Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권보경 | - |
dc.date.accessioned | 2024-08-27T01:31:37Z | - |
dc.date.available | 2024-08-27T01:31:37Z | - |
dc.date.issued | 2022-05-12 | - |
dc.identifier.citation | SCIENTIFIC REPORTS, v. 12, no. 1, article no. 7889, page. 1-11 | en_US |
dc.identifier.issn | 2045-2322 | en_US |
dc.identifier.uri | https://www.nature.com/articles/s41598-022-11726-3 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/191796 | - |
dc.description.abstract | Respiratory sounds are expressed as nonlinear and nonstationary signals, whose unpredictability makes it difficult to extract significant features for classification. Static cepstral coefficients such as Mel-frequency cepstral coefficients (MFCCs), have been used for classification of lung sound signals. However, they are modeled in high-dimensional hyperspectral space, and also lose temporal dependency information. Therefore, we propose shifted delta-cepstral coefficients in lower-subspace (SDC-L) as a novel feature for lung sound classification. It preserves temporal dependency information of multiple frames nearby same to original SDC, and improves feature extraction by reducing the hyperspectral dimension. We modified EMD algorithm by adding a stopping rule to objectively select a finite number of intrinsic mode functions (IMFs). The performances of SDC-L were evaluated with three machine learning techniques (support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF)) and two deep learning algorithms (multilayer perceptron (MLP) and convolutional neural network (cNN)) and one hybrid deep learning algorithm combining cNN with long short term memory (LSTM) in terms of accuracy, precision, recall and Fl-score. We found that the first 2 IMFs were enough to construct our feature. SVM, MLP and a hybrid deep learning algorithm (cNN plus LSTM) outperformed with SDC-L, and the other classifiers achieved equivalent results with all features. Our findings show that SDC-L is a promising feature for the classification of lung sound signals. | en_US |
dc.description.sponsorship | This work was supported by the research fund of Hanyang University ERICA [Grant Number: HY-2021000000001819, 2021]. | en_US |
dc.language | en_US | en_US |
dc.publisher | NATURE PORTFOLIO | en_US |
dc.relation.ispartofseries | v. 12, no. 1, article no. 7889;1-11 | - |
dc.title | A temporal dependency feature in lower dimension for lung sound signal classification | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1038/s41598-022-11726-3 | en_US |
dc.relation.page | 1-11 | - |
dc.relation.journal | SCIENTIFIC REPORTS | - |
dc.contributor.googleauthor | Kwon, Amy M. | - |
dc.contributor.googleauthor | Kang, Kyungtae | - |
dc.relation.code | 2022036495 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | DEPARTMENT OF ARTIFICIAL INTELLIGENCE | - |
dc.identifier.pid | amykwon | - |
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