Reliability evaluation of R-R interval measurement status for time domain heart rate variability analysis with wearable ECG devices

Eguchi, Kana; Aoki, Ryôzô; Yoshida, Kazuhiro; Yamada, Tomonori

doi:10.1109/embc.2017.8037072

Cited by 10 publications

(15 citation statements)

References 9 publications

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“…We expect that the accuracy of fHRV feature will be improved by adding our proposed method to the RRI outlier processing method in fHRV analysis, even if the complemented RRIs are not perfectly correct, and this may contribute to improve the estimation accuracy in the last step of HRV analysis. Note that our method assumes in advance that RRI outliers are excluded using the conventional methods [12,14,15], and that RRI time series data are divided into two different sectors: a data sector consisting of spontaneous RRIs without any missing data, and a data loss sector with missing RRIs (Fig. 5).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, since both approaches use only time information of RRI, miscalculated RRIs within a reasonable duration are not detected as outliers. The latter approach excludes RRI outliers on the basis of their measurement status [15]. This method focuses on the fact that the electric potential of the QRS complex (hereafter QRS potential), which is the absolute difference between local maxima and local minima of an arbitrary QRS complex (Fig.…”

Section: Related Researchmentioning

confidence: 99%

“…Since RRI is measured from two adjacent R waves, this method calculates RRI measurement status based on the measurement status of the corresponding R waves, and RRIs with low reliability are excluded as outliers. Combining all the aforementioned RRI outlier exclusion techniques is an effective way to improve the accuracy of time domain measures of HRV (tHRV) [15].…”

Section: Related Researchmentioning

confidence: 99%

“…er processing methods; our previously reported outlier exclusion method [15] alone (hereafter conventional method), and the method combining RRI outlier exclusion [15] and missing RRI complement we propose here (hereafter proposed method), to those derived from the reference RRIs. The main purpose of this experiment was to con rm two points: (a) the appropriateness of the complemented values in the missing RRI sector, and (b) the effectiveness of RRI complement in fHRV analysis.…”

Section: Outlinementioning

confidence: 99%

“…Assuming a situation that often occurs in actual HRV analysis when using shirt-type ECG devices, we employed the Pan-Tompkins algorithm (hereafter PTA), which is an R wave detection algorithm that does not appropriately handle noise or artifacts [18], for ECG waveform feature extraction to con rm the effectiveness of different RRI outlier processing methods. The proposed method comprised a function for RRI outlier exclusion based on both time and measurement status [15], and a function for complementing missing RRIs before fHRV analysis. In the experiment we conducted, we rst compared resampled RRI tachograms derived from each method, and then checked four kinds of fHRV which frequently used in fHRV analysis: the logarithmic values of HRV LF [log(HRV LF )] and HRV HF [log(HRV HF )], and the secondary feature values of HRV LF/HF and HRV HF/(LF+HF) .…”

Section: Outlinementioning

confidence: 99%

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R-R Interval Outlier Processing for Heart Rate Variability Analysis using Wearable ECG Devices

Eguchi

Aoki

Shimauchi

et al. 2018

ABE

Self Cite

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Electrocardiograms (ECGs) captured by wearable ECG devices readily contain artifacts due to measurement faults. Since artifacts and R waves have quite similar frequency characteristics, R wave misdetection or R-R interval (RRI) miscalculation may result. Aiming at accurate analysis of heart rate variability (HRV), this paper proposes a new RRI outlier processing method consisting of three steps: evaluating RRI reliability, excluding RRI outlier, and complementing missing RRI. In the rst step, the method evaluates the measurement status of all detected R waves and calculates RRI reliability based on the measurement status of a combination of the measurement status of two R waves. Since we target wearable ECG devices used in non-medical environment, the method evaluates R waves based on the threshold electric potential for left ventricular hypertrophy, and determines those exceeding the threshold as artifacts. The method accordingly sets lower reliability to RRIs containing R waves evaluated as artifacts. In the second step, the method excludes all RRIs with low reliability as outliers. These steps may be effective for HRV measures in the time domain, but are not suf cient for analyzing HRV measures in the frequency domain. Resampling the time series RRI data, which is essential for analyzing HRV in the frequency domain, may produce outliers if the target RRIs contain missing values. Our method accordingly complements missing RRIs before data resampling based on RRI characteristics. We postulate that consecutive changes in RRIs follow a simple formula consisting of three components: direct current, low frequency, and high frequency. Our method complements missing values according to the formula, which is calculated from RRIs time series regarded as having been properly measured. To con rm the effectiveness of the method before applying it to ECGs recorded by wearable devices, we evaluated all the steps using pseudo-ECGs generated arti cially by adding noise and artifacts to open ECG data. Initial evaluation results showed that the proposed method outperformed conventional method regarding the precision of both time and frequency domain measures of HRV.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Section: Outlinementioning

confidence: 99%

Section: Outlinementioning

confidence: 99%

See 3 more Smart Citations

R-R Interval Outlier Processing for Heart Rate Variability Analysis using Wearable ECG Devices

Eguchi

Aoki

Shimauchi

et al. 2018

ABE

Self Cite

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Data analysis targeting healthcare-support applications using Internet-of-Things sensors

Chibá

Eguchi

Kurasawa

2019

Chemical, Gas, and Biosensors for Internet of Things and Related Applications

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R-R Interval Estimation for Wearable Electrocardiogram Based on Single Complex Wavelet Filtering and Morphology-Based Peak Selection

et al. 2021

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Recent innovations in wearable electrocardiogram (ECG) devices have enabled various personal healthcare applications based on heart rate variability (HRV). However, wearable ECGs rarely undergo visual inspection by medical experts, hence may contain noise and artifacts. Because apparent changes in the recorded ECGs caused by noise and artifacts may hamper the extraction of QRS complexes, an R-R interval (RRI) estimation algorithm tolerant to these measurement faults is required as the initial step toward HRV analysis using wearable ECGs. This paper proposes a semi-real-time RRI estimation for wearable ECGs utilizing a two-stage structure. In the preprocessing stage, we use a complex-valued wavelet that can adaptively fit to morphological variations of the QRS complex while retaining computing resources for extracting the QRS complex features. In the decision stage, we make use of complex-valued features and select appropriate QRS complexes in consideration of three features: peak magnitude, peak location, and peak morphology (phase). Initial evaluations show that the QRS complex detection performance of the proposed method achieved the F1 score of 0.952 ± 0.040 when targeting pseudo ECG data created from open data assuming wearable ECGs, and of 0.986 ± 0.018 when targeting actual ECG data recorded by using a shirttype wearable ECG device during an exercise activity. Furthermore, the proposed method was able to suppress overlook or misdetection of QRS complexes, so the obtained RRIs are closer to the reference RRIs. The proposed method therefore contributes to achieving accurate HRV analysis using wearable ECGs in terms of obtaining accurate RRIs.

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Reliability evaluation of R-R interval measurement status for time domain heart rate variability analysis with wearable ECG devices

Cited by 10 publications

References 9 publications

R-R Interval Outlier Processing for Heart Rate Variability Analysis using Wearable ECG Devices

R-R Interval Outlier Processing for Heart Rate Variability Analysis using Wearable ECG Devices

Data analysis targeting healthcare-support applications using Internet-of-Things sensors

R-R Interval Estimation for Wearable Electrocardiogram Based on Single Complex Wavelet Filtering and Morphology-Based Peak Selection

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