ECG Denoising based on PCA and using R Peaks Detection

Talbi, Mourad

doi:10.5220/0004998201190125

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…AthrIA was tested in both simulated and experimental 10 s ECG signals (sampling frequency: 250 Hz), preprocessed using a technique based on the principal component analysis (only the first eigenvector projection was used) [13,14]. R peaks were detected through the Pan-Tompkins algorithm [1].…”

Section: Datamentioning

confidence: 99%

AThrIA: a New Adaptive Threshold Identification Algorithm for Electrocardiographic P Waves

Sbrollini¹,

Mercanti²,

Agostinelli³

et al. 2017

Computing in Cardiology Conference (CinC)

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Proposed algorithms for P-wave identification and segmentation usually search for it within a window just before the R peak, thus hypothesizing the presence of at most one P wave, as it is in a normal electrocardiographic (ECG) tracings. In presence of abnormal atrial depolarization, however, there might be no P waves (as in atrial fibrillation) or multiple P waves (as in second-or third-degree atrioventricular blocks). Thus, this study proposes a new Adaptive Threshold Identification Algorithm (AThrIA) for ECG P-waves whose most innovative feature is to look for P waves all along the heartbeat, potentially allowing multiple Pwaves identification. AThrIA ability to identify and segment (finding onset, maximum and offset) P waves was tested in simulated and experimental ECG tracings with no P waves, one P wave and two P waves, respectively. All P waves involved in the study were annotated. Results indicate that AThrIA correctly identified all P waves (no false-negative or false-positive detections). Segmentation errors were 0 ms for the simulated ECG tracings, and no more than 10 ms for the experimental tracings. Thus, AThrIA represents a promising tool for P-wave identification and segmentation in both physiological (one P wave) and pathological (none or multiple P waves) conditions.

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Section: Datamentioning

confidence: 99%

AThrIA: a New Adaptive Threshold Identification Algorithm for Electrocardiographic P Waves

Sbrollini¹,

Mercanti²,

Agostinelli³

et al. 2017

Computing in Cardiology Conference (CinC)

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show abstract

“…Recently, a research reported that combination of EEMD and PCA techniques applied to a PPG to monitor heart rate (HR) and RR yielded a good result [23]. Other researchers used PCA as a tool to reduce ECG noise [24].…”

Section: Introductionmentioning

confidence: 99%

Automatic estimation of respiration rate from oronasal pressure using Ensemble empirical mode decomposition, Butterworth filter, and data fusion

Chung

2020

IET Science, Measurement &Amp; Technology

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Respiratory rate is an essential indicator of serious illness. Compared to heart rate or systolic blood pressure, its change is more evident, hence is a better means to discern stable patients from those at risks. In addition, the respiration rate is a crucial indication of sleep quality associated with sleep disorders such as obstructive sleep apnea. Oronasal pressure, as a clinical respiratory signal for sleep analysis, can be used in polysomnography both in labs of hospital and homes. Besides, it is often taken as a reference signal in research as opposed to the estimated respiration rate. This study aims to provide an automated respiratory rate estimation system with signals taken from oronasal pressure transducer that can cope with noises and is adaptive to various respiratory frequencies. A robust approach is presented here that employs Ensemble empirical mode decomposition method to remove signal noise, together with Butterworth band-pass filter to obtain the breathing frequency by means of zero-crossing. Among 97.6% of the test data, the study yields a root mean square error of 1.031. Compared to other methods, the current approach provides a more accurate respiration rate estimation in the application of orinasal pressure to sleep analysis.

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“…To solve this problem, many scholars have proposed various methods. Among all the methods, template‐based techniques [1, 2] and principle component analysis [3–5] denoise method are often used; however, both methods work only when R peak has been detected in processing single lead ECG. Therefore, filter‐based [6, 7] method, empirical mode decomposition [8, 9] and wavelet transform (WT) [10, 11]‐based method are developed.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiograph signal denoising based on sparse decomposition

Zhu

2017

Healthcare Technology Letters

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Noise in ECG signals will affect the result of post-processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse-based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG signals, with a view to myoelectric interference existing in ECG signals. Firstly, a denoising model for ECG signals is constructed. Then the model is solved by matching pursuit algorithm. In order to get better results, four kinds of dictionaries are investigated with the ECG signals from MIT-BIH arrhythmia database, compared with wavelet transform (WT)-based method. Signal–noise ratio (SNR) and mean square error (MSE) between estimated signal and original signal are used as indicators to evaluate the performance. The results show that by using the present method, the SNR is higher while the MSE between estimated signal and original signal is smaller.

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ECG Denoising based on PCA and using R Peaks Detection

Cited by 4 publications

References 11 publications

AThrIA: a New Adaptive Threshold Identification Algorithm for Electrocardiographic P Waves

AThrIA: a New Adaptive Threshold Identification Algorithm for Electrocardiographic P Waves

Automatic estimation of respiration rate from oronasal pressure using Ensemble empirical mode decomposition, Butterworth filter, and data fusion

Electrocardiograph signal denoising based on sparse decomposition

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