ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit

Rebergen, Dennis J.; Nagaraj, Sunil Belur; Rosenthal, Eric S.; Bianchi, Matt T.; Putten, Michel J.A.M. van; Westover, M. Brandon

doi:10.1007/s10877-017-9999-9

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Raw ECG signals from the Philips monitor were first preprocessed using a modified version of the Pan-Tompkins algorithm [ 19 ], enabling detection of R-peaks of the ECG and calculation of NN-intervals. Artefacts within the NN-intervals were corrected using the ADARRI method [ 20 ]. After preprocessing, the cleaned tachogram was split in subsequent windows of 5 min.…”

Section: Methodsmentioning

confidence: 99%

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

Wijk

Quinten

Rossum

et al. 2023

Scand J Trauma Resusc Emerg Med

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Background Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients. Methods Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups. Results In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time. Conclusions We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017.

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Section: Methodsmentioning

confidence: 99%

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

Wijk

Quinten

Rossum

et al. 2023

Scand J Trauma Resusc Emerg Med

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“…In terms of novel algorithms, Rebergen and colleagues [ 38 ] published the methodology of a simple algorithm for the detection of artifacts in the R-R interval time series, which is highly relevant for the quantification of heart rate variability. The authors correctly assumed that very fast fluctuations in the R-R interval time series (absolute differences) would identify both missed R spikes as well as erroneous “premature” detections and showed that this simple approach, which is patient-independent, outperformed two existing algorithms currently used and described in the scientific literature.…”

Section: Technical Developmentsmentioning

confidence: 99%

Journal of clinical monitoring and computing end of year summary 2018: hemodynamic monitoring and management

Saugel

Flick

Bendjelid

et al. 2019

J Clin Monit Comput

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Hemodynamic management is a mainstay of patient care in the operating room and intensive care unit (ICU). In order to optimize patient treatment, researchers investigate monitoring technologies, cardiovascular (patho-) physiology, and hemodynamic treatment strategies. The Journal of Clinical Monitoring and Computing (JCMC) is a well-established and recognized platform for publishing research in this field. In this review, we highlight recent advancements and summarize selected papers published in the JCMC in 2018 related to hemodynamic monitoring and management.

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“…In each 270-second epoch we extracted timings of R peaks 18 and converted the ECG to a binary sequence, where R peaks are indicated by a "1" and all other points indicated by "0". The 270-second epochs with spurious R peaks were identified using the ADARRI 19 method. 270-second epochs with less than 20 R peaks per minute were also identified, as they are not physiologically possible.…”

Section: Introductionmentioning

confidence: 99%

Sleep staging from electrocardiography and respiration with deep learning

Sun

Ganglberger

Panneerselvam

et al. 2019

Sleep

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Study Objective:Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. Therefore, we hypothesize that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.Methods: Using a dataset including 8,682 polysomnographs, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals.Results: ECG in combination with the abdominal respiratory effort achieve the best performance for staging all five sleep stages with a Cohen's kappa of 0.600 (95% confidence interval 0.599 -0.602); and 0.762 (0.760 -0.763) for discriminating awake vs. rapid eye movement vs. non-rapid eye movement sleep. The performance is better for young participants and for those with a low apnea-hypopnea index, while it is robust for commonly used outpatient medications. Conclusions:Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large population. It opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible, such as in critically ill patients. Deep Network ArchitectureWe trained five deep neural networks based on the following input signals and their combinations: 1) ECG; 2) CHEST (chest respiratory effort); 3) ABD (abdominal respiratory effort); 4) ECG+CHEST; and 5) ECG+ABD. Each deep neural network contained a feed-forward convolutional neural network (CNN) which learned features pertaining to each epoch, and a recurrent neural network (RNN), in this case long-short term memory (LSTM), to learn temporal patterns among consecutive epochs.The CNN of the network is similar to that in Hannun et al. 20 . As shown in Figure 1A and Figure 1B, the network for a single type of input signal, i.e. ECG, CHEST or ABD, consists of a convolutional layer, several residual blocks and a final output block. For a network with both ECG and CHEST/ABD as input signals ( Figure 1C), we first fixed the weights of the layers up to the 9 th residual block (gray) for the ECG network and similarly fixed up to the 5 th residual block (gray) for the CHEST/ABD network, concatenated the outputs, and then fed this concatenation into a subnetwork containing five residual blocks and a final output block. The numbers of fixed layers were chosen so that the outputs of layers from different modalities have the same shape (after padding zeros), and were then concatenated.The LSTM of the network has the same structure for different input signals. It is a bi-directional LSTM, where the context cells from the forward and backward directions are concatenated. For the network

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ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit

Cited by 8 publications

References 17 publications

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

Journal of clinical monitoring and computing end of year summary 2018: hemodynamic monitoring and management

Sleep staging from electrocardiography and respiration with deep learning

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