Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

Rossi, Alessio; Pedreschi, Dino; Clifton, David A.; Morelli, Davide

doi:10.3390/s20247122

Cited by 12 publications

(15 citation statements)

References 43 publications

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“…Another striking result is that residual errors are typically lower for longer-term (σ, lf) than for shorter-term (rmssd, hf) HRV features. This finding is consistent with [ 28 ]; it shows that high-level applications (e.g., inner states monitoring or heart event prevention) using high-frequency HRV should carefully consider the missing sample rate when running its predictions.…”

Section: Discussionsupporting

confidence: 90%

“…Another limitation of this study is that HRV features were computed without accounting for the influence of the window size (W = 60 s). Future work should also consider the combined effect of the Lack Index (L) and the window size (W) while extracting features from real-life recordings (similar work has been done with simulated data loss [ 28 ]). Bearing that in mind, whether or not pulse rate variability and HRV should be considered as distinct cardiac measures is still an open question.…”

Section: Discussionmentioning

confidence: 99%

“…The impact of data loss on feature extraction (assumption (ii)) was shown in a simulation study [ 28 ], in which samples were removed from time windows of an ECG-based IBI signal, following a Gilbert burst model. As the missing sample rate increased, low-frequency components of the heart rate signal tended to be under-estimated, while the high-frequency components tended to be over-estimated.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Vila

Godin

Charbonnier

et al. 2021

Sensors

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Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Vila

Godin

Charbonnier

et al. 2021

Sensors

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“…In case of missing data, one needs to impute the missing data using a suitable method before modelling [5,27]. Data are missed due to various reasons, and researchers must determine the type of missing data [2,28,29].…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm

Almasinejad

Golabpour

Meybodi

et al. 2021

Journal of Healthcare Engineering

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Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions. Missing data is usual in research, and the extent of it will determine how misinterpreted the conclusions will be. All methods of parameter estimation and prediction models are based on the assumption that the data are complete. Extensive missing data will result in false predictions and increased bias. In the present study, a novel method has been proposed for the imputation of medical missing data. The method determines what algorithm is suitable for the imputation of missing data. To do so, a multiobjective particle swarm optimization algorithm was used. The algorithm imputes the missing data in a way that if a prediction model is applied to the data, both specificity and sensitivity will be optimized. Our proposed model was evaluated using real data of gastric cancer and acute T-cell leukemia (ATLL). First, the model was then used to impute the missing data. Then, the missing data were imputed using deletion, average, expectation maximization, MICE, and missForest methods. Finally, the prediction model was applied for both imputed datasets. The accuracy of the prediction model for the first and the second imputation methods was 0.5 and 16.5, respectively. The novel imputation method was more accurate than similar algorithms like expectation maximization and MICE.

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“…The python hrv-analysis library (https://pypi.org/project/hrv-analysis, accesed on: 1 September 2020) is used to remove outliers and ectopic beats from signal in each long-term ECG timeseries, as showed in Rossi et al [30]. In total, 2.46 ± 4.29% of inter-beats intervals in the dataset were ectopic beats (i.e., disturbance of the cardiac rhythm frequently related to the electrical conduction system of the heart) or missing values induced by motion artefacts.…”

Section: Data Preprocessingmentioning

confidence: 99%

SDNN24 Estimation from Semi-Continuous HR Measures

Morelli

Rossi

Bartoloni³

et al. 2021

Sensors

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The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users’ heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk.

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Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

Cited by 12 publications

References 43 publications

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm

SDNN24 Estimation from Semi-Continuous HR Measures

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