Impact of observational error on heart rate variability analysis

Petelczyc, Monika; Gieraltowski, Jan; Żogała-Siudem, Barbara; Siudem, Grzegorz

doi:10.1016/j.heliyon.2020.e03984

Cited by 10 publications

(12 citation statements)

References 18 publications

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“…However, derivatives amplify noise and thus any errors in the R-peak detection will result in even larger errors as further derivatives are taken. When calculating HRV the 2nd derivative between successive heart beat readings is used, and thus any small errors in the heart beat detection will compound into much larger errors in your subsequent HRV result [4]. As such, a heart beat detector algorithm that can accurately and precisely identify the R-peaks in an ECG, is vital in obtaining a useful and representative HRV.…”

Section: Introductionmentioning

confidence: 99%

A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors

Porr

Howell

2019

Preprint

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The R peak detection of an ECG signal is the basis of virtually any further processing and any error caused by this detection will propagate to further processing stages. Despite this, R peak detection algorithms and annotated databases often allow large error tolerances around 10%, masking any error introduced. In this paper we have revisited popular ECG R peak detection algorithms by applying sample precision error margins. For this purpose we have created a new open access ECG database with sample precision labelling of both standard Einthoven I, II, III leads and from a chest strap. 25 subjects were recorded and filmed while sitting, solving a math test, operating a handbike, walking and jogging. Our results show that using an error margin with sample precision, common R peak detection algorithms perform much worse than previously reported. In addition, there are significant performance differences between detectors which can have detrimental effects on applications such as heartrate variability, thus leading to meaningless results.

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Section: Introductionmentioning

confidence: 99%

A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors

Porr

Howell

2019

Preprint

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“…The leakage power is limited by the nanoscale transistors in the implemented process and can be further improved with advanced techniques or process [33]. The temporal resolution is typically limited by the sampling period (e.g., 33 μs in [6] and [34]) in conventional frame-based sampling systems [35]. The temporal resolution in feature extraction mode is measured by overlaying multiple of extracted R labels of the CAP and measuring the timing uncertainty, as shown in Fig.…”

Section: Measurement Resultsmentioning

confidence: 99%

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

Corradi

Shi

et al. 2022

IEEE J. Solid-State Circuits

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“…The device has a sufficient battery life and internal memory to perform monitoring for an entire day. We selected 512 Hz as the sampling frequency for ECG data collection [39,40]. The sampling frequency is sufficient to accurately extract HR and HRV [41].…”

Section: Data Collectionmentioning

confidence: 99%

Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis

Cao¹,

Azimi²,

Sarhaddi³

et al. 2022

J Med Internet Res

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Background Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings. Objective This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor. Methods We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices. Results Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. Conclusions The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests.

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Impact of observational error on heart rate variability analysis

Cited by 10 publications

References 18 publications

A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors

A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis

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