Visualization of cardiac health using vector cardiogram

Chee, Johnny; Er, Kaan; Tan, W.; Chua, Caslon

doi:10.1016/j.rbmret.2007.10.002

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…26 A novel visualization technique was presented to display each ECG beat using features like PR interval, QRS width, ST interval, extracted from magnitude and phase plots of different lead combinations. 51 11 Hierarchical representation with a simple colour coding technique was adopted for easy detection of drift of cardiac functioning from normalcy. A visual display platform for the study of voluminous ECG data collected over several hours was developed.…”

Section: Discussionmentioning

confidence: 99%

Cardiac Health Visualization and Diagnosis Using Entropies

Faust¹,

Yu²

2013

J Med Imaging Hlth Inform

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An Electrocardiogram (ECG) indicates the electrical activity of the heart and it can be used investigate the cardiac health. However, it is very difficult to observe the subtle changes in the ECG signal which indicate a particular type of cardiac abnormality. To avoid this difficulty, we have extracted the Heart Rate (HR) from the ECG signal and used it as the base for our analysis. In general, HR signals are highly nonlinear and nonstationary. Hence, we have used nonlinear signal processing to extract Bi-Spectral and approximate entropies from the HR signals. These entropies were used to construct a novel cardiac health visualization system. To show the feasibility of the proposed system, we have analyzed 320 subjects, from eight different cardiac classes. The features, which were extracted from the HR signals, were displayed on a polar coordinate system as different points, and the plots were obtained by joining these points. The orientation of these plots can be used to identify different types of abnormalities. Furthermore, we propose a cardiac index using different entropies. This one value (index) can be used to identify different cardiac classes.

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Section: Discussionmentioning

confidence: 99%

Cardiac Health Visualization and Diagnosis Using Entropies

Faust¹,

Yu²

2013

J Med Imaging Hlth Inform

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“…In the present study, all of the records in the database were pre-processed using a single stage of median filtering to reduce the low-frequency baseline wander and drift. 44,45…”

Section: T-af (Label For Immediately-terminating Af)mentioning

confidence: 99%

Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space

Parvaneh

Golpayegani

Firoozabadi

et al. 2011

Proc Inst Mech Eng H

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Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets. The results showed that 86.67% of dataset A and 80% of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination.

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Frontal Plane Vectorcardiograms: Theory and Graphics Visualization of Cardiac Health Status

2009

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The electrocardiogram (ECG) is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, these signals are highly subjective, and the symptoms may appear at random in the time scale. It is very taxing and time-consuming to decipher cardiac abnormalities based on these ECG signals. The Vectorcardiogram (VCG) is the vector loop in the 2-D frontal plane, indicating the magnitude and direction of the instantaneous heart electrical activity vector (HAV), which represents the sum of the dipole vectors located along the instantaneous depolarization wavefront. The HAV is constructed from the monitored 3-lead ECG signals, placed at the three vertices of the modified Einthoven triangle formed by the 3-lead system in the frontal plane of the torso. The VCG examines the electrical activities within the heart, using the ECG signals along the three sides of the modified Einthoven triangle, and displays electrical events in the 2-dimensional frontal plane. This study demonstrates the development of the heart-depolarisation vector-locus cardiogram (using modified Einthoven's triangle), as a diagnostic measure of the left ventricular depolarisation strength. Our work involves the reconstruction of the "equivalent heart vector" for the QRS complex from limb lead voltages of a sample ECG, and plotting the progression of the cardiac vector during the QRS complex. We have demonstrated the construction of the frontal plane heart-depolarization vector cardiogram (HDVC), as the path of the locus of the tip of the heart electrical activity vector, with initial and terminal points at the origin. In this work, we have shown characteristic patterns of HDVC for cardiac states namely, normal, bundle branch block, ventricular hypertrophy and myocardial infarction. We have demonstrated how HDVC can be diagnostically employed to characterize cardiac disorders, such as ventricular hypertrophy bundle branch block and inferior myocardial infarction.

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Visualization of cardiac health using vector cardiogram

Cited by 4 publications

References 21 publications

Cardiac Health Visualization and Diagnosis Using Entropies

Cardiac Health Visualization and Diagnosis Using Entropies

Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space

Frontal Plane Vectorcardiograms: Theory and Graphics Visualization of Cardiac Health Status

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