Orthogonal electrocardiograms obtained from 87 patients with necropsy evidence of biventricular hypertrophy (BVH) were compared to record samples obtained from normal subjects (N) and patients with left or right ventricular hypertrophy (LVH or RVH). From 333 different ECG measurements an attempt was made to select optimal discriminators of the various pathological entities. The best separation between the BVH sample and other entities was obtained by utilizing linear discriminant function analysis and a likelihood ratio test. By using a combined covariance matrix of BVH versus N, LVH, and RVH, 69% of the BVH sample was correctly classified, thus demonstrating that multivariate analysis can lead to a substantial improvement in diagnostic classification over previous studies reported in the literature.
To test the multivariate classification method against completely independent record samples, new series of LVH and RVH cases were compared to the BVH sample. The results were similar to those obtained against the original samples. When a "clinical" BVH sample was classified by the combined covariance matrix, 44% of the new cases were classified correctly.
A search was also made for optimal scalar and vectorial measurements that can be used in routine ECG interpretation without access to computer facilities. On using 96 percentile ranges, the separations were less efficient than those obtained by multivariate analysis but they still compared very favorably with previous reports.
Summary:A new automated ECG system using advances in microprocessor technology and computerized electrocardiography is described. This microcomputer-based system is self-contained and mobile. It acquires both the 12-lead and orthogonal lead (Frank) electrocardiograms and analyzes the latter within minutes. Software includes the program developed in the Veterans Administration which uses advanced statistical classification techniques and a large well-documented patient data base. Diagnostic probabilities are computed using a Bayesian approach. Diagnostic performance has been tested using independent clinical criteria and found to be quite accurate. This system enables the clinician to immediately review the computer's identifications, measurements, and diagnostic classifications and quickly * The ECG system was conceived by Jack Klingeman, who since 1965, has specialized in diagnostic techniques for noninvasive evaluation of cardiovascular disease. In cooperation with the other authors he implemented the system and began demonstrating its usefulness to the medical community. He completed the initial manuscript describing the system shortly before his death on July 27, 1982. use these results in clinical decision making. Serial comparisons are readily made since all previous recordings are stored on floppy diskettes. The use of microprocessors in this system makes it economically feasible for practicing physicians.
A pilot study was undertaken to determine quantitatively from a large number of signs, symptoms and laboratory tests of patients with, the differential diagnosis of chest pain, which information items could serve as optimal descriptors and/or discriminators of disease. Data were obtained from 1238 patients. For each subject 429 questions of the »yes-no« type were answered and 69 numerical data collected. Incidence rates of signs and symptoms were considered as descriptors. Out of those exceeding an incidence rate of 25 percent, between 60 and 95 percent referred to medical history, depending upon the disease entity under study.Contingency table analysis and chi-square tests were used first to determine the discriminative power of various items. Historical data predominated again. Out of the total of 498 information items tested only 46 reached a chi-square level of 40 which was considered the minimum for efficient separation of diseases. Many items with high incidence rates contributed little or nothing to disease differentiation.To test the discriminative power of the identified signs, symptoms and laboratory data, discriminant function analysis was used. The number of items could be further reduced to less than 10. More than 95 percent of the 1000 patients with Coronary Artery Disease and Pneumonia could be classified correctly with this reduced set.Data reduction and identification of optimal descriptors and discriminators can be considered as one of the most important preliminary steps in computer analysis of clinical information.
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