A multiresolution time-dependent entropy method for QRS complex detection

Farashi, Sajjad

doi:10.1016/j.bspc.2015.09.008

Cited by 51 publications

(21 citation statements)

References 31 publications

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“…Many previous studies have designed linear or non-linear filters to remove additive noise and interferences, such as moving averaging filters [1], low-pass filters [2], bandpass filters [2][3][4][5], non-linear filters [6], quadratic filters [7], and Savitzky-Golay (SG) smoothing filters [8]. The methods for noise removal based on the wavelet transform have also appeared in the previous works by removing part of the approximation and detail wavelet coefficients to filter out high frequency noise and baseline drift effects, respectively [9][10][11][12][13][14]. Other studies have proposed noise removal methods based on mathematical morphology [15] and artificial neural networks [16].…”

Section: Introductionmentioning

confidence: 99%

“…Phukpattaranont [7] developed a QRS detection algorithm based on the quadratic filter which can enhance the QRS-to-noise ratio and then allow us to use a single fixed threshold to detect the QRS. Farashi [13] proposed a method based on the analysis of multiresolution time-dependent entropy. It calculates the entropy of the ECG signal at different temporal resolutions and can improve the accuracy for the detection of various QRS morphologies.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Wavelet-Based Algorithm for Detection of QRS Complex

et al. 2019

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Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Wavelet-Based Algorithm for Detection of QRS Complex

et al. 2019

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“…A computationally efficient R or QRS detection algorithm is indispensable for the low-power operation of a wearable ECG device. The QRS detection algorithms developed to date can be categorized as derivative [13], digital filters [14][15][16], Wavelet transform [17,18], Hilbert transform [19,20], phasor transform [21], adaptive threshold [22,23], morphology [24,25], and signal energy [14,[26][27][28] based algorithms. The mentioned methods show superb QRS detection performance; however, major issues under the mobile environment include the low-complexity algorithms and real-time processing methods related to power efficiency and processing speed.…”

Section: Introductionmentioning

confidence: 99%

Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

Bae

Kwon

2019

Applied Sciences

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Recently, with the active development of wearable electrocardiogram (ECG) devices such as smart-bands or portable ECG devices, efficient ECG signal processing technology that can be applied in real-time has been actively studied. However, a wearable ECG device is exposed to various noise situations, thereby reducing the reliability of the detected R point or QRS interval. In addition, as early warning techniques in healthcare systems have been studied, real-time ECG signal processing techniques have become very important in wearable ECG devices. In this paper, we propose an efficient real-time R and QRS detection method using two kinds of first-order derivative filters and a max filter to analyze ECG signals measured from wearable ECG devices in real-time. The proposed method detects the R point and QRS interval in units of a sliding window for real-time processing and combines the detected R points in each sliding window. Also, the reliability of the detected R points and RR intervals is examined through noise region analysis using the histogram characteristic of a sample point. The performance of the proposed method was verified by the MIT-BIH database (DB), CYBHi DB and real ECG data measured from the developed wearable ECG patch. The proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB. In addition, the proposed method enables accurate R point detection and heart rate variability (HRV) analysis even with noisy ECG signals.

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“…They associate two different transforms (wavelet vs cosinus) with statistical techniques of reduction such as the Principal Component Analysis followed by clustering analysis involving K-nearest neighbor, decision tree and artificial neural networks. Since that time, a large spectrum of methods have been tested such as sequential Bayesian methods [7], multiscale energy and eigenspace approach [8], multiresolution time-dependent entropy method [9], signal decomposition model-based Bayesian approach [10] and continuous wavelet transform [11]. However, several main differences may be emphasized between the ECG and cardiomyocyte impedance analysis.…”

Section: Introductionmentioning

confidence: 99%

A novel statistical signal processing method to estimate effects of compounds on contractility of cardiomyocytes using impedance assays

Batista

Bastogne

Delaunois

et al. 2018

Biomedical Signal Processing and Control

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Label free methods such as cell impedance assays are in vitro tests increasingly used in drug development and producing large and high-content data files. Since the current commercial software are not suited for fully automated analysis, there is a need to develop validated and rapid solutions to extract relevant information for biologists. This need is particularly obvious in the case of impedance signals analysis from cardiomyocytes. The proposed solution is based on three main steps. The first one consists in calculating five indices informing about the time variations of frequency (F), amplitude (A), shape (S) of beatings, trends (T) of the cardiomyocyte dependent on spreading, viability and attachment as well as irregularity (I) of the contractility. In a second phase, two summary statistics are proposed to test the concentration effect of drugs on the five FASTI indices. Results of the statistical tests are finally aggregated in a cardio-effect grade to compare the tested molecules in a cardio-impact scale graduated from 0 (no influence) to 10 (highly disturbed effects in cardiomyocytes). This innovative approach was tested using in vitro data obtained from cell impedance analysis of three known molecules (2 cardiotoxic and 1 noncardiotoxic compounds). Results have clearly shown the ability of the proposed approach to identify significant effects on the contractility of cardiomyocytes. This solution speeds up the analysis of cardiomyocyte impedance data, takes into account all the kinetic data generated and is now available for biologists on a web-platform: i-Cardio TM developed by CYBERnano TM .

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A multiresolution time-dependent entropy method for QRS complex detection

Cited by 51 publications

References 31 publications

A Novel Wavelet-Based Algorithm for Detection of QRS Complex

A Novel Wavelet-Based Algorithm for Detection of QRS Complex

Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

A novel statistical signal processing method to estimate effects of compounds on contractility of cardiomyocytes using impedance assays

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