中華民國的新生兒聽力篩檢已經日益普遍，中華民國衛生福利部國民健康署也補助自動聽性腦幹響應 (Automated Auditory Brainstem Response, AABR) 作為新生兒的聽力篩檢，在聽性腦幹響應 (Auditory Brainstem Response, ABR) 量測時會產生七個波，當中以I、III和V波最為明顯，而一般偵測新生兒有聽力受損的方法，是以辨別第V波的存在與否。
先前的研究所開發之AABR量測系統使用卡爾曼濾波器 (Kalman Filter) 與指數權重平均演算法 (Exponential Weighted Average, EWA) 自動偵測到的結果和第V波的結果及判別，但仍需要人為去截取第V波畫面，為了改善此半自動的判別方式，本研究更以二次指數權重平均演算法 (Second Exponential Weighted Average)，改善高雜訊的AABR，以便於辨別，除了比較先前卡爾曼加指數的演算法，也與以往常被使用的單點變異數演算法 (Variance Of A Single Point, Fsp) 做比較，而本研究也比較了三種自動辨別法來選擇最好的方法，分別為快速傅利葉 (Fast Fourier Transform, FFT) 、圖像分析法 (Image and Pattern Analysis 99, IPAN99) 和微方判別法 (Differential) ，最後選擇較快速且準確的微分判別法。
為了評估此方法的可行性，本研究實驗一對5名年齡23到26歲正常聽力之男性和1名聽力受損之男性進行實驗，本研究所使用的方法產生第V波的反應時間約在6至7.5ms之間，雖然波形反應時間會較長 (多0.5ms) ，但是卻能消除大部分之雜訊，而波形也更容易觀察，及自動辨識並截取畫面，又可以免除主觀的人為判斷，實驗二則對2名正常聽力的受試者，在聽力室沒有關門的情況下測量ABR訊號，由於背景噪音過大，導致無法測量到ABR訊號，所以本研究在量測ABR波形適用於聽力室內做檢查，否則環境中的聲音可能會影響結果而量測不到，本研究所使用之演算法和自動判別法確實改善了先前研究設備的顯示波形過多雜訊和辨識第V波時需要人為去截取畫面缺點，使得此系統更為完整。

With the growing popularity of the newborn hearing screening, R.O.C. Ministry of Health and Welfare subsidize infant hearing screening with Automated Auditory Brainstem Response. The Auditory Brainstem Response typically has seven waves when measured. Wave I、III and V are obvious than all the other waves. Detecting wave V is the common method used to identify hearing loss.
Previous studies used Kalman Filter with Exponential Weighted Average in an AABR measurement system, but its detecting method still required manual screenshots. Therefore, the semi-automatic detecting method need to be improved. This study uses the Second Exponential Weighted Average approach to improve the detection of ABR signals under noisy condition. In addition to compare the kalman filter with Exponential Weighted Average, the proposed approach also compares Variance of a Single Point used to calculate in ABR. The compared three methods evaluated in this study are the Fast Fourier Transform method, Image and Pattern Analysis 99 method and the Differential method. The Differential method has been chosen for its fast processing speed and accuracy.
In order to assess the feasibility of our approach, 5 male subjects with normal hearing and 1 male subject with hearing loss were participated in the first experiment. Wave V wave latencies were measured between 6 and 7.5ms. Although this method has longer latency (0.5ms), it can eliminate most noises, and the ABR waves are easier to be observed. It can automatically recognize and take screenshots to avoid undesired subjective human judgment. In the second experiment, ABR signals from 2 of previous 5 normal hearing subjects were measured in a hearing exam room without closing the door. With significant background noises in this situation, ABR signals were not measured as the result. Consequently, this study is carried out in hearing exam rooms, to avoid interference of background noises on the measurement of ABR signals. This algorithm and the auto-detection method developed from this study actually improve the previous research's problems about the waveforms which contained too many interference and need to take screenshot manually. Therefore, this study makes our system more complete.

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