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研究生: 林陞樵
Lin, Sheng-Chiao
論文名稱: 結合頭部衝動測試同調性與人工神經網路之突發性耳聾預後分析
Prognosis Prediction in Sudden Sensorineural Hearing Loss Utilizing Coherence Analysis of Video Head Impulse Test and Artificial Neural Network
指導教授: 林哲偉
Lin, Che-Wei
學位類別: 博士
Doctor
系所名稱: 工學院 - 生物醫學工程學系
Department of BioMedical Engineering
論文出版年: 2026
畢業學年度: 114
語文別: 英文
論文頁數: 100
中文關鍵詞: 突發性耳聾暈眩頭部衝動測試小波同調性人工智慧
外文關鍵詞: Sudden sensorineural hearing loss, Vertigo, Video head impulse test, Wavelet coherence, Artificial intelligence
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  • 突發性耳聾定義為三天之內感音性聽力閾值於連續三音頻突降三十分貝。近半世紀以來,經標準類固醇治療後仍僅有15.7-26%的病患聽力完全恢復。28.8-40%的患者有前庭共伴症狀例如暈眩、頭暈、重心不穩。在前庭疾患的臨床治療上,著實需要一個既能協助診斷又能判斷症狀預後的參數指標。儘管近年前庭檢查的發展,仍然缺乏探討突發性耳聾合併暈眩患者之前庭預後及前庭損傷與聽力預後關係的研究。前庭動眼反射已被證明具有頻率選擇性質。在我們的研究中,我們採用頭部衝動測試的小波同調性分析來研究兩頻率訊號的功能性關聯性。此外,人工神經網路也被應用於加速及協助預後判斷。
    我們回溯檢視於2016年12月至2020年12月在本醫學中心治療之突發性耳聾合併暈眩患者,依聽力檢查、前庭檢查、及暈眩視覺類比量表之資料完整性,最終對31位及64位患者分別進行聽力預後及前庭預後之研究,使用同調性分析對所有測得頻率量測小波同調相關係數,進而使用多項式Cox迴歸分析由美國耳鼻喉頭頸外科醫學會定義之聽力恢復程度及由暈眩視覺類比量表所定義之暈眩恢復狀態。五倍交叉驗證之卷積神經網路進而針對聽力預後進行小波同調性時頻圖的分析。
    前庭預後方面,於病發兩周時之早期緩解中,水平半規管、後半規管、三半規管平均值、三半規管最小值於小波同調相關係數≥ 0.9時之時頻圖中最高頻率值,水平半規管、前半規管於0-6赫茲之相位遲滯總和,以及水平半規管於0-125赫茲之相位遲滯總和均與暈眩症狀統計上顯著相關。在多變項分析中,三半規管最小值是影響早期前庭恢復的獨立因子(風險比 2.219,95%信賴區間 1.168-4.214)。於病發兩個月的晚期恢復中,除了上述於兩周早期緩解之顯著因子外,診斷時的暈眩視覺類比量表數值、水平半規管及三半規管平均值之前庭動眼反射增益也呈現統計上顯著差異。聽力預後方面,後半規管於小波同調相關係數≥ 0.9時之時頻圖中較高的最高頻率值為治療後聽力完全恢復的獨立因子(風險比 2.11,95%信賴區間 1.86-2.35)。使用AlexNet進行特徵提取並使用支持向量機進行分類水平影像擷取的併圖時,其測量精度在聽力完全恢復 對聽力未完全恢復之分類中,原始資料組、過取樣法、額外資料提取法、時間序列數據內插分別為82.8%、90.9%、92%、90.9%。因此小波同調相關係數≥ 0.9時之時頻圖中最高頻率值,在突發性耳聾合併暈眩的患者中,不僅與早期前庭症狀緩解有關,更與優良的聽力預後相干。本研究證實小波同調性分析可以更全面性地評估前庭動眼反射。卷積神經網路能妥善應用於分類小波同調性分析之時頻圖、預測治療預後、並促進頭部衝動測試檢查的深入判讀。

    Sudden sensorineural hearing loss (SSNHL) was defined as 30 dB or more sensory sudden hearing drop in contiguous three frequencies within 3 days. Throughout recent half-century, complete recovery (CR) of hearing still remained only 15.7-26% after steroid treatment. Concomitant vestibular affection with symptoms such as vertigo, dizziness, or unsteadiness in 28.8-40% patients has been reported. In clinical practice for vestibular disorder, a parameter associated with not only disease diagnosis but also symptom prognosis was needed. Despite the progressive development of vestibular laboratory, studies discussing vestibular recovery and its relationship with hearing recovery in SSNHL with vertigo (SSNHLV) were still lacking. The vestibulo-ocular reflex (VOR) adaptation has been shown to be frequency selective. In this study, the wavelet coherence analysis (WCA) of video head impulse test (vHIT) was adopted for the investigations of functional connectivity in frequency domain between signals. Besides, the artificial neural network was utilized to facilitate and assist the prognosis prediction.
    We retrospectively reviewed SSNHLV patients managed at tertiary referral center during December 2016 to December 2020. Depending on the data integrity of audiometry, vestibular exams, and visual analog scale (VAS) of vertigo, 31 and 64 patients were enrolled for vertigo and hearing prognosis analyses. The coherence analysis measured the magnitude-squared wavelet coherence (MSWC) through diverse frequencies in vHIT. Recovery of hearing defined by AAO-HNS and vertigo in VAS were analyzed utilizing a multivariable Cox regression model. The hearing outcomes were further analyzed with convolutional neural network (CNN) of WCA with 5-fold cross-validation.
    For vestibular prognosis, the greater highest coherent frequency at MSWC ≥ 0.9 in horizontal semicircular canal (SCC), posterior SCC, total phase lag of 0-6 Hz in horizontal SCC (p = 0.031), anterior SCC (p = 0.038), total phase lag of 0-125 Hz in horizontal SCC (p = 0.033), mean value, and minimal value of highest coherent frequency at MSWC ≥ 0.9 among three SCCs were significantly associated with the early complete remission of vertigo at 2 weeks after treatment. In multivariable analysis, the minimal coherent frequency of MSWC ≥ 0.9 among three SCCs stood as the independent factor (hazard ratio [HR] 2.219, 95% confidence interval [CI] 1.168-4.214). At 2 months after therapy, in addition to the above significant parameters after 2 weeks therapy, the initial VAS, VOR gain in horizontal SCC, and mean value of VOR gain among three SCCs were also related to total relief of vertigo. For hearing prognosis, a greater highest coherent frequency at MSWC ≥ 0.9 of posterior SCC was associated with CR of hearing. After adjustment for other factors, the result remained robust (hazard ratio [HR] 2.11, 95% confidence interval [CI] 1.86-2.35). In the feature extraction with AlexNet followed by SVM in horizontal image cropping style, the classification accuracy for (CR vs. partial + no recovery [PR + NR]), (over-sampling of CR vs. PR + NR), (extensive data extraction of CR vs. PR + NR), and (interpolation of time series of CR vs. PR + NR) were 82.8%, 90.9%, 92%, and 90.9%, respectively. Therefore, the highest coherent frequency at MSWC ≥ 0.9 was not only related to early vestibular recovery but also to good hearing prognosis in patients with SSNHLV. WCA may provide comprehensive evaluation of VOR. CNN could be utilized to classify WCA, predict treatment outcomes, and facilitate vHIT interpretation.

    摘 要 I Abstract III Table of Contents VI List of Tables IX List of Figures X Chapter 1 Introduction 1 1.1 Background 1 1.1.1 The Need and Challenges for Prognosis Prediction in Vestibular Disorders 1 1.1.2 Virtual Reality Application in Vestibular Evaluation 3 1.2 Literature Review 4 1.2.1 Artificial Intelligence Applied in Vestibular Laboratory 4 1.2.2 Deep Learning of Coherence in Biomedical Field 12 1.2.3 Wearable Device with Eye Trackers Applied in Vestibular Evaluation 15 1.3 Research Purposes of this Thesis 22 1.4 Organization of this Thesis 24 Chapter 2 Material & Methodology 25 2.1 Material 25 2.1.1 Patients and Study Design 25 2.1.2 Data Collection and Treatment Protocol 25 2.2 Methodology 26 2.2.1 Video Head Impulse Test 26 2.2.2 The Proposed Algorithm Overview 27 2.2.3 Wavelet Coherence 28 2.2.4 Data Augmentation and Image Preprocessing 28 2.2.5 Convolutional Neural Network and K-Fold Cross-Validation 29 2.2.6 Statistical Analysis 29 Chapter 3 Results 31 3.1 Data Source 31 3.1.1 Demographic Data in Vestibular Prognosis 31 3.1.2 Demographic Data in Hearing Prognosis 31 3.2 Results 32 3.2.1 Statistical Analysis of Vestibular Prognosis 32 3.2.2 Statistical Analysis of Hearing Prognosis 32 3.2.3 Association between vHIT Parameters and WCA 33 3.2.4 Classification Results of Hearing Prognosis 47 Chapter 4 Discussions & Conclusion 50 4.1 Discussion 50 4.1.1 Scalogram of vHIT 50 4.1.2 Phase Coherence Analysis of vHIT 51 4.1.3 Wavelet Coherence Analysis of Vestibular Prognosis 51 4.1.4 Wavelet Coherence Analysis of Hearing Prognosis 54 4.1.5 Classification of Hearing Prognosis 56 4.2 Conclusion 58 Chapter 5 Limitations & Future Work 59 5.1 Limitations 59 5.2 Future Work 60 5.2.1 Scheme to Develop Virtual Reality-Based Precise Vestibular Evaluation 60 5.2.2 Investigation in Impact of VOR Coherence on Vestibular Recovery Outcome 61 5.2.3 Virtual Reality-Based Frequency-Specific Vestibular Evaluation 62 5.2.4 Extensive Applications of Virtual Reality in Vestibular Evaluation 63 5.2.5 Comparison of Thesis with Existing Device-Assisted Vestibular Evaluation to Our Work 64 Abbreviation 66 References 70

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