Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141307
Title: 3-way respiratory sound classification using machine learning algorithms
Authors: Tu, Yixuan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Abstract: With the development in the fields of artificial intelligence and machine learning, such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN), many biomedical and healthcare applications have gained additional support from high-tech. Nowadays, more and more fields about health problem detection are using sound information and Artificial Intelligence (AI) techniques rather than only depending on image detection or diagnosis from doctors, which could save medical resources as well as improve diagnostic rate and diagnosis accuracy. This project mainly focuses on two types of classification algorithms, one is to project the processed audio signal to a set of spatial points and use geometrical distance to divide them into three parts, the other is to use machine learning algorithms. For geometrical distance, I tried three types of distances: Minkowski, Manhattan and Euclidean distance; for machine learning algorithms, I tried two widely used methods: Support Vector Machine and Convolutional Neural Network, while the SVM part I tried both Gaussian Core and linear SVM. In conclusion, the linear SVM provides the best performance. This dissertation also shows the signal processing progress before the respiratory sound could be classified, which includes audio value extraction, data choosing, signal processing, segmentation, feature extraction, etc.
URI: https://hdl.handle.net/10356/141307
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
TU YIXUAN_dissertation0528.pdf
  Restricted Access
1.61 MBAdobe PDFView/Open

Page view(s)

249
Updated on Jan 27, 2023

Download(s)

8
Updated on Jan 27, 2023

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.