Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes

Computers in Biology and Medicine

Volumn 39, Issue 9, 2009, Pages 824-843

  Bahoura, Mohammed ^a  

^a UNIVERSITÉ DU QUÉBEC À RIMOUSKI   (Canada)

Author keywords

Gaussian mixture models; Linear predictive coding; McNemar's test; Mel frequency cepstral coefficients; Multi layer perceptron; Receiver operating characteristic; Respiratory sounds; Statistical significance; Vector quantization; Wavelet transform

Indexed keywords

GAUSSIAN MIXTURE MODELS; LINEAR PREDICTIVE CODING; MCNEMAR'S TEST; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; MULTI-LAYER PERCEPTRON; RECEIVER OPERATING CHARACTERISTIC; RESPIRATORY SOUNDS; STATISTICAL SIGNIFICANCE;

BLIND SOURCE SEPARATION; COMMUNICATION CHANNELS (INFORMATION THEORY); FEATURE EXTRACTION; MAGNETOSTRICTIVE DEVICES; MIXTURES; NEURAL NETWORKS; OBJECT RECOGNITION; WAVELET TRANSFORMS;

VECTOR QUANTIZATION;

ABNORMAL RESPIRATORY SOUND; ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; ASTHMA; AUTOMATED PATTERN RECOGNITION; CLASSIFICATION ALGORITHM; CLINICAL ARTICLE; CONTROLLED STUDY; DIGITAL FILTERING; FOURIER TRANSFORMATION; HUMAN; KERNEL METHOD; MCNEMAR TEST; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SIGNAL PROCESSING; WAVEFORM; WHEEZING;

ACOUSTICS; ARTIFICIAL INTELLIGENCE; ASTHMA; AUSCULTATION; CASE-CONTROL STUDIES; DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; FOURIER ANALYSIS; HUMANS; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; RESPIRATORY SOUNDS; ROC CURVE; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 68049095980     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2009.06.011     Document Type: Article

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