Data compression and feature extraction of ECG signals

Saxena, Sahaj; Sharma, Ambalika; Chaudhary, Surabhi

doi:10.1080/00207729708929409

Cited by 31 publications

(9 citation statements)

References 20 publications

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“…Moreover, we had only two P-wave and 5 T-wave false detections, representing extremely low error detection rates of 0.07%, and 0.17% for the P-and T-waves respectively. The proposed detection algorithm is then compared with well-known QRS detection algorithms including Saxena [18], So [19], and MOBD [11], reported in the literature [10] as the algorithms which satisfy both the real-time and accuracy criterions the best. Two ECG recordings are selected from the MIT-BIH Arrhythmia Database.…”

Section: Detection Resultsmentioning

confidence: 99%

A new fitting approach for online electrocardiogram component waves delineation

Zoghlami

Ayari

Tielert

et al. 2008

2008 Computers in Cardiology

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A real-time capable and accurate template-based fitting approach for the detection and delineation of all the QRScomplexes, P-, and T-waves is presented. The ECG features extraction is accomplished with a very low amount of ECG signal preprocessing. The performance of our proposed algorithm is validated using ECG records from both the MIT-BIH Arrhythmia and QT standard databases. All of the ECG component waves detectors achieved very high detection rates, detecting correctly up to 3015, 3013, and 3010 QRS-complexes, P-and T-waves respectively from 3015 cardiac beats. The delineation results showed that the standard deviations of the errors are within the tolerance limits.

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Section: Detection Resultsmentioning

confidence: 99%

A new fitting approach for online electrocardiogram component waves delineation

Zoghlami

Ayari

Tielert

et al. 2008

2008 Computers in Cardiology

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“…It does not change the information content present in the signal. The Wavelet Transform provides a time-frequency representation of the signal and is well suited to the analysis of non-stationary signals [9] such as ECG. A Wavelet Transformation uses multi resolution technique by which different frequencies are analysed with different resolutions.…”

Section: Wavelet Transformmentioning

confidence: 99%

Feature Extraction of ECG Signal Using HHT Algorithm

Soorma¹,

Singh²,

Tiwari³

2014

IJETT

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Abstract-This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical model to extract such useful parameter. Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and nonstationary data. It is unique and different from the existing methods of data analysis and does not require an a priori functional basis. The effectiveness of the proposed scheme is verified through the simulation.

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“…An approach for effective feature extraction from ECG signal was described by Saxena et al, (1997). Their approach used an efficient composite method developed for data compression; signal retrieval and feature extraction with error-back propagation (EBP) artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network - Part I: Model Development

A.Ponnle¹,

Ogundepo²

2015

IJAIS

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Electrocardiogram (ECG) is a graphic recording of the electrical activity produced by the heart. The accuracy of any electrocardiogram waveform extraction plays a vital role in helping a better diagnosis of any heart related illnesses. We present a computer-aided application model for detection of cardiac arrhythmia in ECG signal, which consists of signal pre-processing and detection of the ECG signal components adapting Pan-Tompkins and Hamilton-Tompkins algorithms; feature extraction from the detected QRS complexes, and classification of the beats extracted from QRS complexes using Back Propagation Neural Network (BPNN). The application model was developed for ECG signal classification under 'Normal' or 'Abnormal' heartbeats to detect cardiac arrhythmia in the ECG signal. The model was trained with standard arrhythmia database of Massachusetts

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Data compression and feature extraction of ECG signals

Cited by 31 publications

References 20 publications

A new fitting approach for online electrocardiogram component waves delineation

A new fitting approach for online electrocardiogram component waves delineation

Feature Extraction of ECG Signal Using HHT Algorithm

Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network - Part I: Model Development

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