Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/24/7246 |
| _version_ | 1827699823009071104 |
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| author | Yu-Hung Chuang Chia-Ling Huang Wen-Whei Chang Jen-Tzung Chien |
| author_facet | Yu-Hung Chuang Chia-Ling Huang Wen-Whei Chang Jen-Tzung Chien |
| author_sort | Yu-Hung Chuang |
| collection | DOAJ |
| description | Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra- and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area. |
| first_indexed | 2024-03-10T13:58:56Z |
| format | Article |
| id | doaj.art-3cdf18ccf1b44df59c454a202577ed6c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:58:56Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3cdf18ccf1b44df59c454a202577ed6c2023-11-21T01:18:43ZengMDPI AGSensors1424-82202020-12-012024724610.3390/s20247246Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived VectorcardiographyYu-Hung Chuang0Chia-Ling Huang1Wen-Whei Chang2Jen-Tzung Chien3Institute of Electrical and Computer Engineering, National Chiao-Tung University, Hsinchu 30010, TaiwanInstitute of Electrical and Computer Engineering, National Chiao-Tung University, Hsinchu 30010, TaiwanInstitute of Electrical and Computer Engineering, National Chiao-Tung University, Hsinchu 30010, TaiwanInstitute of Electrical and Computer Engineering, National Chiao-Tung University, Hsinchu 30010, TaiwanMyocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra- and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area.https://www.mdpi.com/1424-8220/20/24/7246electrocardiographyvectorcardiographymyocardial infarctionlong short-term memorysplinemultilayer perceptron |
| spellingShingle | Yu-Hung Chuang Chia-Ling Huang Wen-Whei Chang Jen-Tzung Chien Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography Sensors electrocardiography vectorcardiography myocardial infarction long short-term memory spline multilayer perceptron |
| title | Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography |
| title_full | Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography |
| title_fullStr | Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography |
| title_full_unstemmed | Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography |
| title_short | Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography |
| title_sort | automatic classification of myocardial infarction using spline representation of single lead derived vectorcardiography |
| topic | electrocardiography vectorcardiography myocardial infarction long short-term memory spline multilayer perceptron |
| url | https://www.mdpi.com/1424-8220/20/24/7246 |
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