Deep Learning Approach for Automatic Heartbeat Classification
Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional arrhythmia detection methods are time-consuming, e...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1400 |
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| author | Roger de T. Guerra Cristina K. Yamaguchi Stefano F. Stefenon Leandro dos S. Coelho Viviana C. Mariani |
| author_facet | Roger de T. Guerra Cristina K. Yamaguchi Stefano F. Stefenon Leandro dos S. Coelho Viviana C. Mariani |
| author_sort | Roger de T. Guerra |
| collection | DOAJ |
| description | Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional arrhythmia detection methods are time-consuming, error-prone, and often subjective, making it difficult for doctors to discern between distinct patterns of arrhythmia. To understand ECG signals, this study presents a multi-class classifier and an autoencoder with long short-term memory (LSTM) network layers for extracting signal properties on a dataset from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital (MIT-BIH). The suggested model had an accuracy rate of 98.57% on the arrhythmia dataset and 97.59% on the supraventricular dataset. In contrast to other deep learning models, the proposed model eliminates the problem of the gradient disappearing in classification tasks. |
| format | Article |
| id | doaj-art-db089f790b9f481da35198dda3be193a |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-db089f790b9f481da35198dda3be193a2025-08-20T02:53:23ZengMDPI AGSensors1424-82202025-02-01255140010.3390/s25051400Deep Learning Approach for Automatic Heartbeat ClassificationRoger de T. Guerra0Cristina K. Yamaguchi1Stefano F. Stefenon2Leandro dos S. Coelho3Viviana C. Mariani4Graduate Program in Electrical Engineering, Federal University of Parana, Curitiba 80242-980, PR, BrazilPostgraduate Program in Productive Systems in Association with UNIPLAC, UNC, UNESC, and UNIVILLE, Lages 88509-900, SC, BrazilGraduate Program in Electrical Engineering, Federal University of Parana, Curitiba 80242-980, PR, BrazilGraduate Program in Electrical Engineering, Federal University of Parana, Curitiba 80242-980, PR, BrazilDepartment of Electrical Engineering, Federal University of Parana, Curitiba 80242-980, PR, BrazilArrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional arrhythmia detection methods are time-consuming, error-prone, and often subjective, making it difficult for doctors to discern between distinct patterns of arrhythmia. To understand ECG signals, this study presents a multi-class classifier and an autoencoder with long short-term memory (LSTM) network layers for extracting signal properties on a dataset from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital (MIT-BIH). The suggested model had an accuracy rate of 98.57% on the arrhythmia dataset and 97.59% on the supraventricular dataset. In contrast to other deep learning models, the proposed model eliminates the problem of the gradient disappearing in classification tasks.https://www.mdpi.com/1424-8220/25/5/1400cardiac arrhythmia detectionmulticlass classificationdeep learning |
| spellingShingle | Roger de T. Guerra Cristina K. Yamaguchi Stefano F. Stefenon Leandro dos S. Coelho Viviana C. Mariani Deep Learning Approach for Automatic Heartbeat Classification Sensors cardiac arrhythmia detection multiclass classification deep learning |
| title | Deep Learning Approach for Automatic Heartbeat Classification |
| title_full | Deep Learning Approach for Automatic Heartbeat Classification |
| title_fullStr | Deep Learning Approach for Automatic Heartbeat Classification |
| title_full_unstemmed | Deep Learning Approach for Automatic Heartbeat Classification |
| title_short | Deep Learning Approach for Automatic Heartbeat Classification |
| title_sort | deep learning approach for automatic heartbeat classification |
| topic | cardiac arrhythmia detection multiclass classification deep learning |
| url | https://www.mdpi.com/1424-8220/25/5/1400 |
| work_keys_str_mv | AT rogerdetguerra deeplearningapproachforautomaticheartbeatclassification AT cristinakyamaguchi deeplearningapproachforautomaticheartbeatclassification AT stefanofstefenon deeplearningapproachforautomaticheartbeatclassification AT leandrodosscoelho deeplearningapproachforautomaticheartbeatclassification AT vivianacmariani deeplearningapproachforautomaticheartbeatclassification |