An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier
Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electroc...
| Main Authors: | , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/95821/ |
| _version_ | 1848862229543256064 |
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| author | Hussein, Ahmed Faeq Hashim, Shaiful Jahari Rokhani, Fakhrul Zaman Wan Adnan, Wan Azizun |
| author_facet | Hussein, Ahmed Faeq Hashim, Shaiful Jahari Rokhani, Fakhrul Zaman Wan Adnan, Wan Azizun |
| author_sort | Hussein, Ahmed Faeq |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases. |
| first_indexed | 2025-11-15T13:13:42Z |
| format | Article |
| id | upm-95821 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:13:42Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-958212023-04-03T07:41:37Z http://psasir.upm.edu.my/id/eprint/95821/ An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier Hussein, Ahmed Faeq Hashim, Shaiful Jahari Rokhani, Fakhrul Zaman Wan Adnan, Wan Azizun Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed Hussein, Ahmed Faeq and Hashim, Shaiful Jahari and Rokhani, Fakhrul Zaman and Wan Adnan, Wan Azizun (2021) An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier. Sensors, 21 (7). art. no. 2311. pp. 1-18. ISSN 1424-8220 https://www.mdpi.com/1424-8220/21/7/2311 10.3390/s21072311 |
| spellingShingle | Hussein, Ahmed Faeq Hashim, Shaiful Jahari Rokhani, Fakhrul Zaman Wan Adnan, Wan Azizun An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier |
| title | An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier |
| title_full | An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier |
| title_fullStr | An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier |
| title_full_unstemmed | An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier |
| title_short | An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams time-frequency analysis incorporating a multi-class SVM classifier |
| title_sort | automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ecg and choi-williams time-frequency analysis incorporating a multi-class svm classifier |
| url | http://psasir.upm.edu.my/id/eprint/95821/ http://psasir.upm.edu.my/id/eprint/95821/ http://psasir.upm.edu.my/id/eprint/95821/ |