Automated real-time method for ventricular heartbeat classification. (February 2019)

• Record Type: Journal Article
• Authors: Ortín, Silvia; Soriano, Miguel C.; Alfaras, Miquel; Mirasso, Claudio R.
• Is Part Of: Computer methods and programs in biomedicine. Volume 169(2019)
• Page Start: 1
• Page End: 8
• Publication Date: 2019-02
• Subjects: Biomedical signal processing -- ECG heartbeat classification -- Reservoir computing -- Template matching; 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.2018.11.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A fully automated and real-time heartbeat ventricular arrhythmia classifier based on a single lead designed to be fast and efficient in the training and evaluation phase. The arrhythmia classifier can be applied to different ECG leads with excellent performance. Potential application to wearables with unconventionally placed electrodes. Trained and evaluated with different databases, allowing the inter-patient and inter-database classification. Abstract: Background and objective: In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity. Methods: The proposed method uses an Echo State Network (ESN) to classify ECG signals following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations with an inter-patient scheme. To achieve real-time classification, the classifier itself and the feature extraction approach are fast and computationally efficient. In addition, our approach allows transferring the knowledge from one database to another without additional training. Results: The classification performance of the proposed model is validated on the MIT-BIH arrhythmia and INCART databases. The sensitivity and precision of the proposed method for MIT-BIH arrhythmia database are 95.3 and 88.8 for the modified lead II and 90.9 and 89.2 for the V1 lead. The results reported are further compared to the existing methodologies in literature. Our methodology is a competitive single lead ventricular heartbeat classifier, that is comparable to state-of-the-art algorithms using multiple leads. Conclusions: The proposed fully automated, single-lead and real-time heartbeat classifier of ventricular heartbeats reports an improved classification accuracy in different leads of the evaluated databases in comparison with other single lead heartbeat classifiers. These results open the possibility of applying our methodology to wearable long-term monitoring devices with an unconventional placement of the electrodes.