Detection of the P and T waves in an ECG

Gritzali, F.; Frangakis, G.; Παπακωνσταντίνου, Γ.

doi:10.1016/0010-4809(89)90017-7

Cited by 91 publications

(42 citation statements)

References 6 publications

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“…The R location can be obtained by means of various methods, e.g., based on the derivative [5], sliding mean filer, weighted and squared operators of the first derivative [6], detection algorithm of PanTompkins [7]. For PQRST complex detection, there are discrete wavelet transformation based algorithms [8,9], and duration transformation [10].…”

Section: Ecg Entities Recognition C Qrs Complex Detectionmentioning

confidence: 99%

Expert System for Structural Analysis of Electrocardiograms

Krasovitskaya¹,

Cherkashin²,

Gorunchik³

2016

Proccedings of the ICAIIT2016

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Abstract. The paper deals with process of a cardiological expert system development. A definition of a electrocardiogram is presented. Problems of ECG characteristics determination such as ECG data digitizing is considered. A problem of QRS complex recognition and P and T waves parameters measurement is discussed. A general outline of analysis technique for ECG using wavelet transformation are proposed.

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Section: Ecg Entities Recognition C Qrs Complex Detectionmentioning

confidence: 99%

Expert System for Structural Analysis of Electrocardiograms

Krasovitskaya¹,

Cherkashin²,

Gorunchik³

2016

Proccedings of the ICAIIT2016

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“…Computer interpretation of the 12-lead ECG is a popular technique. Currently under development are arrhythmia monitors for ambulatory patients which analyze the ECG in real time [1][2][3][4][5][6][7][8][9][10]. When an arrhythmia appears, such a monitor can be programmed to immediately store an interval of the abnormal ECG for subsequent transmission to a central station where a physician can interpret it.…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive thresholds and T-wave discrimination techniques provide part of the decision rule algorithm. The slope of the R wave is a popular signal feature used to locate the QRS complex in many QRS detectors [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Computer Aided Identification System for ECG Characteristic Points

Tun¹

2015

IJBSE

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Abstract:Digital signal processing and data analysis are very often used methods in a biomedical engineering research. In this work, the descriptions of two detection algorithms for ECG characteristic points are enclosed. The detection algorithms presented in this work are based on Pan and Tompkins' algorithm and wavelet transform for signal de-noising and detection of QRS complexes. In the first approach, efficient designed filters are focused on removing supply network 50 Hz frequency and baseline drift due to breathing. A special digital bandpass filter reduces false detection caused by the various types of interference present in ECG signals. The next process after filtering is differentiation followed by squaring, and then integration. The integrated signal is detected by thresholding for QRS complex. P wave and T wave detection are performed by using detected QRS complexes. MATLAB program is developed for the characteristic points' detection. The algorithm for peak detection case is modified and it is applied to show ECG characteristic points. Wavelet Transform (WT) method is used for peak detection in this work. Wavelet based detection algorithms for one-dimensional signals are presented along with the results of detection ECG data. Firstly, ECG signals are decomposed by the Discrete Wavelet Transform (DWT). The decomposed signals are detected by thresholding for QRS complex. Detection of the QRS complex is the most important task in automatic ECG signal analysis. Finally, P wave and T wave detection are performed by using detected QRS complexes. Different types of algorithms are applied and evaluated their performance with sensitivity (Se), positive predictive (+P).

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“…Many methods mostly non-syntactic have been used for QRS detection [1][2][3][4][5]. These methods mainly suffer from three problems: (1) QRS waveform varies from patient to patient and depends on the state of the patient; (2) noise and QRS complexes pass bands overlap; (3) without post-filtering, we can introduce significant modifications in the shape of the signal, generating false detection result.…”

Section: Introductionmentioning

confidence: 99%

Detection of QRS Complexes Using Wavelet Transforms and Golden Section Search

Chen¹,

Mo²,

Guo³

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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The extraction and identification of ECG (electrocardiogram) signal characteristic parameters are the basis of ECG analysis and diagnosis. The fast and precise detection of QRS complexes is very important in ECG signal analysis, for it is the precondition of the correlative parameters calculation and diagnosis. In this paper, the modulus maxima of wavelet transform was applied to the QRS complexes detection of ECG signal firstly. Once misdetections or fail-detections happen, we used the Golden Section Search to adjust the thresholds of maxima determination. The correct detection rate of the QRS complexes is up to 99.6% based on MIT-BIH ECG data.

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Detection of the P and T waves in an ECG

Cited by 91 publications

References 6 publications

Expert System for Structural Analysis of Electrocardiograms

Expert System for Structural Analysis of Electrocardiograms

Analysis of Computer Aided Identification System for ECG Characteristic Points

Detection of QRS Complexes Using Wavelet Transforms and Golden Section Search

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