On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios
| Autor: | Estrella Everss-Villalba, Francisco-Manuel Melgarejo-Meseguer, Zaida Molins-Bordallo, Francisco-Javier Gimeno-Blanes, Jose-Antonio Flores-Yepes, José Luis Rojo-Álvarez, Arcadi García-Alberola, Manuel Blanco-Velasco |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
02 engineering and technology QRS detection ECG long-term monitoring Holter 7-day computer.software_genre lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry Beat detection 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Sensitivity (control systems) Electrical and Electronic Engineering Instrumentation Noise (signal processing) 010401 analytical chemistry Atomic and Molecular Physics and Optics 0104 chemical sciences Term (time) Ecg monitoring Key (cryptography) 020201 artificial intelligence & image processing Data mining Holter monitoring computer |
| Zdroj: | Sensors (Basel, Switzerland) Sensors; Volume 18; Issue 5; Pages: 1387 Sensors, Vol 18, Iss 5, p 1387 (2018) |
| ISSN: | 1424-8220 |
| Popis: | Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems. |
| Databáze: | OpenAIRE |
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