Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implem...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/136847 |
| _version_ | 1811682146714124288 |
|---|---|
| author | Oh, Shu Lih Ng, Eddie Yin Kwee Tan, Ru San Acharya, U. Rajendra |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Oh, Shu Lih Ng, Eddie Yin Kwee Tan, Ru San Acharya, U. Rajendra |
| author_sort | Oh, Shu Lih |
| collection | NTU |
| description | Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG. |
| first_indexed | 2024-10-01T03:52:12Z |
| format | Journal Article |
| id | ntu-10356/136847 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:52:12Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1368472023-03-04T17:20:31Z Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats Oh, Shu Lih Ng, Eddie Yin Kwee Tan, Ru San Acharya, U. Rajendra School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Arrhythmia Ambulatory Electrocardiogram Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG. Accepted version 2020-01-31T04:18:26Z 2020-01-31T04:18:26Z 2018 Journal Article Oh, S. L., Ng, E. Y. K., Tan, R. S., & Acharya, U. R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in biology and medicine, 102, 278-287. doi:10.1016/j.compbiomed.2018.06.002 0010-4825 https://hdl.handle.net/10356/136847 10.1016/j.compbiomed.2018.06.002 29903630 2-s2.0-85048210995 102 278 287 en Computers in Biology and Medicine © 2018 Elsevier Ltd. All rights reserved. This paper was published in Computers in Biology and Medicine and is made available with permission of Elsevier Ltd. application/pdf |
| spellingShingle | Engineering::Mechanical engineering Arrhythmia Ambulatory Electrocardiogram Oh, Shu Lih Ng, Eddie Yin Kwee Tan, Ru San Acharya, U. Rajendra Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats |
| title | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats |
| title_full | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats |
| title_fullStr | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats |
| title_full_unstemmed | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats |
| title_short | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats |
| title_sort | automated diagnosis of arrhythmia using combination of cnn and lstm techniques with variable length heart beats |
| topic | Engineering::Mechanical engineering Arrhythmia Ambulatory Electrocardiogram |
| url | https://hdl.handle.net/10356/136847 |
| work_keys_str_mv | AT ohshulih automateddiagnosisofarrhythmiausingcombinationofcnnandlstmtechniqueswithvariablelengthheartbeats AT ngeddieyinkwee automateddiagnosisofarrhythmiausingcombinationofcnnandlstmtechniqueswithvariablelengthheartbeats AT tanrusan automateddiagnosisofarrhythmiausingcombinationofcnnandlstmtechniqueswithvariablelengthheartbeats AT acharyaurajendra automateddiagnosisofarrhythmiausingcombinationofcnnandlstmtechniqueswithvariablelengthheartbeats |