Customized Spectro-Temporal CNN Feature Extraction and ELM-Based Classifier for Accurate Respiratory Obstruction Detection

The accurate prediction based on lung auscultation of respiratory obstruction conditions (ROC), such as chronic obstructive pulmonary disease (COPD) and asthma, is a challenging task due to the availability of small datasets, ambient noise, variability between patients, high computational power, ove...

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Vydáno v:	IEEE access Ročník 13; s. 114145 - 114158
Hlavní autoři:	Muthulakshmi, M., Venkatesan, K., Bahiyah Rahayu, Syarifah, Nayana Sree, K. L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Artificial neural networks Asthma Audio data Audio signals Auscultation Chronic obstructive pulmonary disease CNN enhanced time-frequency features Deep learning ELM Feature extraction feature fusion Health services Low cost Lungs Machine learning Mel frequency cepstral coefficient Noise Pneumonia Real time Representations Respiratory diseases respiratory obstruction condition Spectrogram Time-frequency analysis
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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