Key result
The proposed missing RR interval complement method using respiratory features significantly improved complementation accuracy compared to 2nd and 3rd order spline interpolation for long-term missing data.
Why the study?
Monitoring of biometric signals can be unreliable due to missing RR intervals.
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A novel dynamic linear model utilizing respiratory features may improve the reliability of biometric monitoring by accurately complementing missing RR intervals.
Advanced technologies in bioinstrumentation allows easy monitoring of biometric signals such as electrocardiogram (ECG) and respiration. In order to improve unreliable monitoring due to missing RR intervals (RRIs), this paper proposes a missing RRI complement method based on respiratory features. The proposed method rst selects respiratory features from the measured data based on Granger causality, and then complements the missing RRIs based on a dynamic linear model (DLM) for RRIs with selected features. The performance of the proposed method was evaluated by comparison with standard spline interpolation, standard regression, and a vector autoregressive (VAR) model. The results are discussed in terms of the effectiveness of respiratory feature selection and utilization of the DLM to capture temporal uctuations.
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Nomura et al. (2022) studied Missing RR intervals in ECG (n=20). Missing RRI complement method based on respiratory features and dynamic linear model vs. Spline interpolation, standard regression, and vector autoregressive (VAR) model was evaluated on Accuracy of missing RRI complementation (Correlation coefficient, RMSE, DTW, LF/HF error) (p=<0.01). The proposed missing RR interval complement method using respiratory features significantly improved complementation accuracy compared to 2nd and 3rd order spline interpolation for long-term missing data.
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