Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension
Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a fatal cardiovascular disease. A novel method for non-invasive initial diagnosis of the CHD-PAH was put forward in this work. First, original heart sounds were segmented into each cardiac cycle by using double-thr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123000365 |
| _version_ | 1797796447032180736 |
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| author | Pengyue Ma Bingbing Ge Hongbo Yang Tao Guo Jiahua Pan Weilian Wang |
| author_facet | Pengyue Ma Bingbing Ge Hongbo Yang Tao Guo Jiahua Pan Weilian Wang |
| author_sort | Pengyue Ma |
| collection | DOAJ |
| description | Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a fatal cardiovascular disease. A novel method for non-invasive initial diagnosis of the CHD-PAH was put forward in this work. First, original heart sounds were segmented into each cardiac cycle by using double-threshold adaptive method. According to clinical auscultation, the pathological information of CHD-PAH is concentrated in S2, so the time-frequency features in both of an entire cardiac cycle and S2 were extracted. Then the time-frequency features combine with the deep learning features to form a feature vector. It is the fusion feature, which will be input into a classifier. Finally, the majority voting algorithm was used to obtain the optimal classification results. A classification accuracy of 88.61% was achieved using this novel method. Three points are essential: • A double-threshold adaptive method is used to segment heart sound into each cardiac cycle. • The time-frequency domain features in both of an entire cardiac cycle and S2 were extracted, which are combined with deep learning features to form the fusion feature. • The XGBoost was used as three-class classifier for the classification of normal, CHD and CHD-PAH. The majority voting algorithm was used to obtain the optimal classification results. |
| first_indexed | 2024-03-13T03:33:12Z |
| format | Article |
| id | doaj.art-38fee47ec97c48d9b7fd389147b63743 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-13T03:33:12Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-38fee47ec97c48d9b7fd389147b637432023-06-24T05:17:04ZengElsevierMethodsX2215-01612023-01-0110102032Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertensionPengyue Ma0Bingbing Ge1Hongbo Yang2Tao Guo3Jiahua Pan4Weilian Wang5Yunnan University, Fuwai Yunnan Cardiovascular Hospital, ChinaYunnan University, Fuwai Yunnan Cardiovascular Hospital, ChinaYunnan University, Fuwai Yunnan Cardiovascular Hospital, ChinaYunnan University, Fuwai Yunnan Cardiovascular Hospital, ChinaYunnan University, Fuwai Yunnan Cardiovascular Hospital, ChinaCorresponding author.; Yunnan University, Fuwai Yunnan Cardiovascular Hospital, ChinaPulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a fatal cardiovascular disease. A novel method for non-invasive initial diagnosis of the CHD-PAH was put forward in this work. First, original heart sounds were segmented into each cardiac cycle by using double-threshold adaptive method. According to clinical auscultation, the pathological information of CHD-PAH is concentrated in S2, so the time-frequency features in both of an entire cardiac cycle and S2 were extracted. Then the time-frequency features combine with the deep learning features to form a feature vector. It is the fusion feature, which will be input into a classifier. Finally, the majority voting algorithm was used to obtain the optimal classification results. A classification accuracy of 88.61% was achieved using this novel method. Three points are essential: • A double-threshold adaptive method is used to segment heart sound into each cardiac cycle. • The time-frequency domain features in both of an entire cardiac cycle and S2 were extracted, which are combined with deep learning features to form the fusion feature. • The XGBoost was used as three-class classifier for the classification of normal, CHD and CHD-PAH. The majority voting algorithm was used to obtain the optimal classification results.http://www.sciencedirect.com/science/article/pii/S2215016123000365Fusion of time-frequency domain features and depth features and classification of XGBoost. |
| spellingShingle | Pengyue Ma Bingbing Ge Hongbo Yang Tao Guo Jiahua Pan Weilian Wang Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension MethodsX Fusion of time-frequency domain features and depth features and classification of XGBoost. |
| title | Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension |
| title_full | Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension |
| title_fullStr | Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension |
| title_full_unstemmed | Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension |
| title_short | Application of time-frequency domain and deep learning fusion feature in non-invasive diagnosis of congenital heart disease-related pulmonary arterial hypertension |
| title_sort | application of time frequency domain and deep learning fusion feature in non invasive diagnosis of congenital heart disease related pulmonary arterial hypertension |
| topic | Fusion of time-frequency domain features and depth features and classification of XGBoost. |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123000365 |
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