Automated Identification of Myocardial Infarction Using a Single Vectorcardiographic Feature
04 Nov 2017-Vol. 749, pp 641-651
TL;DR: A novel VCG feature - the volume ratio of the 3-d QRS and the ST-T loop, which combines both the loop morphologies into a single feature is proposed, which provides a MI detection sensitivity of 98.8%.
Abstract: Myocardial infarction (MI), more commonly known as heart attack, is a predominant cause of mortality all over the world. Automated MI identification techniques aid in early detection, thus ensuring timely medication and prevention. The vector-cardiogram (VCG) proves to be a more informative and low dimensional alternative for the 12 lead Electrocardiogram (ECG). The automated VCG analysis tools, reported till date, utilize a large number of features based on the sizes, area and orientation of the QRS and the T loops. Such features are not only difficult to extract but also suffers from the curse of dimensionality. This paper proposes a novel VCG feature - the volume ratio of the 3-d QRS and the ST-T loop, which combines both the loop morphologies into a single feature. Statistical analysis of this feature extracted from the PTB diagnostic ECG database reveals that it is significantly different for the healthy and infarction data and provides a MI detection sensitivity of 98.8%. This study is indicative of the strong utility of this new feature for automated MI classification algorithms.
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TL;DR: This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank’s orthogonal lead system, used in clinical practice.
Abstract: This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient’s body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank’s leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank’s orthogonal lead system, used in clinical practice.
27 citations
TL;DR: This review highlights the analysis of VCG records within the extraction of functional parameters for the detection of heart disease and focuses on methods of processing VCG functionalities and their use in given pathologies.
Abstract: Vectorcardiography (VCG) is another useful method that provides us with useful spatial information about the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common nowadays, mainly due to the well-established 12-lead ECG system. However, VCG leads can be derived from standard 12-lead ECG systems using mathematical transformations. These derived or directly measured VCG records have proven to be a useful tool for diagnosing various heart diseases such as myocardial infarction, ventricular hypertrophy, myocardial scars, long QT syndrome, etc., where standard ECG does not achieve reliable accuracy within automated detection. With the development of computer technology in recent years, vectorcardiography is beginning to come to the forefront again. In this review we highlight the analysis of VCG records within the extraction of functional parameters for the detection of heart disease. We focus on methods of processing VCG functionalities and their use in given pathologies. Improving or combining current or developing new advanced signal processing methods can contribute to better and earlier detection of heart disease. We also focus on the most commonly used methods to derive a VCG from 12-lead ECG.
2 citations
References
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TL;DR: Information on MI rates can provide useful information regarding the burden of CAD within and across populations, especially if standardized data are collected in a manner that …
Abstract: ACCF
: American College of Cardiology Foundation
ACS
: acute coronary syndrome
AHA
: American Heart Association
CAD
: coronary artery disease
CABG
: coronary artery bypass grafting
CKMB
: creatine kinase MB isoform
cTn
: cardiac troponin
CT
: computed tomography
CV
: coefficient of variation
ECG
: electrocardiogram
ESC
: European Society of Cardiology
FDG
: fluorodeoxyglucose
h
: hour(s)
HF
: heart failure
LBBB
: left bundle branch block
LV
: left ventricle
LVH
: left ventricular hypertrophy
MI
: myocardial infarction
mIBG
: meta-iodo-benzylguanidine
min
: minute(s)
MONICA
: Multinational MONItoring of trends and determinants in CArdiovascular disease)
MPS
: myocardial perfusion scintigraphy
MRI
: magnetic resonance imaging
mV
: millivolt(s)
ng/L
: nanogram(s) per litre
Non-Q MI
: non-Q wave myocardial infarction
NSTEMI
: non-ST-elevation myocardial infarction
PCI
: percutaneous coronary intervention
PET
: positron emission tomography
pg/mL
: pictogram(s) per millilitre
Q wave MI
: Q wave myocardial infarction
RBBB
: right bundle branch block
sec
: second(s)
SPECT
: single photon emission computed tomography
STEMI
: ST elevation myocardial infarction
ST–T
: ST-segment –T wave
URL
: upper reference limit
WHF
: World Heart Federation
WHO
: World Health Organization
Myocardial infarction (MI) can be recognised by clinical features, including electrocardiographic (ECG) findings, elevated values of biochemical markers (biomarkers) of myocardial necrosis, and by imaging, or may be defined by pathology. It is a major cause of death and disability worldwide. MI may be the first manifestation of coronary artery disease (CAD) or it may occur, repeatedly, in patients with established disease. Information on MI rates can provide useful information regarding the burden of CAD within and across populations, especially if standardized data are collected in a manner that …
6,659 citations
TL;DR: A deterministic algorithm for computing the convex hull of n points inEd in optimalO(n logn+n⌞d/2⌟) time and a by-product of this result is an algorithm for Computing the Voronoi diagram ofn points ind-space in optimal O(nLogn+ n⌜d/ 2⌝) time.
Abstract: We present a deterministic algorithm for computing the convex hull ofn points inEd in optimalO(n logn+n?d/2?) time. Optimal solutions were previously known only in even dimension and in dimension 3. A by-product of our result is an algorithm for computing the Voronoi diagram ofn points ind-space in optimalO(n logn+n?d/2?) time.
387 citations
TL;DR: The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences and heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH that are relevant for classification of normal and abnormal cardiac patterns.
Abstract: In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.
270 citations
TL;DR: The results show that the proposed MEES approach can successfully detect the MI pathologies and help localize different types of MIs.
Abstract: In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.
235 citations
TL;DR: The linear classification models using multiscale RQA features were shown to detect MI with an average sensitivity of 96.5% and an average specificity of 75% in the randomized classification experiments of PhysioNet Physikalisch-Technische Bundesanstalt database, which is comparable to the performance of human experts.
Abstract: Myocardial infarction (MI), also known as a heart attack, is a leading cause of mortality in the world. Spatial vectorcardiogram (VCG) signals are recorded on the body surface to monitor the underlying cardiac electrical activities in three orthogonal directions of the body, namely, frontal, transverse, and sagittal planes. The 3-D VCG vector loops provide a new way to study the cardiac dynamical behaviors, as opposed to the conventional time-delay reconstructed phase space from a single ECG trace. However, few, if any, previous approaches studied the relationships between cardiac disorders and recurrence patterns in VCG signals. This paper presents the recurrence quantification analysis (RQA) of VCG signals in multiple wavelet scales for the identification of cardiac disorders. The linear classification models using multiscale RQA features were shown to detect MI with an average sensitivity of 96.5% and an average specificity of 75% in the randomized classification experiments of PhysioNet Physikalisch-Technische Bundesanstalt database, which is comparable to the performance of human experts. This study is strongly indicative of potential automated MI classification algorithms for diagnostic and therapeutic purposes.
158 citations