J Med Syst - Applying cybernetic technology to diagnose human pulmonary sounds.

Tópicos

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Resumo

Chest auscultation is a crucial and efficient method for diagnosing lung disease; however, it is a subjective process that relies on physician experience and the ability to differentiate between various sound patterns. Because the physiological signals composed of heart sounds and pulmonary sounds (PSs) are greater than 120?Hz and the human ear is not sensitive to low frequencies, successfully making diagnostic classifications is difficult. To solve this problem, we constructed various PS recognition systems for classifying six PS classes: vesicular breath sounds, bronchial breath sounds, tracheal breath sounds, crackles, wheezes, and stridor sounds. First, we used a piezoelectric microphone and data acquisition card to acquire PS signals and perform signal preprocessing. A wavelet transform was used for feature extraction, and the PS signals were decomposed into frequency subbands. Using a statistical method, we extracted 17 features that were used as the input vectors of a neural network. We proposed a 2-stage classifier combined with a back-propagation (BP) neural network and learning vector quantization (LVQ) neural network, which improves classification accuracy by using a haploid neural network. The receiver operating characteristic (ROC) curve verifies the high performance level of the neural network. To expand traditional auscultation methods, we constructed various PS diagnostic systems that can correctly classify the six common PSs. The proposed device overcomes the lack of human sensitivity to low-frequency sounds and various PS waves, characteristic values, and a spectral analysis charts are provided to elucidate the design of the human-machine interface.

Resumo Limpo

chest auscult crucial effici method diagnos lung diseas howev subject process reli physician experi abil differenti various sound pattern physiolog signal compos heart sound pulmonari sound pss greater hz human ear sensit low frequenc success make diagnost classif difficult solv problem construct various ps recognit system classifi six ps class vesicular breath sound bronchial breath sound tracheal breath sound crackl wheez stridor sound first use piezoelectr microphon data acquisit card acquir ps signal perform signal preprocess wavelet transform use featur extract ps signal decompos frequenc subband use statist method extract featur use input vector neural network propos stage classifi combin backpropag bp neural network learn vector quantiz lvq neural network improv classif accuraci use haploid neural network receiv oper characterist roc curv verifi high perform level neural network expand tradit auscult method construct various ps diagnost system can correct classifi six common pss propos devic overcom lack human sensit lowfrequ sound various ps wave characterist valu spectral analysi chart provid elucid design humanmachin interfac

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