Key result
Approximate nearest neighbors RRI algorithm achieves lower error and faster computational speed than CKNN.
Why the study?
Heart rate variability features cannot be extracted accurately from R-R interval data when artifacts like premature atrial contractions and premature ventricular contractions are present.
Does the ANN-based RRI modification algorithm improve RMSE and computational speed compared to CKNN for RRI data with PAC and PVC?
Does the ANN-based RRI modification algorithm improve RMSE and computational speed compared to CKNN for RRI data with PAC and PVC?
The ANN-based RRI modification algorithm effectively reduces errors in HRV analysis caused by PAC and PVC artifacts with high computational speed.
Heart rate variability (HRV) analysis plays an essential role in healthcare. HRV features cannot be extracted accurately from the R-R interval (RRI) when RRI data contains artifacts. Previous research for modifying RRI data with artifacts considered premature atrial contraction (PAC) and premature ventricular contraction (PVC), which are the most common types of extrasystoles occurring every day in healthy persons. This research proposed three new RRI modification algorithms for PAC and PVC using nearest neighbor search (NNS) algorithms: k-nearest neighbors (KNN), clustering-KNN (CKNN), and approximate nearest neighbors (ANN). The present work demonstrated that the ANNbased RRI modification (ANN-RM) algorithm achieved lower root mean squared errors (RMSEs) than the CKNN-based RRI modification algorithm and the highest computational speed. The RMSEs of ANN-RM for PAC and PVC were 23.0 ms and 26.2 ms, respectively.
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Chen et al. (2023) studied Premature atrial contraction and premature ventricular contraction. Approximate nearest neighbors (ANN)-based RRI modification (ANN-RM) algorithm vs. Clustering-KNN (CKNN)-based RRI modification algorithm was evaluated on Root mean squared errors (RMSEs). The approximate nearest neighbors-based RRI modification algorithm achieved lower root mean squared errors (23.0 ms for PAC and 26.2 ms for PVC) and higher computational speed than the CKNN algorithm.
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