Abstract
The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.
Highlights
The RR interval (RRI) fluctuation in an electrocardiogram (ECG) is known as heart rate variability (HRV), which is a physiological activity reflecting the cardiovascular control exerted by the autonomic nervous systems (ANS) [1,2]
These root mean squared error (RMSE) of the four methods were calculated through a simulation repeated 30 times using all of the eighteen pieces of validation data
Locally weighted partial least squares (LWPLS)-RI achieved the best HRV analysis performance. These results demonstrate the usefulness of the proposed LWPLS-RI for HRV analysis from R-R interval (RRI)
Summary
The RR interval (RRI) fluctuation in an electrocardiogram (ECG) is known as heart rate variability (HRV), which is a physiological activity reflecting the cardiovascular control exerted by the autonomic nervous systems (ANS) [1,2]. Since the ANS is a control system that acts largely unconsciously and regulates functions such as the heart rate, digestion, respiration, perspiration, and body temperature, it is related to various diseases. A brief introduction of HRV analysis is described in Appendix A. It is known that changes in sleep condition affect HRV [5,6]. An HRV-based drowsiness detection method using linear discriminant analysis (LDA) was developed by Vicente et al [7].
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