Computer classifications of electrocardiograms

Klingeman, Jack D.; Pipberger, Hubert V.

doi:10.1016/0010-4809(67)90003-1

Cited by 40 publications

(8 citation statements)

References 7 publications

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“…The statistical properties of such small samples appear, therefore, not yet truly representative. The problem of sample size and sample stability in multivariate analysis has been investigated in more detail for some entities in electrocardiography (4). It was found that for some disease groups, as many as 300 cases may be necessary, depending to some extent also on the number of variates which are being examined.…”

Section: Discussionmentioning

confidence: 99%

Computer Evaluation of Statistical Properties of Clinical Information in the Differential Diagnosis of Chest Pain

Pipberger¹,

Klingeman²,

Cosma³

1968

Methods Inf Med

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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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Section: Discussionmentioning

confidence: 99%

Computer Evaluation of Statistical Properties of Clinical Information in the Differential Diagnosis of Chest Pain

Pipberger¹,

Klingeman²,

Cosma³

1968

Methods Inf Med

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“…In the field of medical research alone, literally hundreds of efforts have been reported involving the extraction of features from waveforms and the automatic interpretation of these features [2]. Waveform analysis can be done from a research perspective to study the phenomena generating the signals [3], or from a clinical perspective to gather and use diagnostic information on an individual [9].…”

Section: Lntrodu(;tionmentioning

confidence: 99%

Structural pattern recognition of Carotid pulse waves using a general waveform parsing system

1976

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A general waveform parsing system with application to structural pattern recognition of carotid pulse waves is described. The carotid arterial pulse wave is of medical importance because of variation in its structure induced by arterial aging and cardiovascular disease. The syntax-driven waveform analysis system has been applied with good results to tbese pulse waves to detect and measure structural variations. The waveform parsing system is modeled on a compiler-compiler system and allows the user to enter application specific information as data. It is thus general enough to be applicable to otber waveforms.

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“…There are, however, a lot of possibilities of assuming the categories to which the observed samples may belong. In ECG classification, for example, we may be interested in discriminating only between two classes (KLINGEMANN and PIPBERGER [14]), such as a normal and an abnormal one, or we may be interested in a differential diagnosis, that is in discriminating between N categories, one normal and N -1 different abnormal classes (PIPBERGER et a1. [21]; MUCCIARDI and GasE [19]).…”

Section: Jelmentioning

confidence: 99%

On the Optimum Choice of Categories for the Classification of Biomedical Data Patterns

Hess¹,

Brodda²

1979

Methods Inf Med

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The optimum choice of categories for pattern recognition problems is on the one hand determined by the requirement of a low rate of misclassification. On the other hand, the classification of a pattern should result in an information gain as high as possible. A criterion for an optimum choice of categories which is the best compromise between the demands mentioned above is worked out. The Bayes rule is used as a decision function. The alteration of the Bayes risk as indicator for the rate of malrecognition is examined for different choice of categories concerning the very same classification problem. As the calculation of the Bayes risk is commonly difficult, an estimation using the Bhattacharyya coefficient is given. The information content of a choice of categories is defined using Shannon’s information measure. The alteration of the information contents is analyzed by putting together certain categories, i.e. a coarser choice of categories. With the aid of the relative information loss and the relative reduction of the Bayes risk coefficient, a criterion on the goodness of a choice of categories can be given. The criterion also serves as an optimum choice of classes. The extension of the latter criterion to a generalized decision rule is possible.

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Computer classifications of electrocardiograms

Cited by 40 publications

References 7 publications

Computer Evaluation of Statistical Properties of Clinical Information in the Differential Diagnosis of Chest Pain

Computer Evaluation of Statistical Properties of Clinical Information in the Differential Diagnosis of Chest Pain

Structural pattern recognition of Carotid pulse waves using a general waveform parsing system

On the Optimum Choice of Categories for the Classification of Biomedical Data Patterns

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