Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors

Aygun, Ayca; Ghasemzadeh, Hassan; Jafari, Roozbeh

doi:10.1109/jbhi.2019.2962627

Cited by 20 publications

(4 citation statements)

References 38 publications

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“…Recent WS research has shown promising efficacy in calculating HRV from noisy signals in both PPG and ECG signals [ 153 , 154 ]. This highlights the potential for HRV to be used as a preoperative tool calculated from WS.…”

Section: Feature Extractionmentioning

confidence: 99%

Wearable Sensors as a Preoperative Assessment Tool: A Review

Syversen,

Dosis,

Jayne

et al. 2024

Sensors

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Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

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Section: Feature Extractionmentioning

confidence: 99%

Wearable Sensors as a Preoperative Assessment Tool: A Review

Syversen,

Dosis,

Jayne

et al. 2024

Sensors

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show abstract

“…They reported that cubic interpolation can in some cases result in lower errors for long gaps. Finally, some works address artifact correction in the detection stage, using methods such as adaptive filtering, wavelet transform or feature extraction of the cardiac signal [ 27 , 28 ]. These approaches are beyond the scope of this paper, as they are signal specific, and many wearables do not allow exporting cardiac signals but event series.…”

Section: Introductionmentioning

confidence: 99%

Effects of Missing Data on Heart Rate Variability Metrics

Cajal

Hernando

Lázaro

et al. 2022

Sensors

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Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.

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“…Heart rate variability (HRV) is an important marker to assess autonomic nervous system (ANS) dynamics by measuring variations between consecutive heartbeats [1]. HRV variables have been widely used to evaluate anxiety disorder, depression, and psychotropic medication [2]. In particular, HRV indices in frequency domain describe power distributions with different frequency bands, which have been found to be reliable markers for assessing sympathetic (SNS) and parasympathetic nervous system (PNS) activities [1].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, HRV data can be obtained by measuring variations of RR intervals (RRI) in electrocardiography (ECG) recordings, or it can be derived from inter-beat intervals (IBI) of photoplethysmography (PPG) signals [2]. However, measuring HRV using ECG or PPG signals is still challenging, because these data recordings are vulnerable to measurement noises and motion artifacts, which have subsequent influence on HRV data analysis [2], [4]. Many machine learning techniques have been developed to address the various uncertainties of HRV data, such as the Gaussian modelling [4], ensemble deep learning [5], and Bayesian deep learning [6].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Estimation of Heart Rate Variability Using Low-rank Matrix Completion

Zhu

Zhang

et al. 2022

2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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Heart rate variability (HRV) is an important noninvasive parameter to assess the cardiac autonomic nervous system. In particular, spectrum matrices of HRV data have been widely used for physical and mental health monitoring. However, measurement uncertainties from data acquisition and physiological factors can easily affect the HRV spectrum and degrade outcomes of health monitoring. In this paper, we propose a new model for incomplete spectrum estimation of the HRV data based on matrix completion (MC). We show that our model performs efficiently when estimating missing entries for HRV spectra. Moreover, a refined model of matrix completion (RMC) is proposed that can be derived from correlation analysis of the HRV spectra. Two benchmark electrocardiography (ECG) datasets are retrieved and used to derive the HRV data, which are employed to evaluate the performance of our RMC method on the estimation of missing entries in the spectra. Furthermore, four different types of deep recurrent neural networks and the traditional MC method are used for a comparison study, and our RMC method obtains the least estimation error with different masking ratios. The experimental studies and comparison results demonstrate the advantages and robustness of our developed method for the estimation of incomplete HRV spectra.

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Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors

Cited by 20 publications

References 38 publications

Wearable Sensors as a Preoperative Assessment Tool: A Review

Wearable Sensors as a Preoperative Assessment Tool: A Review

Effects of Missing Data on Heart Rate Variability Metrics

Spectrum Estimation of Heart Rate Variability Using Low-rank Matrix Completion

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