Using Back-Propagation Neural Network for Automatic Wheezing Detection
Date Issued
2005
Date
2005
Author(s)
Lin, Bor-Shing
DOI
en-US
Abstract
Wheezes are continuous adventitious lung sounds, which have been defined as lasting for at least 250 ms. They are probably produced when airflow makes narrow airways vibrate. Wheezes are common clinical signs in patients with obstructive pulmonary disease such as asthma. Automatic wheezing detection offers an objective and accurate way to analyze wheezing lung sounds. By the automatic wheezing detection system, the features about amount, time duration, frequency range, and power strength of wheezes can be extracted to help physicians diagnose. It also can provide long-term auscultation and analysis of a patient.
This Dissertation describes the design of a fast and high performance wheeze recognition system. First, respiratory sounds are captured, amplified and filtered by an analog circuit; then digitized through a PC soundcard, and recorded in accordance with the Computerized Respiratory Sound Analysis (CORSA) standards. Since the proposed wheezing detection algorithm is based on three methods: 2D bilateral filtering of spectrogram, order truncate average (OTA) method, and moving average (MA) method. Some features are then extracted from the processed spectra to train a back-propagation neural network (BPNN). Eventually, the new testing samples go through the trained BPNN to recognize whether they are wheezing sounds.
Experiment results of the MA method show a high sensitivity of 1.0 and a specificity of 0.895 in qualitative analysis of wheeze recognition. Due to its high efficiency, great performance and easy-to-implement features, this wheeze recognition system could be of interest in the clinical monitoring of asthma patients and the study of physiological mechanisms in the respiratory airways.
Subjects
哮鳴
時頻圖的雙邊濾波處理法
逐次框取平均法
移動平均法
倒傳遞類神經網路
wheeze
2D bilateral filtering of spectrogram
order truncate average method
moving average method
back-propagation neural network
Type
thesis
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