Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach
| Autor: | Søren Hjøllund Jensen, Sadasivan Puthusserypady, Gerald Hirsch, Erik S. Poulsen |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
02 engineering and technology Cross-validation 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Heart rate variability Ensemble classifier Empirical mode decomposition Mathematics business.industry General Engineering Atrial activity Atrial fibrillation Pattern recognition medicine.disease Linear discriminant analysis Computer Science Applications Random forest Random subspace method Automatic detection 020201 artificial intelligence & image processing Artificial intelligence business F1 score Test data |
| Zdroj: | Hirsch, G, Jensen, S H, Poulsen, E S & Puthusserypady, S 2021, ' Atrial fibrillation detection using heart rate variability and atrial activity : A hybrid approach ', Expert Systems with Applications, vol. 169, 114452 . https://doi.org/10.1016/j.eswa.2020.114452 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2020.114452 |
| Popis: | Goal: Develop a real-time hybrid scheme for the automatic detection of atrial fibrillation (AF), based on the RR interval (RRI) time series and the atrial activity (AA) derived from the electrocardiogram (ECG) signals. Method: The whole scheme was developed and tested on the MIT-BIH AF database (AFDB). First the R-peak detection and the filtering was performed. Following, all features regarding the RRI time series and AA were extracted. These features were then fed into three popular classifiers (boosted trees (BoT), random forest (RF), and linear discriminant analysis (LDA) with random subspace method (RSM)). Sampling training and test data from the same subject (23 overall) was strictly avoided. Furthermore, for each ECG, individual performance statistics were analyzed to elaborate on the subject-wise performance dependencies. Results: From a 4-fold cross validation (CV) analysis, the RF classifier provided the best results with a sensitivity (Sn), specificity (Sp), accuracy (Acc), and F1 score of 98.0%, 97.4%, 97.6%, and 97.1%, respectively for the AF prediction. Test results on individual ECG's however, have slightly reduced these performances to 95.9%, 96.1%, 97.4% and 88.4%, respectively. Conclusion: Using the RRI features alone were found to provide satisfying prediction performance of the model. The addition of AA features to the model enhanced the model performance by up to 3%. Overall, the results obtained in this study are comparable or even superior to the state-of-the-art algorithms using RRI and AA based features. Significance The hybrid model allows us to detect AF even with regular RRI. The performance was evaluated under real-world conditions, and no manual labelling, exclusion, or pre-processing was performed. Furthermore, we evaluated the performance for each ECG individually and kept the subjects strictly unknown for the classifier. Finally, we show that the overall performance on a data set, especially from a standard CV, results in an over-optimistic estimation. |
| Databáze: | OpenAIRE |
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