Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature
Heart rate abnormalities can lead to many cardiovascular diseases such as heart arrythmia, heart failure, heart valve disease and many more. Some cardiovascular disease can cause death. Abnormalities signal feature can be seen using electrocardiogram. Electrocardiogram is an electric signal record f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Andalas
2021-11-01
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| Series: | Jurnal Nasional Teknik Elektro |
| Online Access: | http://jnte.ft.unand.ac.id/index.php/jnte/article/view/829 |
| _version_ | 1819209844344750080 |
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| author | Sevia Indah Purnama Mas Aly Afandi |
| author_facet | Sevia Indah Purnama Mas Aly Afandi |
| author_sort | Sevia Indah Purnama |
| collection | DOAJ |
| description | Heart rate abnormalities can lead to many cardiovascular diseases such as heart arrythmia, heart failure, heart valve disease and many more. Some cardiovascular disease can cause death. Abnormalities signal feature can be seen using electrocardiogram. Electrocardiogram is an electric signal record from heart activity. Normal heart and abnormal heart have a different electrocardiogram signal pattern. This research is aim to detect abnormality from heart rate using electrocardiogram abnormality signal feature. Abnormality signal pattern can be used to classify normal and abnormal heart rate. Abnormality feature consists of P signal condition, R signal condition, P R interval rate, and double R interval. Electrocardiogram data that used in this study is obtain from MIT-BIH Arrythmia database. 20 electrocardiogram data have been used to see detection and classification performance while classifying normal and abnormal heart rate. Research result shows that feature based has 90.00% in accuracy, 90.00%in precision, and 90.00% in sensitivity while classify normal and abnormal heart rate. Research result can conclude that abnormality feature can be used to classify normal and abnormal heart rate. This method can be used for embedded system device that has limitation in memory and size. |
| first_indexed | 2024-12-23T06:01:44Z |
| format | Article |
| id | doaj.art-68c26b4c32794d2b9e707ab8644b41d3 |
| institution | Directory Open Access Journal |
| issn | 2302-2949 2407-7267 |
| language | English |
| last_indexed | 2024-12-23T06:01:44Z |
| publishDate | 2021-11-01 |
| publisher | Universitas Andalas |
| record_format | Article |
| series | Jurnal Nasional Teknik Elektro |
| spelling | doaj.art-68c26b4c32794d2b9e707ab8644b41d32022-12-21T17:57:39ZengUniversitas AndalasJurnal Nasional Teknik Elektro2302-29492407-72672021-11-0110310.25077/jnte.v10n3.829.2021309Electrocardiogram Abnormal Classification Based on Abnormality Signal FeatureSevia Indah Purnama0Mas Aly Afandi1Institut Teknologi Telkom PurwokertoInstitut Teknologi Telkom PurwokertoHeart rate abnormalities can lead to many cardiovascular diseases such as heart arrythmia, heart failure, heart valve disease and many more. Some cardiovascular disease can cause death. Abnormalities signal feature can be seen using electrocardiogram. Electrocardiogram is an electric signal record from heart activity. Normal heart and abnormal heart have a different electrocardiogram signal pattern. This research is aim to detect abnormality from heart rate using electrocardiogram abnormality signal feature. Abnormality signal pattern can be used to classify normal and abnormal heart rate. Abnormality feature consists of P signal condition, R signal condition, P R interval rate, and double R interval. Electrocardiogram data that used in this study is obtain from MIT-BIH Arrythmia database. 20 electrocardiogram data have been used to see detection and classification performance while classifying normal and abnormal heart rate. Research result shows that feature based has 90.00% in accuracy, 90.00%in precision, and 90.00% in sensitivity while classify normal and abnormal heart rate. Research result can conclude that abnormality feature can be used to classify normal and abnormal heart rate. This method can be used for embedded system device that has limitation in memory and size.http://jnte.ft.unand.ac.id/index.php/jnte/article/view/829 |
| spellingShingle | Sevia Indah Purnama Mas Aly Afandi Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature Jurnal Nasional Teknik Elektro |
| title | Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature |
| title_full | Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature |
| title_fullStr | Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature |
| title_full_unstemmed | Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature |
| title_short | Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature |
| title_sort | electrocardiogram abnormal classification based on abnormality signal feature |
| url | http://jnte.ft.unand.ac.id/index.php/jnte/article/view/829 |
| work_keys_str_mv | AT seviaindahpurnama electrocardiogramabnormalclassificationbasedonabnormalitysignalfeature AT masalyafandi electrocardiogramabnormalclassificationbasedonabnormalitysignalfeature |