A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Saleem, Shiza; Khandoker, Ahsan H.; Alkhodari, Mohanad; Hadjileontiadis, Leontios J.; Jelinek, Herbert F.

doi:10.1038/s41598-022-21776-2

Cited by 7 publications

(6 citation statements)

References 75 publications

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“…To determine the running TP, QT, ST interval, and QRS width within a measurement window, we considered a sufficient number of heartbeats. The annotated NN interval data underwent a Single Cosinor Analysis fitting algorithm [ 32 ] to ensure a 24-hour circadian cycle for all recordings. The reference achrophase angle was set at 0°, representing the 00:00 hour.…”

Section: Methodsmentioning

confidence: 99%

“…After obtaining the starting hour, the 24-hour data was standardized to commence at midnight for all patients. The SDROM-ADF filter was further applied to eliminate noise from each hour of the Holter ECG data [ 32 ]. We took the average of the relevant intervals through this hourly window to capture the variations over the entire 24-hour period accurately.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

Al Younis,

Hadjileontiadis,

Khandoker

et al. 2024

PLoS ONE

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Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcare expenditures, with anticipated escalation in the future. It is essential to classify HF patients into three groups based on their ejection fraction: reduced (HFrEF), mid-range (HFmEF), and preserved (HFpEF), such as for diagnosis, risk assessment, treatment choice, and the ongoing monitoring of heart failure. Nevertheless, obtaining a definitive prediction poses challenges, requiring the reliance on echocardiography. On the contrary, an electrocardiogram (ECG) provides a straightforward, quick, continuous assessment of the patient’s cardiac rhythm, serving as a cost-effective adjunct to echocardiography. In this research, we evaluate several machine learning (ML)-based classification models, such as K-nearest neighbors (KNN), neural networks (NN), support vector machines (SVM), and decision trees (TREE), to classify left ventricular ejection fraction (LVEF) for three categories of HF patients at hourly intervals, using 24-hour ECG recordings. Information from heterogeneous group of 303 heart failure patients, encompassing HFpEF, HFmEF, or HFrEF classes, was acquired from a multicenter dataset involving both American and Greek populations. Features extracted from ECG data were employed to train the aforementioned ML classification models, with the training occurring in one-hour intervals. To optimize the classification of LVEF levels in coronary artery disease (CAD) patients, a nested cross-validation approach was employed for hyperparameter tuning. HF patients were best classified using TREE and KNN models, with an overall accuracy of 91.2% and 90.9%, and average area under the curve of the receiver operating characteristics (AUROC) of 0.98, and 0.99, respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8–9 am, and 10–11 pm were the ones that contributed to the highest classification accuracy. The results pave the way for creating an automated screening system tailored for patients with CAD, utilizing optimal measurement timings aligned with their circadian cycles.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

Al Younis,

Hadjileontiadis,

Khandoker

et al. 2024

PLoS ONE

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“…HRV signal was calculated for each hour of the Holter ECG recordings using Pan Tompkins 41 and pre-processed for noise using the SDROM-ADF filter 42 . Cosinor fitting analysis was used to fix the starting point of the 24-h circadian rhythm at 12:00 am and HRV features extracted.…”

Section: Methodsmentioning

confidence: 99%

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction

Saleem

Khandoker

Alkhodari

et al. 2023

Sci Rep

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Heart failure is characterized by sympathetic activation and parasympathetic withdrawal leading to an abnormal autonomic modulation. Beta-blockers (BB) inhibit overstimulation of the sympathetic system and are indicated in heart failure patients with reduced ejection fraction. However, the effect of beta-blocker therapy on heart failure with preserved ejection fraction (HFpEF) is unclear. ECGs of 73 patients with HFpEF > 55% were recruited. There were 56 patients in the BB group and 17 patients in the without BB (NBB) group. The HRV analysis was performed for the 24-h period using a window size of 1,4 and 8-h. HRV measures between day and night for both the groups were also compared. Percentage change in the BB group relative to the NBB group was used as a measure of difference. RMSSD (13.27%), pNN50 (2.44%), HF power (44.25%) and LF power (13.53%) showed an increase in the BB group relative to the NBB group during the day and were statistically significant between the two groups for periods associated with high cardiac risk during the morning hours. LF:HF ratio showed a decrease of 3.59% during the day. The relative increase in vagal modulated RMSSD, pNN50 and HF power with a decrease in LF:HF ratio show an improvement in the parasympathetic tone and an overall decreased risk of a cardiac event especially during the morning hours that is characterized by a sympathetic surge.

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“…The 24-hour circadian ECG was fixed to start at 12:00 am for all the patients using a cosinor ftting analysis [ 32 ]. The SDROM-ADF filter [ 33 ] was used to filter each hour of the Holter ECG data for noise. Time- and frequency-domain features were extracted from the denoised ECGs, as explained next.…”

Section: Methodsmentioning

confidence: 99%

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Al Younis,

Hadjileontiadis,

Al Shehhi

et al. 2023

PLoS ONE

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Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8–9 am, and 10–11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.

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A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Cited by 7 publications

References 75 publications

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

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