Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM

Mehta, S. S.; Lingayat, N. S.

doi:10.1016/j.compbiomed.2007.08.003

Cited by 42 publications

(27 citation statements)

References 22 publications

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“…The accuracy of the proposed technique to detect QRS complexes has been compared with the state-of-the-art techniques [16,17] available in the literature. QRS complexes have been determined with an accuracy of 99.98 % against an accuracy of 99.93 % obtained through the technique proposed in [16] and an accuracy of 99.68 % obtained through the technique proposed in [17].…”

Section: Resultsmentioning

confidence: 99%

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High-resolution detection of sustained ventricular and supraventricular tachycardia through FPGA-based fuzzy processing of ECG signal

Chowdhury

2015

Med Biol Eng Comput

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The paper presents a field-programmable gate array (FPGA)-based fast processing system with 12-channel high-resolution (24 bits) front-end for ECG signal processing. The implemented high-resolution data conversion makes the system suitable for recording of late potentials of the QRS complex in patients prone to sustained ventricular tachycardia. The system accepts ECG signals through 12 channels and then filtered to minimize baseline wander and power-line interference. The filter outputs are connected to 12 delta-sigma ADCs. The whole ADCs work synchronously at 8 kHz sampling frequency, and their output data are transferred to an FPGA that computes online on the digitized sample values in real time and ascertains whether the patient under study suffers from ventricular tachycardia or not. In order to ascertain the QRS complex accurately in the noisy ECG signal, fuzzy entropy of the sample values has been computed and provided as an input to inverse multiquadratic radial basis function neural network. Using the standard CSE ECG database, the algorithm performed highly effectively. The performance of the algorithm in respect of QRS detection with sensitivity of 99.83 % and accuracy of 99.7 % is achieved when tested using single-channel ECG with entropy criteria. The performance of the QRS detection system has been compared and found to be better than most of the QRS detection systems available in the literature. Using the system, 200 patients have been diagnosed with an accuracy of 99 %.

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Section: Resultsmentioning

confidence: 99%

“…Christov et al [4] gave a comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Some recent works highlight the use of combined entropy for detection of QRS complexes [16] or signal entropy for the estimation of errors in detection of QRS complexes [17].…”

Section: Introductionmentioning

confidence: 99%

High-resolution detection of sustained ventricular and supraventricular tachycardia through FPGA-based fuzzy processing of ECG signal

Chowdhury

2015

Med Biol Eng Comput

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“…The extensive review of the various approaches is given in [1][2][3][4]. Recently few other detectors based on Hybrid Complex Wavelet [5], transformative approach [6], PCA-ICA based algorithm [7], continuous wavelet transform [8], multiscale filtering based on mathematical morphology [9], Support Vector Machine [10][11][12], Adaptive quantized threshold [13] etc. have been proposed.…”

Section: Introductionmentioning

confidence: 99%

K-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram

Mehta

Shete

Lingayat

et al. 2010

IRBM

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“…Fanga proposed a novel algorithm based on the phase space trajectory of ECG [4]. Better results can be obtained based on machine learning and morphology theory [5][6][7][8], but it is very difficult to meet real-time application using these complicated algorithms. Therefore, new, more efficient algorithms need to be presented [9,10].…”

Section: Introductionmentioning

confidence: 99%

Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering

Xia

Han

Wang

2015

BME

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Abstract. Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods.

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Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM

Cited by 42 publications

References 22 publications

High-resolution detection of sustained ventricular and supraventricular tachycardia through FPGA-based fuzzy processing of ECG signal

High-resolution detection of sustained ventricular and supraventricular tachycardia through FPGA-based fuzzy processing of ECG signal

K-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram

Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering

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