Differential Diagnosis of Asthma and COPD Based on Multivariate Pulmonary Sounds Analysis.

Objective: Asthma and chronic obstructive pulmonary disease (COPD) can be confused in clinical diagnosis due to overlapping symptoms. The purpose of this study is to develop a method based on multivariate pulmonary sounds analysis for differential diagnosis of the two diseases.

Methods: The recorded 14-channel pulmonary sound data are mathematically modeled using multivariate (or, vector) autoregressive (VAR) model, and the model parameters are fed to the classifier.

A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds.

Goal: The aim of this study is to find a useful methodology to classify multiple distinct pulmonary conditions including the healthy condition and various pathological types, using pulmonary sounds data.

Methods: Fourteen-channel pulmonary sounds data of 40 subjects (healthy and pathological, where the pathologies are of obstructive and restrictive types) are modeled using a second order 250-point vector autoregressive model. The estimated model parameters are fed to support vector machine and Gaussian mixture model (GMM) classifiers which are used in various configurations, resulting in eight different methodologies in total.

Computerized diagnosis of respiratory disorders. SVM based classification of VAR model parameters of respiratory sounds.

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced METHODS for Studying Cardiovascular and Respiratory Systems".

Objectives: This work proposes an algorithm for diagnostic classification of multi-channel respiratory sounds.

Methods: 14-channel respiratory sounds are modeled assuming a 250-point second order vector autoregressive (VAR) process, and the estimated model parameters are used to feed a support vector machine (SVM) classifier.

Exploring an optimal vector autoregressive model for multi-channel pulmonary sound data.

The purpose of this study is to find a useful mathematical model for multi-channel pulmonary sound data. Vector auto-regressive (VAR) model schema is adopted and the best set of arguments, namely, the order and sample size of the model and the sampling rate of the data, is aimed to be determined. Both conventional prediction error criteria and a set of three new criteria which are derived specifically for pulmonary sound signals are used to evaluate the success of the model.

Acoustic mapping of the lung based on source localization of adventitious respiratory sound components.

The aim of this study is to devise a methodology to estimate and depict the source locations of respiratory adventitious sound components in the lungs, particularly crackles, associated with certain pulmonary diseases. Using the multichannel respiratory sound signals recorded on the chest wall, we have tried to locate the sources of crackling sounds. The source localization is performed using basic independent component analysis (basic ICA) followed by an evaluation of the mixing coefficients in a center of weights approach, where after the ICA, by taking the relevant mixing matrix coefficients and assuming them to be placed on the microphone locations, the estimated sound source location is calculated as the center of those weights.