Sensors (May 2018)

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

  • 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

Journal volume & issue
Vol. 18, no. 5
p. 1387


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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.