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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Veröffentlicht in:	IEEE access Jg. 13; S. 114145 - 114158
Hauptverfasser:	Muthulakshmi, M., Venkatesan, K., Bahiyah Rahayu, Syarifah, Nayana Sree, K. L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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, overlapping auscultation characteristics and lack of universal clinical standards. Secondly, discrimination of obstructive respiratory diseases (COPD, asthma) and restrictive respiratory diseases (other) is critical, as they have different treatment and management strategies. Third, a clear distinction between COPD and asthma is of great concern, as treatment at the appropriate stage results in an open air passage in COPD and an improvement in shortness of breath in asthma. Although several techniques have been explored for diagnosing respiratory disease based on lung auscultation, there is still a need for an effective, low-cost, and faster solution suitable for real-time ROC detection. The time-frequency representations of audio signals are suitable to capture low-frequency information, as well as tonal and harmonic relationships. In contrast, deep learning architectures can learn complex, hierarchical, and high-level patterns from the spatio-temporal structures. In addition, extreme learning machines (ELM) can provide generalized performance with fewer training parameters. Hence, combining time-frequency representations, deep learning architecture, and ELM would result in the most reliable low-cost tool to predict ROC from lung auscultation. The fusion of deep features from different spatiotemporal structures outperforms individual features when fed into the ELM model, resulting in clear discrimination of obstructive and restrictive respiratory diseases. The proposed CNN-enhanced time-frequency features powered the ELM-based framework, yielding a test accuracy of 97.5% for the unseen test data considered. Thus, this study would be a useful aid for pulmonologists and would play a pivotal role in the accessibility to healthcare, early intervention, and long-term management of ROC disease.
AbstractList	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, overlapping auscultation characteristics and lack of universal clinical standards. Secondly, discrimination of obstructive respiratory diseases (COPD, asthma) and restrictive respiratory diseases (other) is critical, as they have different treatment and management strategies. Third, a clear distinction between COPD and asthma is of great concern, as treatment at the appropriate stage results in an open air passage in COPD and an improvement in shortness of breath in asthma. Although several techniques have been explored for diagnosing respiratory disease based on lung auscultation, there is still a need for an effective, low-cost, and faster solution suitable for real-time ROC detection. The time-frequency representations of audio signals are suitable to capture low-frequency information, as well as tonal and harmonic relationships. In contrast, deep learning architectures can learn complex, hierarchical, and high-level patterns from the spatio-temporal structures. In addition, extreme learning machines (ELM) can provide generalized performance with fewer training parameters. Hence, combining time-frequency representations, deep learning architecture, and ELM would result in the most reliable low-cost tool to predict ROC from lung auscultation. The fusion of deep features from different spatiotemporal structures outperforms individual features when fed into the ELM model, resulting in clear discrimination of obstructive and restrictive respiratory diseases. The proposed CNN-enhanced time-frequency features powered the ELM-based framework, yielding a test accuracy of 97.5% for the unseen test data considered. Thus, this study would be a useful aid for pulmonologists and would play a pivotal role in the accessibility to healthcare, early intervention, and long-term management of ROC disease.
Author	Venkatesan, K. Nayana Sree, K. L. Muthulakshmi, M. Bahiyah Rahayu, Syarifah
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SubjectTerms	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
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Title	Customized Spectro-Temporal CNN Feature Extraction and ELM-Based Classifier for Accurate Respiratory Obstruction Detection
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