Accurate deep neural network model to detect cardiac arrhythmia on more than 10, 000 individual subject ECG records. (December 2020)

• Record Type: Journal Article
• Authors: Yildirim, Ozal; Talo, Muhammed; Ciaccio, Edward J.; Tan, Ru San; Acharya, U Rajendra
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Arrhythmia detection -- Deep neural networks -- Ecg signals -- 12-lead ECG; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105740
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: DNN model is proposed to detect arrhythmia. More than 10, 000 individual subject ECG records subject records are used. Two different scenarios are employed: (i) reduced rhythms (seven rhythm types) and (ii) merged rhythms (four rhythm types). Achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. System can aid cardiologists in the accurate detection of arrhythmia accurately. Abstract: Background and objective: Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. Methods: Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. Results: We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. Conclusion: Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10, 000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.