0349 Robust inter-beat interval estimation algorithm using clustering method
Authors: Kim Myeong Seok , Ann Soo Young , Ha Tae Kyoung , Yi Ho Dong , Lee Young Jun 
Published: May 2022

Abstract

Introduction
The heart rate is a basic indicator of the state of the human body and therefore it is important to measure the heart rate continuously, reliably, and accurately. However, it is difficult to measure the heart rate in various environments due to the limitations of Contact sensors, such as ECG. Therefore, heart rate measurement using non-contact sensors such as PVDF, Radar, and cameras has been widely studied. Among these non-contact sensors to measure the heart rate, the PVDF sensor, installed under a bed or chair, can measure the heart rate from the J-peak of Ballistocardiograph (BCG). However, it is challenging to accurately measure the heart rate due to noise-induced by the external environment. To overcome these challenges in this study, we have proposed a new algorithm for accurate heart rate measurement using PVDF sensor. The proposed algorithm includes estimators and clustering techniques to estimate the beat-to-beat of BCG signals.

Methods
In order to continuously estimate inter-beat interval, basic, auto-correlation, average magnitude difference function, maximum amplitude pairs, and a Bayesian approach are used to obtain basic information to calculate inter-beat interval. Auto-correlation is an estimator that estimates heart rate intervals by calculating for all discrete lags. The average magnitude difference function (AMDF), which is often used for pitch tracking, also calculates for discrete lags like auto-correlation. If the waveforms of BCG are similar, the calculated value with AMDF is small. Therefore, the reciprocal of the ADMF output is used to take the larger value for the most likely interval. This is a complementary relationship from auto-correlation because the noise characteristics are different. Maximum amplitude pairs is an estimator used for indirect peak detectors with the amplitude information of the signal. The estimated IBI was determined by combining the estimation results of three estimators with a Bayesian approach. After the estimated IBI was obtained, continuously estimated inter-beat intervals were clustered into locally similar values. The inter-beat interval values were determined by voting for the most estimated interval value in the clustered sets.All inter-beat intervals estimated by the Bayesian method have their own probability density function and the confidence value was calculated using the difference between the largest peak and the second largest peak of the probability density function. By excluding inter-beat intervals with low confidence values, the accuracy of the estimation was improved. After, the final continuous inter-beat intervals were calculated by performing the ectopic beats removal algorithm and interpolation.

Results
To evaluate the performance, BCG data from PVDF sensor was simultaneously measured with PSG. The PSG datasets consisted of three male patients with moderate sleep apnea, a patient with snoring problems, and a normal person. Because of the difference in timing between the peak signal of ECG and that of BCG, less than 1 HR count was allowed when the inter-beat intervals were converted to HR. When the 21-hour inter-beat intervals were compared, the difference in HR was less than 1 in 91.4%. The rest 8.6% had a difference of 1 HR or more, and it was confirmed that 6.9% of them were caused by movement, and the remaining 1.7% were caused by a temporary failure of the sensor.

Conclusion
The proposed algorithm showed better coverage by 18.71% compared to the coverage of 72.69% from the previous study. It was confirmed that the proposed algorithm provides a more accurate heart rate than previous studies, and the measured inter-beat intervals will be used to estimate, not only the heart rate but also the sleep stage in the future.

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