Impact of the high-frequency cutoff of bandpass filtering on ECG quality and clinical interpretation: A comparison between 40Hz and 150Hz cutoff in a surgical preoperative adult outpatient population

Ricciardi, Danilo; Cavallari, Ilaria; Creta, António; Giovanni, Giacomo Di; Calabrese, Vito; Belardino, Natale Di; Mega, Simona; Colaiori, Iginio; Ragni, Laura; Proscia, Claudio; Nenna, Antonio; Sciascio, Germano Di

doi:10.1016/j.jelectrocard.2016.07.002

Cited by 24 publications

(16 citation statements)

References 16 publications

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“…Prior to CS encoding, the ECG signals were filtered by 4-pole Butterworth high-pass and low-pass filters. The filters' cutoff frequencies are 0.5 Hz and 40 Hz, respectively, as specified for ambulatory ECG monitoring [51], [52]. Due to their impact on minimizing the CS encoder complexity, we selected RSBM with d = 12 and RD sensing matrices for CS encoding in our experiments.…”

Section: Resultsmentioning

confidence: 99%

A Fast Compressed Sensing Decoding Technique for Remote ECG Monitoring Systems

Rateb

2020

IEEE Access

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Compressed Sensing (CS) has been proposed as a low-complexity ECG data compression scheme for wearable wireless bio-sensor devices. However, CS decoding is characterized by high computational complexity. As a result, it represents a burden to the computational and energy resources of the network gateway node, where decoding is performed. In this article, we propose a Fast Compressive Electrocardiography (FCE) technique to address this problem. CS decoding in FCE is based on Weighted Regularized Least-Squares (WRLS), rather than the standard approach based on 1 norm minimization. The WRLS formulation takes into account prior knowledge of ECG signal properties to estimate an optimally compact and accurate representation of ECG signals. Numerical results show that decoding by FCE is on average 33 times faster than the fastest tested CS-based ECG decoding technique. In addition, highquality ECG signal reconstruction by FCE is achieved at 32% higher compression ratio. Therefore, FCE can contribute to improving the overall energy and computational resource efficiency of CS-based remote ECG monitoring systems.INDEX TERMS Compressed sensing (CS), electrocardiogram (ECG), random demodulator, remote health monitoring, wireless body-sensor network (WBSN).

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Section: Resultsmentioning

confidence: 99%

A Fast Compressed Sensing Decoding Technique for Remote ECG Monitoring Systems

Rateb

2020

IEEE Access

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“…Band-pass filtering is widely used in ECG signal processing for eliminating high-frequency noise and performs well in R-peak detection. Regarding two commonly used cutoff frequencies (i.e., 40 and 150 Hz) in low-pass filtering of ECGs, 40 Hz could effectively eliminate the high-frequency noises but lead to the elevation of J-point, i.e., the junction between QRS termination and ST-segment onset ( Nakagawa et al, 2014 ; Christov et al, 2017 ), resulting in inaccuracy of the onset of the ST segment, whereas 150 Hz could overcome this problem but cause a high level of residual noise ( Ricciardi et al, 2016 ; Christov et al, 2017 ). As compared with a band-pass filter, the discrete wavelet transform could perform better in terms of eliminating high-frequency noise and keeping the morphology feature points of the ECG signal ( Addison, 2005 ; Singh and Pradhan, 2018 ; Chen et al, 2020 ), as illustrated in Supplementary Figure S1 .…”

Section: Methodsmentioning

confidence: 99%

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

et al. 2022

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Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection.Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (SI, THI, and SHI, where SI is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls.Results: The ECG + VCG model with three features (SI,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814.Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.

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“…The recording used the commercial acquisition system (iWorx, model RA834, iWorx Systems Inc, Dover, NH, USA) and ECG devices (iWire-BIO4, iWorx Systems Inc, Dover, New Hampshire, US). The sampling frequency was 1 kHz and the analog filter for the ECG was 0.05-40 Hz [29].…”

Section: Methodsmentioning

confidence: 99%

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

Bao

Abdala

Kamavuako

2020

Sensors

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The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device.

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Impact of the high-frequency cutoff of bandpass filtering on ECG quality and clinical interpretation: A comparison between 40Hz and 150Hz cutoff in a surgical preoperative adult outpatient population

Cited by 24 publications

References 16 publications

A Fast Compressed Sensing Decoding Technique for Remote ECG Monitoring Systems

A Fast Compressed Sensing Decoding Technique for Remote ECG Monitoring Systems

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

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