Dataset Information

Heart rate variability analysis using robust period detection.

ABSTRACT:

Objective

Heart rate variability (HRV) analysis, which is an important tool for activity assessment of the cardiac autonomic nervous system, very often includes the estimation of power spectra for series of interbeat intervals (IBI). Ectopic beats and artifacts have a destructive effect on the standard methods (Fourier transform, FFT) for frequency analysis. This study investigates an alternative method for calculation of the periodogram using a robust period detection (RPD).

Method

Error free IBI series of 5?minutes for 221 subjects during one day were artificially distorted by randomly changing IBI values by ±15-40%. The low to high frequency rate (LF/HF) were calculated from periodograms estimated by the FFT, RPD and Lomb (LSP) methods for both error free and distorted series and for series with removed beats. Log transformed LF/HF values for series with distorted/removed beats were compared to undistorted values by linear regression.

Results

For series with 10% of distorted IBI values the regression analysis between distorted and undistorted series showed a goodness of fit, coefficient and intercept of 0.98, 0.94 and 0.02, respectively. In comparison, the values of these parameters were (0.34, 0.46, -1.61) and (0.28, 0.42,-1.32) for the FFT and LSP methods, respectively. Similarly, the comparison between series with removed and undistorted beats yielded goodness of fit, coefficient and intercept of (0.98, 0.96, -0.01), (0.93, 0.78, -0.02) and (0.98, 0.95, 0.19) for RPD, FFT and LSP, respectively.

Conclusion

The RPD method demonstrated superior performance compared to the FFT and LSP method by estimation of power spectral characteristics for HRV analysis.

SUBMITTER: Skotte JH

PROVIDER: S-EPMC4228159 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Heart rate variability analysis using robust period detection.

Skotte Jørgen H JH Kristiansen Jesper J

Biomedical engineering online 20140923

<h4>Objective</h4>Heart rate variability (HRV) analysis, which is an important tool for activity assessment of the cardiac autonomic nervous system, very often includes the estimation of power spectra for series of interbeat intervals (IBI). Ectopic beats and artifacts have a destructive effect on the standard methods (Fourier transform, FFT) for frequency analysis. This study investigates an alternative method for calculation of the periodogram using a robust period detection (RPD).<h4>Method</ ...[more]

PMID: 25248280

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Project description:Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby's overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby's stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant's HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant's clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients' health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

Project description:BackgroundUsing Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices.MethodsFeatures extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed.ResultsThe best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62-94.55% accuracy and 91.77-94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively.ConclusionThe tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

Dataset Information

Heart rate variability analysis using robust period detection.

Objective

Method

Results

Conclusion

Publications

Heart rate variability analysis using robust period detection.

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