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

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

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Despite the wide literature on R-wave detection nyx 22 brush 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 Latest Product Releases & Innovations – Stay Updated! 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.