Classification Techniques for Diagnosing Respiratory Sounds in Infants and Children

摘要

Recently, many studies were performed using several techniques to classify and diagnose lung sound, but as a drawback the age category was limited, almost adult only, as well as the insufficient number of samples and this unfortunately leads to an unfair classification of lung sound. While this study deals with different methods to analyze lung sounds and extract distinctive features then classify them to diagnose lung sounds in infant and children to one of the three categories: Normal, Wheeze, or Stridor. Features were extracted using three different techniques in separate ways to compare the effectiveness; these techniques are Discrete Wavelet transform (DWT), Short Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCCs). After that the sounds are categorized using four different classification techniques which include Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB). The main aim of this research is to choose the best signal processing technique with the most suitable classifier to diagnose lung sounds by categorizing 300 lung sounds especially in infants and children to Normal, Wheeze, or Stridor. These sounds are collected from Alexandria University Children Hospital (AUCH) - Egypt as a particular environment which is considered one of the main advantages of this research. Moreover, extra 146 wheezes were used to validate the usefulness of the classifiers. The results were very promising.