Biomedical subjects

J A Kors

Publications and source records attributed to J A Kors.

12 recordsLinked to original sources

PubMed1992

Improvement of automated electrocardiographic diagnosis by combination of computer interpretations of the electrocardiogram and vectorcardiogram.

In the international project "Common Standards for Quantitative Electrocardiography" (CSE), diagnostic results of different computer programs for the interpretation of the electrocardiogram (ECG) and of the vectorcardiogram (VCG) were combined, and it was shown that the "combined program" performs better than each program separately. Because the program MEANS (Modular ECG Analysis System) comprises 2 different classification programs--one for the ECG, the other for the VCG--this allowed investigation of whether the combined interpretations would yield a better diagnostic result than either one separately. This approach requires that a VCG always be recorded in addition to the ECG. To circumvent this complication, the VCG was reconstructed from the simultaneously recorded ECG leads. This reconstructed VCG was then interpreted by the VCG classification program, whereupon the diagnostic interpretations of the ECG and the reconstructed VCG were combined. For the validation, the CSE database of documented ECGs and VCGs (n = 1,220) was used. The combination of the ECG and VCG interpretations yielded a better diagnostic result than each interpretation program separately (total accuracy 74.2% (ECG + VCG) vs 69.8% (ECG) and 70.2% (VCG), p less than 0.001 in both cases). The results for the reconstructed VCG (total accuracy 70.5%) are comparable to those for the ECG and the VCG (p greater than 0.10 in both cases). The performance of the combined interpretations of ECG and reconstructed VCG (total accuracy 73.6%) is approximately the same as that of the combined ECG and VCG (p greater than 0.10). Thus, the performance of an ECG computer program can be improved by incorporating both ECG and VCG classificatory knowledge, using only the ECG itself.

Diagnosis, Computer-Assisted↗

PubMed1992

Variability in ECG computer interpretation. Analysis of individual complexes vs analysis of a representative complex.

Variability in the electrocardiogram (ECG) can be due to extrinsic noise or can be caused by intrinsic factors, such as changes in the volume conductor or in the heart itself. Computer programs for the interpretation of the ECG base their diagnostic classification on one set of measurements that is derived from a representative PQRST complex or that is computed by taking the median from the measurements for each complex in the recording. However, these methods may fail to do justice to the intrinsic variability that may be present in the ECG. An alternative method is proposed: derive a set of measurements from each complex in the recording, classify each individual complex separately, and then combine the individual classifications into one final classification. This procedure has been evaluated on a validated database (n = 1,220) using an ECG computer program. Total accuracy against the clinical evidence increased from 69.8% for the interpretations of the averaged complexes to 71.2% for the combined interpretations of the individual complexes (p < 0.001). The effect of beat-to-beat variation on the measurements and classifications is demonstrated and the influence of extrinsic and intrinsic variability is assessed.

Electrocardiography↗

PubMed1992

Combination of diagnostic classifications from ECG and VCG computer interpretations.

The Common Standards for Quantitative Electrocardiography (CSE) study showed that the weighted combined diagnostic classification of a group of experts or a set of electrocardiographic (ECG) programs is superior to the average expert or program, and sometimes even better than the best expert. For that reason the authors investigated whether the combination of classifications from the authors' programs for ECG and vectorcardiographic (VCG) interpretation would deliver better results than either one separately. The CSE diagnostic database (n = 1,220) was used for testing purposes. Since the combination of computer interpretations from the ECG and VCG requires a separate and preferably simultaneous recording of the VCG, the authors also examined the combined interpretation of the ECG with a simulated VCG reconstructed from the eight independent leads of the 12-lead ECG (the rVCG). Besides that, the authors investigated the combined interpretation from all single beats of the dominant waveform from the same ECG recording (sECG). The performance of all combinations, that is, the ECG + VCG, ECG + rVCG, and sECG proved to be significantly better (74.2%, 73.6%, and 71.2%, respectively) than that of the ECG or VCG separately (69.8% and 70.2%, respectively; p < 0.001 for all cases). However, the difference in performance between the sECG and the VCG was not significant.

Cardiology↗

PubMed1991

DTL: a language to assist cardiologists in improving classification algorithms.

Heuristic classifiers, e.g., for diagnostic classification of the electrocardiogram, can be very complex. The development and refinement of such classifiers is cumbersome and time-consuming. Generally, it requires a computer expert to implement the cardiologist's diagnostic reasoning into computer language. The average cardiologist, however, is not able to verify whether his intentions have been properly realized and perform as he hoped for. But also for the initiated, it often remains obscure how a particular result was reached by a complex classification program. An environment is presented which solves these problems. The environment consists of a language, DTL (Decision Tree Language), that allows cardiologists to express their classification algorithms in a way that is familiar to them, and an interpreter and translator for that language. The considerations in the design of DTL are described and the structure and capabilities of the interpreter and translator are discussed.

Algorithms↗

PubMed1990

Reconstruction of the Frank vectorcardiogram from standard electrocardiographic leads: diagnostic comparison of different methods.

Three methods for reconstructing the Frank VCG from the standard 12-lead ECG were studied. The first was based on multivariate regression, the second on a model of the cardio-electrical activity, and the third method used a quasi-orthogonal set of ECG leads. The methods were evaluated on a test set of 90 cases by a numerical distance measure and by the agreement in diagnostic classification of the original and reconstructed VCGs. The original and reconstructed VCGs were presented separately and in random order to three referees. Eighteen of the original VCGs were presented three times to estimate the intra-observer agreement. Kappa statistics were used to quantify the agreement between diagnostic classifications. Separately, one referee was simultaneously presented the original VCG and its three reconstructions for all cases. Each reconstruction VCG was classified as either diagnostically 'same' as the original, 'borderline' or 'different'. The performance of the regression method and the model-based method was comparable. Both methods were preferable to the quasi-orthogonal method. The kappa values for the preferred methods indicated a good to excellent diagnostic agreement between the original and reconstructed VCGs. Only one out of ninety VCGs that were reconstructed with the regression method was classified as 'different' compared with the original VCGs; three VCGs were classified as 'different' with the model-based method. It was also found that estimation of similarity by a distance measure could not replace diagnostic evaluation by skilled observers.

Electrocardiography↗

PubMed1990

Signal analysis for ECG interpretation.

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.

Algorithms↗

PubMed1990

Classification methods for computerized interpretation of the electrocardiogram.

Two methods for diagnostic classification of the electrocardiogram are described: a heuristic one and a statistical one. In the heuristic approach, the cardiologist provides the knowledge to construct a classifier, usually a decision tree. In the statistical approach, probability densities of diagnostic features are estimated from a learning set of ECGs and multivariate techniques are used to attain diagnostic classification. The relative merits of both approaches with respect to criteria selection, comprehensibility, flexibility, combined diseases, and performance are described. Optimization of heuristic classifiers is discussed. It is concluded that heuristic classifiers are more comprehensible than statistical ones; encounter less difficulties in dealing with combined categories; are flexible in the sense that new categories may readily be added or that existing ones may be refined stepwise. Statistical classifiers, on the other hand, are more easily adapted to another operating environment and require less involvement of cardiologists. Further research is needed to establish differences in performance between both methods. In relation to performance testing the issue is raised whether the ECG should be classified using as much prior information as possible, or whether it should be classified on itself, explicitly discarding information other than age and sex, while only afterwards other information will be used to reach a final diagnosis. Consequences of taking one of both positions are discussed.

Algorithms↗

PubMed1990

Methodology of the modular ECG analysis system MEANS.

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.

Diagnosis, Computer-Assisted↗

PubMed1990

The Delphi method to validate diagnostic knowledge in computerized ECG interpretation.

We investigated the applicability of the Delphi method for increasing the agreement among multiple cardiologists on, firstly, their classifications of a set of electrocardiograms and, secondly, their reasons for these classifications. Five cardiologists were requested to judge the computer classifications of a set of thirty ECGs. If a cardiologist disagreed with the computer classification, he had to provide a new classification and a reason for this change. The results of this first round were compiled and anonymously fed back to the cardiologists. In a second round the cardiologists were asked once again to judge the ECGs and to rate the reasons provided in the first round. The level of agreement was estimated by means of the kappa statistic. The Delphi procedure substantially increased the agreement on the classifications among the cardiologists. The final agreement was very high and comparable with the intraobserver agreement. There was also a high level of agreement on the reasons provided by the cardiologists. However, their use in improving the program's performance is hampered by the qualitative nature of many of the reasons. Suggestions are given for a more formalized elicitation of knowledge.

Delphi Technique↗

PubMed1986

Multilead ECG analysis.

This paper describes the results of our recent research in computer-assisted ECG/VCG interpretation. It comprises new developments which were initiated by the advent of relatively inexpensive microcomputers. Our previous systems performed an off-line analysis of ECGs. Currently, there is a trend to move computer power near to the patient and to provide on-line analysis of ECGs. Besides the advantage of the direct availability of the ECG interpretation, quality control will reduce the number of uninterpretable ECGs and hence the number of repeated recordings. This paper describes the requirements that were established for a system for on-line ECG analysis. The system is based on our modular approach, just like our off-line system, Modular ECG ANalysis System (MEANS). Changes in the methods and software had to be made mainly because of the simultaneity of all ECG leads and the concurrency of the processing tasks. Other modifications and extensions of the algorithms necessary to meet the requirements of on-line ECG interpretation especially those related to processing speed, are discussed, and evaluation results are presented.

Electrocardiography↗

PubMed1986

Algorithms for the detection of events in electrocardiograms.

The detection of events in electrocardiograms (ECGs) and vectorcardiograms (VCGs) is an essential processing step in systems for computer-assisted ECG/VCG interpretation. This paper describes various algorithms for the detection of QRS complexes, atrial activity and artefacts. These algorithms have been used in various versions of the TNO Modular ECG/VCG Interpretation System including its latest version, MEANS. Evaluation results are presented.

Biometry↗

PubMedDate not supplied

Expert systems integrated with information systems.

Amongst the users of the AIDA applications there is a rapidly growing interest in the use of expert systems, not as independent systems, but as logical extensions of their already existing information systems. In this paper a prototype system (IDEA) will be described that consists of a set of utilities for the construction of an expert system within the context of an AIDA application. Although IDEA does not excel in sophisticated knowledge representations nor in search strategies (the development of which was not our primary concern) it is able to demonstrate that the facilities provided by AIDA together with the IDEA facilities result in an expert system which is characterized by a high degree of integration with the already operational information system.

Artificial Intelligence↗