A robust ECG denoising technique using variable frequency complex demodulation

Hossain, Billal; Bashar, Syed Khairul; Lázaro, Jesús; Reljin, Nataša; Noh, Yign; Chon, Ki H.

doi:10.1016/j.cmpb.2020.105856

Cited by 31 publications

(20 citation statements)

References 40 publications

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“…Variable frequency complex demodulation (VFCDM) is a high-resolution time-frequency domain method, which is widely used for various biosignal processing, including ECG [24,37], EDA [36], PPG [38,39] and other signals. First, the heart rate signal was resampled at 4 Hz to make the samples evenly spaced, which was followed by high-pass filtering (0.01 Hz) to remove any trends.…”

Section: Vfcdm-based Featuresmentioning

confidence: 99%

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Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

et al. 2021

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Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time–frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients’ AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

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Section: Vfcdm-based Featuresmentioning

confidence: 99%

“…Using the VFCDM, the preprocessed heart rate signal was decomposed into K number of components or sub-bands [37]:…”

Section: Vfcdm-based Featuresmentioning

confidence: 99%

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

et al. 2021

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“…We simulate two types of noise, namely EMG (generated by creating random noise as in [29]) and PLI (generated by generating a sine wave with an amplitude of 0.15 and a frequency of 50 Hz, as in [30]). The SNRC architecture is tested on 8 records (100 m, 101 m, 103 m, 105 m, 115 m, 200 m, 215 m, and 230 m), following [5][6][7][8][9]. We randomly order them for testing.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…More recently, Md-Billal [9] used variable frequency composite Demodulation (VFCDM) to remove the sub-band decomposition of noise-contaminated ECG signals. Tracey and Miller used a non-local mean (NLM) method for ECG denoising [10].…”

Section: Introductionmentioning

confidence: 99%

ECG Denoising using a Single-Node Dynamic Reservoir Computing Architecture. (Dept. E)

Elbedwehy

Abo-Elsoud

Elnakib

2021

MEJ. Mansoura Engineering Journal

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“…Hankel matrices are also widely used not only in abnormality or illness detection from ECGs but also in denoising of the data using variable frequency complex demodulation (VFCDM) algorithm [3], ECG signal denoising algorithm based on the deep factor analysis [4]. Another methodology is based on the eigenvalue decomposition of the Hankel matrix.…”

Section: Introductionmentioning

confidence: 99%

Novel feature extraction technique based on ranks of Hankel matrices with application for ECG analysis

Landauskas¹,

Orinaitė²

2021

Math. models, eng.

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A novel feature extraction technique is presented in this paper. The term H-rankgram is coined here due to the similarity in concept with other feature extraction methods like spectrogram. The H-rankgram is two-dimensional feature pattern which shows the change in algebraic complexity (measured using ranks of Hankel matrices) of a given signal at a different scale in time (estimated using phase space reconstruction with different time lags). In general feature extraction techniques could be classified into two broad types: time domain and frequency domain. The proposed technique fits into the first one. The proof of concept for the technique to detect changes in the signal was explained and an effect of additive noise was tested. Application of the technique was demonstrated to classify ECG signals for healthy and ill patients. The results shows that Myocardial infarction is detected most accurately although there is high imbalance in classification accuracy between the classes.

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A robust ECG denoising technique using variable frequency complex demodulation

Cited by 31 publications

References 40 publications

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

ECG Denoising using a Single-Node Dynamic Reservoir Computing Architecture. (Dept. E)

Novel feature extraction technique based on ranks of Hankel matrices with application for ECG analysis

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