用於腸鳴音信號偵測之高階統計分形維度演算法

Abstract

腸音為一種可用來區分胃腸蠕動功能的重要生理參數，在臨床診斷上，藉由醫師使用聽診器聽取腸音並且評估受試者胃腸蠕動狀態，腸音聽診擁有非侵入式的優點但也容易受到環境雜音干擾，因此在充滿噪聲的環境中不易偵測到腸音。本論文提出使用高階統計分形維度演算法來偵測腸鳴音，藉由高階統計的抑制高斯訊號的特性，本論文提出的高階統計分形維度演算法能有效在不同雜訊類型下偵測腸鳴音訊號，且高階統計分形維度演算法在雜訊類型及其強度的變動上擁有較佳的抗噪性。本論文實驗結果顯示，高階統計分形維度演算法可在高斯雜訊與不同的環境噪聲中有效偵測出受干擾的腸音，因此高階統計分形維度演算法可作為腸音信號偵測有效技術。

Bowel sounds is an important physiological parameter of distinguishing the gastrointestinal motility dysfunction. In clinical, expert physicians use the stethoscope to listen to bowel sounds and evaluate gastrointestinal motility states of the subject. Auscultation of bowel sounds provides a noninvasive way for clinical diagnosis, but it is also easily affected by environmental noise. Therefore, detecting bowel sounds under noisy environment is very difficult. In this study, a novel higher-order-statistics (HOS)-based fractal dimension algorithm was proposed for detecting bowel sounds. By using the nature of suppressing Gaussianity of higher order statistics technique, the proposed method can effectively detect bowel sounds under different noise conditions, and its performance is insensitive to the change of noise type and noise level. The experimental results show that HOS-based fractal dimension algorithm have the better performance for signal detection under Gaussian noise and environmental noise. Therefore, HOS-based fractal dimension algorithm can be viewed as a good method for detecting bowel sounds.