On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

• Main Authors: Francisco-Manuel Melgarejo-Meseguer, Estrella Everss-Villalba, Francisco-Javier Gimeno-Blanes, Manuel Blanco-Velasco, Zaida Molins-Bordallo, José-Antonio Flores-Yepes, José-Luis Rojo-Álvarez, Arcadi García-Alberola
• Format: Article
• Language: English
• Published: MDPI AG 2018-05-01
• Series: Sensors
• Subjects: QRS detection; ECG; long-term monitoring; Holter; 7-day
• Online Access: http://www.mdpi.com/1424-8220/18/5/1387

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.