Quantitative comparison of exercise vectorcardiograms and findings at selective coronary arteriography

Ascoop, C. A. P. L.; Distelbrink, C A; Lang, Peter; Bemmel, Jan H. van

doi:10.1016/s0022-0736(74)80003-8

Cited by 20 publications

(7 citation statements)

References 7 publications

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“…The results of different algorithms for separation of normals and patients with coronary artery disease were expressed in terms of the sensitivity (fraction of abnormal findings in the patients) and the specificity (fraction of normal findings in the control group). These two were then combined in a single Index of Merit, which is the sum of sensitivity and specificity minus one (12).…”

Section: Patients Controls and Methodsmentioning

confidence: 99%

Optimal measurements for detection of coronary artery disease by exercise electrocardiography

Simoons

1977

Computers and Biomedical Research

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Exercise electrocardiogramswere analysed by digital computer in order to select criteria for optimal separation of normal subjects and patients with coronary artery disease. The results of a training group of 86 ostensibly healthy men and 52 patients were verified in a test series of 43 patients and 43 controls.All subjects had a normal QRS complex and ST--T segment at rest. ECG measurements included ST amplitudes and slopes at fixed intervals after the end of QRS. time-normalized ST amplitudes, the negative STareas, and Chebyshev waveform vectors. Visual reading of the ECG according to the classical criterion of a 0.1 mV horizontal ST depression yielded a sensitivity of SO and 5 1% and a specificity of 94 and 95% in the training group and the test group. respectively.Best results with a single ST measurement were obtained by the ST60 amplitude in lead X which was adjusted for heart rate according to the gradual ECG changes during exercise in the normal subjects. The sensitivity was 81% and the specificity 93%. In the test group these figures were 70 and 93%. Combination of measurements in linear discriminant functions resulted in some further improvement of these figures.

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Section: Patients Controls and Methodsmentioning

confidence: 99%

Optimal measurements for detection of coronary artery disease by exercise electrocardiography

Simoons

1977

Computers and Biomedical Research

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“…The reverse problem -a transformation from the ECG to the VCG -should not necessarily have one unique solution; in fact there is an infinite number of solutions. Kors has shown that there exists a general inverse matrix which can be obtained when the eight ECG leads X4 to Xll are used [21]. The best transformation, however, is obtained per individual, as was shown by Ascoop for ECGs and VCGs during rest and exercise [21].…”

Section: Xn)•mentioning

confidence: 99%

“…Although the present lead systems might not be optimal for the diagnosis of some heart diseases that are reflected in the abnormal electrophysiological functioning of the cardiac muscle, it has been shown that -at least for individuals -the ECGs of several lead systems can be almost completely transformed to each other [20][21][22][23][24]. This can be understood by the following.…”

Section: Lead Systemsmentioning

confidence: 99%

“…Kors has shown that there exists a general inverse matrix which can be obtained when the eight ECG leads X4 to Xll are used [21]. The best transformation, however, is obtained per individual, as was shown by Ascoop for ECGs and VCGs during rest and exercise [21]. The main problem in all transformations, whether individual or general, is not only that every person has individual image vectors (or "image", shaped by the endpoints of all lead vectors I over the entire body surface [16]) and that there exist large interindividual differences between such images, but the fact that a fixed dipole is not the proper representation for the spatially distributed electrical activity of the cardiac muscle.…”

Section: Xn)•mentioning

confidence: 99%

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Signal Analysis for ECG Interpretation

Bemmel¹,

Zywietz²,

Kors³

1990

Methods Inf Med

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In ECG interpretation usually two main areas are discerned: the signal analysis and the diagnostic classification. This article reviews the major developments in the first area. ECG signal analysis itself is subdivided into the stages data acquisition, data transformation, feature selection, and data reduction. These stages are consecutively reviewed, while in the data transformation stage digital filtering, detection, wave typing, beat selection, and boundary recognition are discussed.

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“…The changes during the last decade are for instance: processing of simultaneous leads [5][6][7]; implementation on a standard PC [8]; implementation in commercial equip-ment\ and extension of the diagnostic modules with interactive tools for optimization and maintenance. From early on, the algorithms and/or modules of MEANS also appeared to be fruitful for the processing of ECGs during exercise [9,10], for serial analysis [11], and for coronary care monitoring [12,13].…”

Section: Introductionmentioning

confidence: 99%

Methodology of the Modular ECG Analysis System MEANS

Bemmel¹,

Kors²,

Herpen³

1990

Methods Inf Med

234

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The methodology, used in the Modular ECG Analysis System (MEANS) is described. MEANS consists of modules for signal analysis and diagnostic classification. The basic structure of the modular interpretation system remained intact over a period of 20 years, while all modules underwent many changes as a function of experience and insight, and the continuously changing information technology. The article describes the advantages of a modular approach to decision-support systems, the most important ones being easier maintenance of the software package and separate optimization and testing of each module. The overall evaluation of MEANS was done in the CSE study. Evaluation results for modules and for the entire system are presented.

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Quantitative comparison of exercise vectorcardiograms and findings at selective coronary arteriography

Cited by 20 publications

References 7 publications

Optimal measurements for detection of coronary artery disease by exercise electrocardiography

Optimal measurements for detection of coronary artery disease by exercise electrocardiography

Signal Analysis for ECG Interpretation

Methodology of the Modular ECG Analysis System MEANS

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