A computer program for the analysis of serial electrocardiograms from patients who suffered a myocardial infarction

Haelst, A.C.T.A. Van; Donker, D.K.; Visser, Frans C.; Cock, Carel C. de; Hasman, Arie; Talmon, Jan L.

doi:10.1016/0020-7101(85)90027-3

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…The high agreements between the expert cardiologist reader and the serial comparison algorithm are very encouraging. These results also confirm the usefulness in AMI management as concluded in previous publications [8][9]. % IMI 190 167 88 190 159 84 AMI 130 117 89 147 108 74 LMI 14 12 86 14 10 71 ALI 56 44 79 59 45 76 PMI 75 71 95 83 69 83 Table 3 summarizes the agreement for the specific evolution statements within the MI categories where the pECGs have been edited.…”

Section: Resultssupporting

confidence: 76%

Computerized serial comparison of electrocardiograms - program performance in acute myocardial infarction

Ariet

Zhou²,

Deluca³

et al. 2005

Computers in Cardiology, 2005

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We have developed a computer program, which is now available in the Philips IntroductionComputerized analysis of electrocardiogram (ECG) including a serial ECG comparison algorithm has been a successful and widely accepted automated clinical application in the last few decades [1][2][3][4][5]. Commercial systems are available that provide valuable assistance in ECG management and automated serial ECG comparison analysis [6][7]. The increasing storage capacity and connectivity of modern electrocardiograph and computer systems make it possible to manage a large ECG database for online access. When a previous ECG is available, the cardiologist ECG reader not only determine the abnormal conditions of the current ECG, but also examines the serial changes in rhythm and morphology in reference to the previous ECG. The availability of a previous ECG for serial comparison is one of the most important clinical pieces of information necessary to improve ECG diagnosis accuracy. The previous ECG becomes more important in ECG diagnosis if significant differences have arisen since the previous ECG. Significant changes in rhythm or morphology in reference to the previous ECG are of clinical concern. Expert cardiologists strongly believe that ECG diagnosis is incomplete without a comparison to previous ECGs. This serial ECG comparison algorithm was originally developed in 1990 [1], and the algorithm has been since then routinely used at the Heart Station of University of Florida and the Heart Station of Duke University Medical Center. Recently, the program was further refined and enhanced, and is now integrated into the newly designed Philips TracemasterVue ECG Management System. The purpose of this paper is to report the newly revised serial comparison algorithm design, particularly in areas of acute myocardial infarct (AMI) and the performance results from recently conducted validation tests. Serial Comparison Algorithm DesignA serial comparison algorithm is most helpful in AMI diagnosis and in tracking the evolution of an AMI. ECG presenting rapid changes. A single ECG often does not provide adequate information for diagnosis unless it is compared to a series of previous ECGs. In addition to comparing ECG statements and detecting significant waveform changes, our serial comparison algorithm is designed to track and report the infarct evolution to better serve the cardiologist ECG readers. The serial comparison work flow, definition for AMI evolution status, and criteria for serial changes in AMI are described in detail below. Serial Comparison Work FlowThe algorithm compares two ECGs at a time based on combined waveform measurements and the diagnostic

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Section: Resultssupporting

confidence: 76%

Computerized serial comparison of electrocardiograms - program performance in acute myocardial infarction

Ariet

Zhou²,

Deluca³

et al. 2005

Computers in Cardiology, 2005

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“…I was involved in the comparison of serial ECGs of patients, who suffered a myocardial infarction [11]. The two most recent ECGs were compared, and a trend analysis based on all ECG recordings of the patient was performed.…”

Section: Ecg Analysismentioning

confidence: 99%

My Journey Through the Field of Medical Informatics

Hasman

2022

Studies in Health Technology and Informatics

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In this contribution some achievements and milestones in the field of medical informatics, especially concerning decision support, as perceived by the author, are presented. The author focuses on those topics with respect to decision support that during his career in medical informatics impressed him and triggered him to convince his PhD students to start research on related topics. Both some of these achievements and the related research of some of his PhD students will be presented. The contribution starts with signal classification. Both ECG classification and sleep EEG classification are discussed. Then the use of Bayes’ theorem for diagnostic purposes is discussed and some early applications pass review, among which the AAPHelp system developed by de Dombal and colleagues. Attention is subsequently paid to the advent of expert systems and other knowledge-based systems such as MYCIN and INTERNIST and to guideline-based decision support systems. Finally, the author presents his ideas about challenges for the field.

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“…Different types of decision schemes can be followed during classification of ECGs of which we mention the statistical and the logical models. In practice, most programs use mixtures of them and the subdivision is only meant to discern the different strategies that we have implemented in MEANS (see [29][30][31][32][33][34][35][36][37] for some of the studies in which we were involved in developing diagnostic classification methods for ECGs). Statistical models are most often used for differential • diagnosis in gen~ era!…”

Section: Contour Classificationmentioning

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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A computer program for the analysis of serial electrocardiograms from patients who suffered a myocardial infarction

Cited by 7 publications

References 3 publications

Computerized serial comparison of electrocardiograms - program performance in acute myocardial infarction

Computerized serial comparison of electrocardiograms - program performance in acute myocardial infarction

My Journey Through the Field of Medical Informatics

Methodology of the Modular ECG Analysis System MEANS

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