024-9-1以後開放)

摘要(中)

新生兒聽力篩檢於國內漸漸普及，嬰兒需在六個月大之前盡早發現是否有聽力損失，以免錯過語言學習的黃金時期，對日後產生負面影響。目前以自動聽性腦幹反應 (Automatic auditory brainstem response, AABR )為最主要的檢測方法，若嬰兒未通過新生兒聽力篩檢，則需再透過聽性腦幹反應 (Auditory brainstem response, ABR)來評估嬰兒的聽閾值，ABR主要由七個波所組成，聽力師會針對第五波存在與否，來當做聽力評估指標。
先前研究所開發之無線自動聽性腦幹響應量測系統使用卡爾曼濾波器加指數權重平均法 (Kalman filter with EWA)對訊號進行濾波，並利用微方法 (Differential)來對ABR的第五波做判別，但仍有雜訊過大造成判別不準確的情況，因此本研究以小波卡爾曼濾波 (Wavelet-Kalman filter)與移動平均法 (Moving average)對先前的濾波演算法做比較，而自動判別方式使用擬合參數峰 (Fitted parametric peak, FPP)去與微分方法進行比較。
模擬結果顯示，經小波卡爾曼濾波後ABR之訊噪比為最高，且聽力師所能標記之第三、五波數量最多。使用FPP所偵測第五波之反應時間，來與聽力師之手動標記計算皮爾森積動差相關係數 (Pearson product-moment correlation coefficient)，兩者之間呈現高度相關，且相關係數高於微方法。因此，本研究將小波卡爾曼濾波與FPP演算法應用於AABR量測系統上。
為了評估本研究演算法之正確性，以4位聽力正常的個案參與實驗，實驗一中每位個案均有出現第五波再現，實驗二中自動偵測之標準差最小為0.02ms，最大為0.3ms，並使用曼-惠特尼U檢定 (Mann-Whitney U test)檢驗自動偵測之平均值與再現性兩者之間的差異性，各組數據所得到U値均大於Uα皆無顯著差異，以此證實演算法之可行性。

摘要(英)

Newborn hearing screening is universal in Taiwan. It is important to find out whether infants have hearing impairments , especially within six months of birth. Early detection and intervention can help those with hearing loss impairments to develop better speech and language skills. Automatic auditory brainstem response (AABR) is one of the major tools for newborn hearing screen. If failed in the AABR screening, the newborn has to receive a further the auditory brainstem response (ABR) examination. The ABR potentials typically consist of seven waves, and clinical audiologists usually inspect the presences or absence of wave V.
The wireless automatic auditory brainstem response measurement system developed in the previous research uses Kalman filter with exponential weight averaging method (Kalman filter with EWA) to filter signal and uses the differential method to detect the Wave V of ABR. However, the signals are too noisy to be accurately determined. Therefore, this study compares the Wavelet-Kalman filter and Moving average to the Kalman filter with EWA, and the fitted parametric peak (FPP) to the differential method, respectively.
The simulation results showed that the signal-to-noise ratio of ABR is the highest after Wavelet Kalman filtering, and the audiologist can mark the waves III and V of ABR the most. The latency of the wave V detected by FPP is used to calculate the Pearson product-moment correlation coefficient with the audiologist’s manual mark. The two are highly correlated, and the correlation coefficient is higher than the differential method. Therefore, the Wavelet-Kalman filter and FPP algorithm were implement in the AABR measurement system.
In order to evaluate the accuracy of the algorithm in this research, human experiments were conducted with four normal hearing subjects. In experiment 1, the reproducibility of wave V was demonstrated with all participants. In experiment 2, the minimum standard deviation of automatic detection is 0.02ms, and the maximum is 0.3ms. The Mann-Whitney U test was used to test the difference between the average value of automatic detection and reproducibility. The U values of each group data were all greater than Uα. Therefore, the feasibility of the proposed algorithm has been confirmed.

關鍵字(中)

★ 聽性腦幹反應
★ 自動聽性腦幹反應
★ 新生兒聽力篩檢
★ 離散小波轉換
★ 卡爾曼濾波器
★ 擬合參數峰

關鍵字(英)

★ Auditory brainstem response (ABR)
★ Automatic auditory brainstem response (AABR)
★ Newborn hearing screening
★ Discrete wavelet transform (DWT)
★ Kalman filter
★ Fitted parametric peak

論文目次

目錄
摘要 i
Abstract iii
致謝 v
圖目錄 ix
表目錄 xii
第一章緒論 1
1.1 研究動機 1
1.2 文獻探討 4
1.3 研究目的 7
1.4 成果貢獻 7
1.5 論文架構 9
第二章聽性腦幹反應與無線AABR量測系統 10
2.1 聽性腦幹反應 (ABR)原理 10
2.2 受測者與ABR之關係 15
2.3 ABR與AABR的基本檢測步驟 17
2.4 無線AABR量測系統 18
第三章 ABR訊號處理與自動偵測第五波之演算法 21
3.1 ABR訊號處理之演算法 21
3.1.1. 離散小波轉換 21
3.1.2. 卡爾曼濾波器加指數權重平均法 24
3.1.3. 移動平均法 26
3.1.4. 小波卡爾曼濾波 27
3.2 比較演算法性能 28
3.2.1. 客觀評估 28
3.2.2. 主觀評估 32
3.3 自動偵測第五波之方法 36
3.3.1. 微分法 36
3.3.2. 擬合參數峰 38
3.4 比較自動偵測之方法 39
3.5 結論 40
第四章實驗設計與結果分析 41
4.1 實驗流程 41
4.2 實驗設計 43
4.2.1. 個案 44
4.2.2. 實驗一 45
4.2.3. 實驗二 46
4.3 結果分析 47
4.3.1. 實驗一之結果與分析 47
4.3.2. 實驗二之結果與分析 52
4.4 實驗討論 58
第五章結論與未來展望 61
5.1 結論 61
5.2 未來展望 62
參考文獻 63
附錄 67

參考文獻

Alwan, A. (2012). Implementation of Wavelet-Kalman Filtering Technique for Auditory Brainstem Response. Computer Science.

Arooj, A., Muhamed, M., Hussain, S., & Salleh, S. (2010). Use of instantaneous energy of ABR signals for fast detection of wave V. Journal of Biomedical Science and Engineering, vol.3, pp. 816-821.

Bradley, A., & Wilson, W. (2004, 5). On wavelet analysis of auditory evoked potentials. Clinical Neurophysiology, vol.115, pp. 1114-1128.

Bradley, A., & Wilson, W. (2005). Automated analysis of the auditory brainstem response using derivative estimation wavelets. Audiol Neurootol, vol.10, pp. 6-21.

Causevic, E., Morley, R., Wickerhauser, M., & Jacquin, A. (2005). Fast wavelet estimation of weak biosignals. IEEE Transactions on Biomedical Engineering, vol.52, pp. 1021 - 1032.

Crumley, W. (2011). Good Practices in Auditory Brainstem Response, Part 1. Retrieved from AudiologyOnline: https://www.audiologyonline.com/articles/good-practices-in-auditory-brainstem-827

Elberling, C., & Don, M. (1984). Quality Estimation of Averaged Auditory Brainstem Responses. Scandinavian Audiology, vol.13, pp. 187-197.

Georgiadis , S., Ranta-aho, P., Tarvainen, M., & Karjalainen, P. (2005). Single-trial dynamical estimation of event-related potentials: a Kalman filter-based approach. IEEE Transactions on Biomedical Engineering, vol.52, pp. 1397-1406.

Hall, J. (2017). Update on auditory evoked responses:Evidence-based ABR protocol for infant hearing assessment. Retrieved from AudiologyOnline: https://www.audiologyonline.com/articles/update-on-auditory-evoked-responses-19040

Hall, J. W. (1992). Handbook of auditory evoked Responses. The United States of America: Boston:Allyn and Bacon.

Ikawa, N. (2013). Automated averaging of auditory evoked response waveforms using wavelet analysis. International Journal of Wavelets, Multiresolution and Information Processing, vol.11.

Källstrand, J., Lewander, T., Baghdassarian, E., & Nielzén, S. (2014). A new method for analyzing auditory brain-stem response waveforms using a moving-minimum subtraction procedure of digitized analog recordings. Neuropsychiatric Disease and Treatment, vol.10, pp. 1011-1016.

Knipper, M., Van Dijk, P., Nunes, I., Ruttiger, L., & Zimmermann, U. (2013). Advances in the neurobiology of hearing disorders: Recent. Progress in Neurobiology, pp. 17-33.

Kostorz, I., Kowalski, W., Ludwig, Z., & Zając , J. (2013). Detection of waves of Auditory Brainstem Responses using IPAN99 algorithm. Journal of Medical Informatics & Technologies, vol. 22, pp. 219-225.

Lin, H., Chang, H., & Hsieh, W. (2018, 1). The Past, Present and Future of Universal Newborn Hearing Screening in Taiwan. Journal of Early Hearing Detection and Intervention, vol.3, pp. 54-56.

Nachar, N. (2008). The Mann‐Whitney U: A Test for Assessing Whether Two Independent Samples Distribution. Tutorials in Quantitative Methods for Psychology, vol.4, pp. 13-20.

Pedriali, I., & Kozlowski, L. (2006, 10). The influence of abr clik intensity and rate in adults with normal hearing. Arq Int Otorrinolaringol, pp. 105-113.

Roeser, R., Valente, M., & Dunn, H. (2007). Audiology Diagnosis. New York: Thieme.

Rosenhall, U., & Axelsson, A. (1995, 2 24). Auditory Brainstem Response Latencies in Patients with Tinnitus. Scandinavian Audiology, pp. 97-100.

Valderrama , J., Torre, A., Alvarez, I., Segura, J., Thornton, A., Sainz, M., & Vargas, J. (2014). Automatic quality assessment and peak identification of auditory brainstem responses with fitted parametric peaks. Computer Methods and Programs in Biomedicine, vol.114, pp. 262-275.

WHO. (2021). Deafness and hearing loss. Retrieved from https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss

Wilkinson, A., & Jiang, Z. (2006, 11). Brainstem auditory evoked response. Seminars in Fetal Neonatal Medicine, pp. 444-451.

Wilson, W., & Aghdasi, F. (1999). Discrete wavelet analysis of the auditory brainstem response: Effects of subject age, gender and test ear. Africon (pp. 291-296). Cape Town, South Africa: IEEE.

Zhang, R., McAllister, G., Scotney, B., McClean, S., & Houston, G. (2006). Combining Wavelet Analysis and Bayesian Networks for the Classification of Auditory Brainstem Response. IEEE Transactions on Information Technology in Biomedicine, vol.10, pp. 458 - 467.

林聖凱. (2015). 「以嵌入式系統及Android為平台之無線自動聽性腦幹響應(AABR)量測系統」. 國立中央大學電機工程研究所碩士論文.

林鴻清. (2014). 台灣新生兒聽力篩檢與確診指引手冊. 國民健康署.

國民健康署. (2018). 如何發現嬰幼兒聽力損傷~嬰幼兒聽力篩檢. 擷取自 https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=515&pid=522

溫登翔. (2019). 「以嵌入式平台為基礎開發聽覺腦幹響應量測系統」. 國立中央大學電機工程研究所碩士論文.

趙柏宇. (2017). 「以無線自動聽性腦幹響應量測系統發展V波自動判別之方法」. 國立中央大學電機工程研究所碩士論文.

劉致中. (2011). 「以個人數位助理為平台之短暫誘發耳聲傳射檢測儀」. 國立中央大學電機工程研究所碩士論文.

指導教授

吳炤民(Chao-Min Wu)

審核日期

2021-8-30

推文