Some problem in Computer assisted ECG Analysis with Bonner's Program

1977 ◽  
Vol 4 (2) ◽  
pp. 63-63
Keyword(s):  
Computer Assisted ◽  
Ecg Analysis

Methodology of ECG Interpretation in the Louvain Program

1990 ◽  
Vol 29 (04) ◽  
pp. 403-409 ◽  
Author(s):  
C. R. Brohet ◽  
C. Derwael ◽  
A. Robert ◽  
R. Fesler
Keyword(s):  
Quantitative Analysis ◽  
High Resolution ◽  
Clinical Utility ◽  
Visual Analysis ◽  
Diagnostic Classification ◽  
Computer Assisted ◽  
Reference Standard ◽  
Independent Evidence ◽  
Ecg Analysis ◽  
Ecg Interpretation

AbstractThe Louvain program performs the analysis and interpretation of the vectorcardiogram (VCG) to increase the clinical utility of ECG analysis. Among its original features, there are (1) a high-resolution vector-loop display for visual analysis, (2) quantitative analysis of the spatial VCG using age-sex stratified limits, (3) separate software for adult and pediatric series and (4) complementary deterministic and statistical methods of diagnostic classification. Using objective, ECG-independent evidence as a reference standard, the Louvain program has shown satisfactory levels of diagnostic accuracy in most basic categories. However, its usefulness is especially marked in “borderline” or “complex” situations, where the 12-lead ECG cannot provide a clear answer. It corresponds to the concept of “computer-assisted ECG interpretation” as opposed to “computer ECG analysis”.

scholarly journals Usefulness of computer-assisted ECG analysis in the pre-operative evaluation of noncardiac surgery

2020 ◽  
Vol 37 (11) ◽  
pp. 1075-1077
Author(s):  
Ancor Sanz-García ◽  
Alberto Cecconi ◽  
Enrique Alday ◽  
Maurizio Cecconi ◽  
Miriam Perez-Romero ◽  
...  
Keyword(s):  
Noncardiac Surgery ◽  
Computer Assisted ◽  
Ecg Analysis ◽  
Operative Evaluation

scholarly journals Application of Pre-Trained Deep Learning Models for Clinical ECGs

2021 ◽  
Author(s):  
Theresa Bender ◽  
Tim Seidler ◽  
Philipp Bengel ◽  
Ulrich Sax ◽  
Dagmar Krefting
Keyword(s):  
Deep Learning ◽  
Clinical Relevance ◽  
Added Value ◽  
Computer Assisted ◽  
Rounding Errors ◽  
Ecg Analysis ◽  
Runtime Environment ◽  
Assisted Diagnosis ◽  
Electrocardiogram Ecg ◽  
Complex Cases

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

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