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Content Provider | IET Digital Library |
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Author | Ntalampiras, Stavros |
Abstract | There are several diseases (e.g. asthma, pneumonia etc.) affecting the human respiratory apparatus altering its airway path substantially, thus characterising its acoustic properties. This work unfolds an automatic audio signal processing framework achieving classification between normal and abnormal respiratory sounds. Thanks to a recent challenge, a real-world dataset specifically designed to address the needs of the specific problem is available to the scientific community. Unlike previous works in the literature, the authors take advantage of information provided by several stethoscopes simultaneously, i.e. elaborating at the acoustic sensor network level. To this end, they employ two features sets extracted from different domains, i.e. spectral and wavelet. These are modelled by convolutional neural networks, hidden Markov models and Gaussian mixture models. Subsequently, a synergistic scheme is designed operating at the decision level of the best-performing classifier with respect to each stethoscope. Interestingly, such a scheme was able to boost the classification accuracy surpassing the current state of the art as it is able to identify respiratory sound patterns with a 66.7% accuracy. |
Starting Page | 223 |
Ending Page | 228 |
Page Count | 6 |
ISSN | 17519675 |
Volume Number | 14 |
e-ISSN | 17519683 |
Issue Number | Issue 4, Jun (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-spr/14/4 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0487 |
Journal | IET Signal Processing |
Publisher Date | 2020-02-21 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Abnormal Respiratory Sounds Acoustic Property Acoustic Sensor Network Level Airway Path Audio Signal Processing Automatic Audio Signal Automatic Classification Biology And Medical Computing Biomedical Engineering Biomedical Measurement And Imaging Classification Accuracy Collaborative Framework Combinatorial Mathematics Convolutional Neural Nets Convolutional Neural Network Decision Level Digital Signal Processing Diseases Feature Extraction Gaussian Mixture Model Gaussian Processes Haemodynamics Hidden Markov Model Human Respiratory Apparatus Knowledge Engineering Technique Learning in AI Markov Processes Medical Signal Processing Neural Computing Technique Normal Respiratory Sounds Pattern Classification Pneumodynamics Probability Theory Real-world Dataset Respiratory Sound Pattern Scientific Community Signal Classification Signal Processing And Detection Spectral Domain Speech And Audio Signal Processing Statistics Stethoscope Stochastic Linearised SCUC Wavelet Domain |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering |
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