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

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题名:	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
作者:	楊明修;Yang, Ming-Xiu
贡献者:	電機工程學系
关键词:	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;Auditory brainstem response (ABR);Automatic auditory brainstem response (AABR);Newborn hearing screening;Discrete wavelet transform (DWT);Kalman filter;Fitted parametric peak
日期:	2021-08-30
上传时间:	2021-12-07 13:12:33 (UTC+8)
出版者:	國立中央大學
摘要:	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?均大於Uα皆無顯著差異，以此證實演算法之可行性。 ;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.
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