Study of a new model of normal ECG wave
01 May 2014-pp 1-4
TL;DR: In the present paper, a new model of ECG signal has been studied and a mathematical equation ofECG wave recorded on the frontal surface of human body has been derived and tested.
Abstract: ECG wave measured on human surface by an ECG monitor or recorder is an essential tool of clinicians for analysis of heart activity. For this analysis, a suitable model of ECG wave is needed. In the present paper, a new model of ECG signal has been studied. In this model, a mathematical equation of ECG wave recorded on the frontal surface of human body has been derived. This equation has been tested by assuming some parameters of human body. The test results are reported in the paper.
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TL;DR: In this article , a nonlinear model-based feature extraction approach for the accurate classification of four types of heartbeats was investigated, where the features are the morphological parameters of ECG signal derived from the nonlinear ECG model using an optimization-based inverse problem solution.
Abstract: This study investigates a nonlinear model-based feature extraction approach for the accurate classification of four types of heartbeats. The features are the morphological parameters of ECG signal derived from the nonlinear ECG model using an optimization-based inverse problem solution. In the model-based methods, high feature extraction time is a crucial issue. In order to reduce the feature extraction time, a new structure was employed in the optimization algorithms. Using the proposed structure has considerably increased the speed of feature extraction. In the following, the effectiveness of two types of optimization methods (genetic algorithm and particle swarm optimization) and the McSharry ECG model has been studied and compared in terms of speed and accuracy of diagnosis. In the classification section, the adaptive neuro-fuzzy inference system and fuzzy c-mean clustering methods, along with the principal component analysis data reduction method, have been utilized. The obtained results reveal that using an adaptive neuro-fuzzy inference system with data obtained from particle swarm optimization will have the shortest process time and the best diagnosis, with a mean accuracy of 99% and a mean sensitivity of 99.11%.
1 citations
07 Oct 2020
TL;DR: Segment specific modelling approach for different wave segments of ECG signal provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.
Abstract: Electrocardiogram (ECG) modeling is useful for abnormality detection and data compression. The common research problem in modeling is retaining pathological information using minimum number of model coefficients. In this paper, a new modeling technique for different wave segments of ECG signal, viz., baseline to P-onset, P wave, P-offset to Q, QRS complex, S to T-onset, T wave and T-offset to next baseline is presented. The processing steps included preprocessing, R-peak detection, beat segmentation and waveform partitioning, followed by modeling of individual partitions. For P, QRS and T wave, Gaussian model was adopted and for other segments, Fourier model was adopted to minimize reconstruction error. For testing of the proposed model, normal sinus rhythm (NSR) and myocardial infarction (MI) data records of PTB Diagnostic ECG database (ptbdb) and atrial premature (APC), premature ventricular contraction (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB) data records of MIT-BIH arrhythmia database (mitdb) under PhysioNet were used. The average SNR, and MSE using proposed method for ptbdb NSR was 86.33, and 4.41×10-6, respectively; for AMI 96.18, and 3.70×10-6 respectively; for IMI 80.86, and 1.36×10-6 respectively; for mitdb NSR 90.94 and 3.50×10-6 respectively; for APC 89.42, and 2.34×10-6 respectively; for PVC 93.28 and 3.06×10-6, respectively; for LBBB 93.77 and 2.74×10-6, respectively; for RBBB 92.83 and 3.52×10-6 respectively. Segment specific modelling approach provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.
Cites methods from "Study of a new model of normal ECG ..."
..., hidden Markov model (HMM) [21], polynomial method [22], auto regressive method (AR) [23], dynamic nonlinear time domain model [24], Fourier model [25], [26], [27], Gaussian model [28], [29], [30], Gaussian combination model (GCM) [31], Gaussian function combined with the Kalman filter [32] etc....
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References
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TL;DR: A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals and may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.
Abstract: A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals. The operator can specify the mean and standard deviation of the heart rate, the morphology of the PQRST cycle, and the power spectrum of the RR tachogram. In particular, both respiratory sinus arrhythmia at the high frequencies (HFs) and Mayer waves at the low frequencies (LFs) together with the LF/HF ratio are incorporated in the model. Much of the beat-to-beat variation in morphology and timing of the human ECG, including QT dispersion and R-peak amplitude modulation are shown to result. This model may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.
1,103 citations
"Study of a new model of normal ECG ..." refers background in this paper
...E Patrick et al[3] have proposed three coupled ordinary differential equations to develope dynamical model of ECGwhich can generate realistic synthetic ECG signals....
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Book•
01 Jan 1968
TL;DR: In this article, the authors describe a set of criteria for the faithful reproduction of an event, based on the impedance of the bioelectric events and the requirements of ventilation and ventilators.
Abstract: Biomedical instruments and the measurement of physiological events resistive transducers inductive transducers capacitive transducers photoelectric transducers piezoelectric devices thermoelectric devices chemical transducers electrodes stimulation and stimulators detection of physiological events by impedance the bioelectric events radiant energy devices ventilation and ventilators criteria for the faithful reproduction of an event.
498 citations
TL;DR: A bidomain theory-based surface heart model AT imaging approach was applied to single-beat data of atrial and ventricular depolarization in two patients with structurally normal hearts, and the reconstructed sinus rhythm sequence was in good qualitative agreement with the pattern previously published for the isolated Langendorff-perfused human heart.
Abstract: Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling ECG mapping and three-dimensional (3-D) + time anatomical data, the electrical excitation sequence can be imaged completely noninvasively in the human heart. In this paper, a bidomain theory-based surface heart model AT imaging approach was applied to single-beat data of atrial and ventricular depolarization in two patients with structurally normal hearts. In both patients, the AT map was reconstructed from sinus and paced rhythm data. Pacing sites were the apex of the right ventricle and the coronary sinus (CS) ostium. For CS pacing, the reconstructed AT pattern on the endocardium of the right atrium was compared with the CARTO map in both patients. The localization errors of the origins of the initial endocardial breakthroughs were determined to be 6 and 12 mm. The sites of early activation and the areas with late activation were estimated with sufficient accuracy. The reconstructed sinus rhythm sequence was in good qualitative agreement with the pattern previously published for the isolated Langendorff-perfused human heart.
123 citations
"Study of a new model of normal ECG ..." refers background in this paper
...B Tilg et al[4] have developed Model-based imaging of cardiac electrical excitation in human body in which a bidomain theory based surface heart model has been developed....
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TL;DR: This paper presents a method for evaluating the properties of features that describe the shape of a QRS complex by examining the distances in the feature space for a class of nearly similar complexes.
Abstract: Automated classification of ECG patterns is facilitated by careful selection of waveform features This paper presents a method for evaluating the properties of features that describe the shape of a QRS complex By examining the distances in the feature space for a class of nearly similar complexes, shape transitions which are poorly described by the feature under investigation can be readily identified To obtain a continuous range of waveforms, which is required by the method, a mathematical model is used to simulate the QRS complexes
114 citations
"Study of a new model of normal ECG ..." refers background in this paper
...Sornmo et al[6] have proposed a mathematical model of ECG wave which can determine the features of QRS complex....
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42 citations