Modeling of the photoplethysmogram during atrial fibrillation

Sološenko, Andrius; Petrźnas, Andrius; Marozas, Vaidotas; Sörnmo, Leif

doi:10.1016/j.compbiomed.2016.12.016

Cited by 56 publications

(39 citation statements)

References 34 publications

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“…The Twave, composed of two asymmetrical slopes-upward and downward-was modeled using a composite model of two functions to characterize each slope separately. The model, inspired by the one proposed in [27] and [28], and briefly described in [29], consisted of one Gaussian and one lognormal functions (Fig. 3).…”

Section: T-wave Parametrizationmentioning

confidence: 99%

Noninvasive Monitoring of Potassium Fluctuations During the Long Interdialytic Interval

et al. 2020

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Hemodialysis patients are susceptible to life-threatening arrhythmias whose incidence is markedly higher during the long interdialytic interval due to electrolyte fluctuations. Noninvasive monitoring of electrolyte fluctuations, particularly those of potassium, would enable restoring electrolyte balance before the onset of arrhythmias. This study investigates the feasibility of continuous long-term monitoring of potassium fluctuations using a single-lead electrocardiogram. We evaluate patient-specific T-wave morphology changes in the electrocardiogram using two descriptors: (i) a model-based descriptor, , developed to account for overall morphology changes, and (ii) the currently available descriptor, , sensitive to potassium levels in single-lead electrocardiograms. Electrocardiograms of 15 hemodialysis patients with pre-existent cardiac diseases were acquired continuously over the long interdialytic interval along with blood samples at predetermined time instants. Results reveal that and respond concordantly with potassium levels, and reacts to potassium lowering medication. The overlapping index of the daily distributions of and are moderately correlated with changes in potassium levels (= − 0.56 and = − 0.57, respectively). exhibits circadian variation, peaking amidst morning and decreasing until evening. appears to be less affected by motion-induced noise, which is preferable for ambulatory monitoring. Although long-term monitoring of potassium fluctuations is feasible even in complicated hemodialysis patients, the presence of concomitant electrolyte (calcium and bicarbonate) imbalances should be accounted for since it can hamper a reliable estimation. Considering that intradialytic T-wave morphologies may differ from the ones manifested between hemodialysis sessions, future studies should also strive to collect blood samples outside of hemodialysis to improve electrolyte estimation methods.

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Section: T-wave Parametrizationmentioning

confidence: 99%

Noninvasive Monitoring of Potassium Fluctuations During the Long Interdialytic Interval

et al. 2020

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“…Within each heart beat cycle, the blood vessel presents pulsatile changes in accordance with the systolic and diastolic functions of the heart, which are termed as the pulse signals [1]. ere are plenty of physiological information in pulse signals, by which some physiological parameters, such as pulse rate, blood oxygen saturation, and microcirculation, can be calculated directly or indirectly, and which can also be applied to related detection models for the evaluation of the cardiovascular, respiratory, and circulatory system statuses [2][3][4]. However, the pulse signals are relatively weak and can be inevitably interfered by various factors in the process of collection, especially the noises caused by the poor sensor contact and the unstable switch power, which may affect the accuracy of the subsequent related detection models [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion

Liu

et al. 2019

Computational and Mathematical Methods in Medicine

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Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholds are designed, respectively, to discriminate the abnormal signals caused by spike noise and poor-sensor-contact noise. 81 hours of pulse signals (with a sleep apnea annotated every 30 seconds and 9,720 segments in total) from the MIT-BIH Polysomnographic Database are used in the study, including 35 minutes of poor-sensor-contact noises and 25 minutes of spike noises. The proposed method was used to preprocess the pulse signals, in which 9,684 segments out of a total of 9,720 were correctly discriminated, and the accuracy of the method reached 99.63%. To quantitatively evaluate the noise removal effect, a simulation experiment is conducted to compare the Jaccard Similarity Coefficient (JSC) calculated before and after the noise removal, respectively, and the results show that the preprocessed signal obtains higher JSC, closer to the reference signal, which indicates that the proposed method can effectively improve the signal quality. In order to evaluate the method, three back-propagation (BP) sleep apnea detection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases.

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“…This paper presents a practical application of a phenomenological model for simulating PPG signals during paroxysmal AF. The model, first introduced in [5], uses the RR interval series as a basis for generating a PPG signal. In the present study, we exemplify the practical value of the proposed simulation model by studying AF detection performance on PPG signals, generated from RR intervals taken from public ECG databases.…”

Section: Introductionmentioning

confidence: 99%

Photoplethysmogram Modeling During Paroxysmal Atrial Fibrillation: Detector Evaluation

Sološenko

Petrėnas

Marozas

et al. 2017

Computing in Cardiology Conference (CinC)

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A phenomenological model for simulating photoplethysmogram (PPG) during paroxysmal atrial fibrillation (AF) is proposed. A PPG pulse is modeled by combining a lognormal and two Gaussian waveforms. Continuous PPG signals are produced by placing and connecting individual pulses according to the RR interval pattern extracted from annotated ECG signals. This paper presents a practical application of the proposed model for studying the performance of an RR-based AF detector. Physionet databases containing AF episodes serve as a basis for modeling PPG signals. Detection performance was tested for different signal-to-noise ratios (SNRs), ranging from 0 to 30 dB. The results show that an SNR of at least 15 dB is required to ensure adequate performance. Considering the lack of annotated, public PPG databases with arrhythmias, the modeling of realistic PPGs based on annotated ECG signals should facilitate the development and testing of PPGbased detectors.

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Modeling of the photoplethysmogram during atrial fibrillation

Cited by 56 publications

References 34 publications

Noninvasive Monitoring of Potassium Fluctuations During the Long Interdialytic Interval

Noninvasive Monitoring of Potassium Fluctuations During the Long Interdialytic Interval

A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion

Photoplethysmogram Modeling During Paroxysmal Atrial Fibrillation: Detector Evaluation

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