Dataset Information

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording.

ABSTRACT:

Background and purpose

With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24-48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.

Methods

In this report, we propose a new nonlinear time-frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.

Results

As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.

Conclusion

The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

SUBMITTER: Wu HT

PROVIDER: S-EPMC5932763 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording.

Wu Hau-Tieng HT Soliman Elsayed Z EZ

Biomedical engineering online 20180503 1

<h4>Background and purpose</h4>With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24-48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.<h4>Methods</h4>In this report, we propose a new nonlinear time-frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from e ...[more]

PMID: 29720178

Similar Datasets

Project description:BackgroundCongenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with congenital LQTS and may not always recognize the accompanying ECG features. In addition, a proportion of disease carriers do not display obvious abnormalities on their ECG. Combined, this can cause underdiagnosing of this potentially life-threatening disease.MethodsThis study presents 1D convolutional neural network models trained to identify genotype positive LQTS patients from electrocardiogram as input. The deep learning (DL) models were trained with a large 10-s 12-lead ECGs dataset provided by Amsterdam UMC and externally validated with a dataset provided by University Hospital Leuven. The Amsterdam dataset included ECGs from 10000 controls, 172 LQTS1, 214 LQTS2, and 72 LQTS3 patients. The Leuven dataset included ECGs from 2200 controls, 32 LQTS1, and 80 LQTS2 patients. The performance of the DL models was compared with conventional QTc measurement and with that of an international expert in congenital LQTS (A.A.M.W). Lastly, an explainable artificial intelligence (AI) technique was used to better understand the prediction models.ResultsOverall, the best performing DL models, across 5-fold cross-validation, achieved on average a sensitivity of 84 ± 2%, 90 ± 2% and 87 ± 6%, specificity of 96 ± 2%, 95 ± 1%, and 92 ± 4%, and AUC of 0.90 ± 0.01, 0.92 ± 0.02, and 0.89 ± 0.03, for LQTS 1, 2, and 3 respectively. The DL models were also shown to perform better than conventional QTc measurements in detecting LQTS patients. Furthermore, the performances held up when the DL models were validated on a novel external cohort and outperformed the expert cardiologist in terms of specificity, while in terms of sensitivity, the DL models and the expert cardiologist in LQTS performed the same. Finally, the explainable AI technique identified the onset of the QRS complex as the most informative region to classify LQTS from non-LQTS patients, a feature previously not associated with this disease.ConclusionsThis study suggests that DL models can potentially be used to aid cardiologists in diagnosing LQTS. Furthermore, explainable DL models can be used to possibly identify new features for LQTS on the ECG, thus increasing our understanding of this syndrome.

Project description:ObjectivesProlonged heart rate-corrected QT(QTc) interval is related to ventricular arrhythmia and cardiovascular mortality, with considerably high prevalence of type 2 diabetes. Additionally, long-term glycaemic variability could be a significant risk factor for diabetic complications in addition to chronic hyperglycaemia. We compared the associations of long-term glycaemic variability versus sustained chronic hyperglycaemia with the QTc interval among type 2 diabetes patients.MethodsIn this cross-sectional study, 2904 type 2 diabetes patients were recruited who had undergone at least four fasting plasma glucose (FPG) and 2-hour postprandial plasma glucose (PPG) measurements (at least once for every 3 months, respectively) during the preceding year. Long-term glycaemic variabilities of FPG and 2-hour PPG were assessed by their standard deviations (SD-FPG and SD-PPG, respectively), and chronic fasting and postprandial hyperglycaemia were assessed by their means (M-FPG and M-PPG, respectively). HbA1c was also determined upon enrolment to assess current overall glycaemic control. QTc interval was estimated from resting 12-lead electrocardiograms, and more than 440 ms was considered abnormally prolonged.ResultsPatients with prolonged QTc interval (≥440 ms) had greater M-FPG, M-PPG, SD-PPG and HbA1c than those with normal QTc interval but comparable SD-FPG. QTc interval was correlated with M-FPG, M-PPG, SD-PPG and HbA1c (r = 0.133, 0.153, 0.245 and 0.207, respectively, p = 0.000) but not with SD-FPG (r = 0.024, p = 0.189). After adjusting for metabolic risk factors via multiple linear regression analysis, SD-PPG, M-PPG and HbA1c (t = 12.16, 2.69 and 10.16, respectively, p = 0.000) were the major independent contributors to the increased QTc interval. The proportion of prolonged QTc interval increased significantly from 10.9% to 14.2% to 26.6% for the first (T1) to second (T2) to third (T3) tertiles of SD-PPG. After adjusting via multiple logistic regression analysis, the odd ratios of prolonged QTc interval of the T2 and T3 versus the T1 of SD-PPG were 1.15 (95% CI, 0.82-1.60) and 2.62 (1.92-3.57), respectively.ConclusionsIncreased long-term variability of PPG is a strong independent risk factor for prolonged QTc interval in type 2 diabetes patients, in addition to long-term postprandial hyperglycaemia and current HbA1c.

Project description:We investigated the association between the heart rate-corrected QT interval (QTc interval) measured by standard electrocardiography and heart rate variability (HRV) in patients with type 2 diabetes mellitus (T2DM). From March 1, 2009, to December 12, 2009, 411 patients with T2DM who underwent resting 12-lead electrocardiography and cardiovascular autonomic function testing concurrently without the exclusion criteria were consecutively recruited in this cross-sectional study. Time- and frequency-domain HRV variables were assessed for 5 minutes by beat-to-beat HRV recording. The QT interval was corrected for the heart rate using Bazett's formula. QTc interval measurements of >440 ms were considered abnormally prolonged. The mean age and diabetes duration were 56.3 ± 10.6 years and 9.6 ± 7.3 years, respectively. A total of 90 patients had QTc interval prolongation (21.9%). The participants with a prolonged QTc interval were older (59.4 ± 10.1 years vs 55.5 ± 10.6 years, P = .002), were more likely to be a woman (72.2% vs 51.7%, P = .001), had a higher prevalence of hypertension (46.7% vs 33.4%, P = .022), had a higher hemoglobin A1c level (8.8% ± 2.2% vs 8.2% ± 1.8%, P = .045), and had decreased values for the variables measuring HRV, except for the low frequency (LF)/high frequency (HF) ratio (total power [TP], 147.7 [74.1-335.9] ms vs 328.7 [185.7-721.7] ms, P = .002). After adjusting for multiple confounders, QTc interval prolongation was associated with the lowest quartile of the HRV parameters of TP (odds ratio [OR] = 3.99; 95% confidence interval [CI]: 2.29-6.96), HF (OR = 3.20; 95% CI: 1.84-5.58), LF (OR = 3.68; 95% CI: 2.10-6.43), standard deviation of the normal-to-normal interval (OR = 3.31; 95% CI: 1.89-5.77), and root-mean-square of the successive differences (OR = 1.98; 95% CI: 1.13-3.47) in patients with T2DM. Decreased values for the variables measuring HRV, except for the LF/HF ratio, might be associated with QTc interval prolongation in patients with T2DM.

Project description:Measures of heart rate variability (and heart rate more generally) are providing powerful insights into the physiological drivers of behaviour. Resting heart rate variability (HRV) can be used as an indicator of individual differences in temperament and reactivity to physical and psychological stress. There is increasing interest in deriving such measures from free ranging wild animals, where individuals are exposed to the natural and anthropogenic stressors of life. We describe a robust, externally mounted heart rate monitor for use in wild mammals, deployed here on wild breeding adult female grey seals (Halichoerus grypus), that delivers millisecond precise measures of inter beat intervals (IBIs), allowing computation of resting HRV parameters. Based on Firstbeat™ heart rate belts, our system allows for remote, continuous recording of IBI data from over 30 individuals simultaneously at ranges of up to 200m. We assessed the accuracy of the IBI data provided by the Firstbeat™ system using concurrent IBI data derived from in-field electrocardiogram (ECG) recordings. Bland-Altmann analyses demonstrated high correspondence between the two sets of IBI data, with a mean difference of 0.87±0.16ms. We used generalized additive mixed-effects models to examine the impact of the default Firstbeat™ software artefact correction procedure upon the generation of anomalous data (flats and stairs). Artefact correction and individual activity were major causes of flats and stairs. We used simulations and models to assess the impact of these errors on estimates of resting HRV and to inform criteria for subsampling relatively error free IBI traces. These analyses allowed us to establish stringent filtering procedures to remove traces with excessive numbers of artefacts, including flats and stairs. Even with strict criteria for removing potentially erroneous data, the abundance of data yielded by the Firstbeat™ system provides the potential to extract robust estimates of resting HRV. We discuss the advantages and limitations of our system for applications beyond the study system described here.

Dataset Information

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording.

Background and purpose

Methods

Results

Conclusion

Publications

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets