系統識別號 U0026-0708201521331900
論文名稱(中文) 基於聽覺原理之訊號處理應用於心血管聲紋特徵分析
論文名稱(英文) Auditory-based signal processing for cardiovascular sound pattern analysis
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 103
學期 2
出版年 104
研究生(中文) 宋柏勳
研究生(英文) Po-Hsun Sung
電子信箱 phsung@gmail.com
學號 N28951463
學位類別 博士
語文別 英文
論文頁數 63頁
口試委員 召集委員-黃錦煌
中文關鍵字 聲音訊號處理  聽覺模型  心血管疾病  聲紋特徵分析  心音圖  脈聲圖 
英文關鍵字 Human-like auditory processing  cardiovascular disease  sound pattern analysis  phonocardiography  phonoangiography 
中文摘要 跟據世界衛生組織統計,心血管疾病是全球的頭號死因,在2008年有1730萬人死於心血管疾病,佔全球總數的30%.估計到2030年,死於心血管疾病的人數將增加到2330萬人。現有的心血管疾病早期檢測設備中,非侵入式儀器,如杜普勒(Doppler)超音波心臟顯影(Echocardiography),雖具精確, 非侵入性等優點.唯該設備較為昂貴,且需具心臟專科專業訓練之醫事人員才具能力判讀,無法成為居家自我檢測醫材.而其他非侵入式心臟監測之方法,如心電圖(Electrocardiography)雖為目前醫學界普遍使用的標準檢測方式,然僅能檢測心臟電氣特性,並無法偵測心臟機械功能之缺損與血管栓塞等疾病.本論文提出利用類人耳聽覺模型在處理語音訊息與聲音辨識上的強健性特徵,利用電子聽診器進行心血管聲音採集,並結合電腦輔助聽診技術,可將心血管的雜音透過3D耳蝸聽覺頻譜呈現,並進一步利用基於聽覺原理之時頻特徵分析,可將心血管之聲紋,如心音圖,脈聲圖等轉為聽覺上的辨識特徵,用以作為先天性心臟病與洗腎廔管栓篩等心血管疾病的早期偵測系統.目前是用此法於開放性動脈導管(Patent Ducturs Arteriosus)的偵測上具100%的敏感度與91.17%的特異度,而在洗腎廔管阻塞(Vascular access stenosis)的判定上則可達83.87 %的準確率.
英文摘要 For cardiovascular disease patients, non-invasive diagnosis methods, such as echocardiogram and electrocardiography (ECG), provide an accurate and safe method to assess the heart function. However, the echocardiogram is expensive and requires the operation of trained cardiologist. Although ECG is a standard method to diagnosis the heart disease in the first screening process, it cannot detect the mechanical disorder of heart function and thrombosis of blood vessel. Accordingly, this study proposed a Human-like auditory processing (HAP) to analysis the cardiovascular signal. The HAP had been verified and performed well at speech processing and robust speech recognition. It was further combined with electrical stethoscope and computerized-auscultation method for mimicking a trained practitioner in performing the auscultation process. In the proposed approach, the bruit obtained by a standard phonoangiography and phonocardiography of heart murmur are transformed into the time-frequency domain, and two spectro-temporal features, namely the auditory spectrum flux and the auditory spectral centroid, are then extracted. The distributions of the two features are analyzed using a multivariate Gaussian distribution (MGD) model. Finally, the distribution parameters of the MGD model are used to detect the presence (or otherwise) of vascular access stenosis. The results show an accuracy of 83.87% in detecting significant vascular access stenosis. Besides, the PDA murmurs, are used in the blind test for algorithm effectiveness assessment. The results demonstrate that the proposed computer-assisted auscultation method can achieve a high sensitivity of 100% and a specificity of 91.67% for PDA detection. The above results demonstrate that the proposed human-like auditory processing system used for cardiovascular sound analysis is robust, cost-effective and convenient for the non-invasive early detection.
論文目次 摘要 I
Abstract II
Acknowledgement IV
Contents V
List of Figures VII
List of Tables IX
List of Acronyms X
Chapter 1 Introduction 1
1.1 Motivations and Aims 1
1.2 Thesis Outline 3
Chapter 2 Literature Review 4
2.1. Auditory processing for speech enhancement and Recognition 4
2.2. Computer-aided Auscultation (CAA) for cardiovascular sound analysis 8
2.2.1. Patent Ductus Arteriosus Detection 8
2.2.2. Hemodialysis vascular access stenosis detection 11
Chapter 3 Methods 15
3.1. Human-like Auditory Processing (HAP) 15
3.1.1. Peripheral Auditory Processing 17
3.1.2. Cortical Auditory Processing 20
3.2. Auditory Spectro-Temporal Feature 22
3.2.1. Auditory Spectrum Flux 22
3.2.2. Auditory Spectral Centroid 23
3.2.3. Multivariate Gaussian Distribution 24
Chapter 4 Patent Ductus Arteriosus Detection 24
4.1. Subjects and Data Acquisition 25
4.2. Auditory Spectro-Temporal Analysis 26
4.3. Results 27
4.4. Discussion 33
Chapter 5 Hemodialysis Vascular Access Detection 36
5.1. Subjects and Data Acquisition 36
5.2. Auditory Spectro-Temporal Features 37
5.3. Results 38
5.4. Discussion 46
Chapter 6 Summary and Future Work 49
6.1. Summary 49
6.2. Future Work 50
Appendix 52
A. Auditory-based feature extraction 52
A.1 Mel Frequency Cepstrum Coefficients 52
A.2 Gammatone Wavelet Cepstral Coefficients 54
A.3 Basilar-membrane Frequency-band Cepstral Coefficients (BFCC) 55
Reference 57
Publication List 62
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