Interpretation of Short ECGs with a Personal Computer: The Porto Program

Abreu-Lima, Cassiano; de, J.P. Marques

doi:10.1055/s-0038-1634796

Cited by 4 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At that time, the goal was to define common stand ards for quantitative electrocardiography (QECG) based on digital recordings and to validate ECG-based diagnostic decision support [66]. In total, 14 different 12-lead ECG analysis programs from Belgium [67], Canada [68], France [69], Germany [70], Italy [71], Japan [72], The Netherlands [73], Portugal [74], United Kingdom [75], and United States [76] including research systems as well as commercial products from major industrial companies have been compared based on the same dataset. However, the entire campaign was based on patient recordings of 8 or 10 seconds [77], thus following the ancient paradigm.…”

Section: Discussionmentioning

confidence: 99%

Computational Electrocardiography: Revisiting Holter ECG Monitoring

Deserno

2016

Methods Inf Med

View full text Add to dashboard Cite

SummaryBackground: Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic method has not been changed. Objectives: After 70 years of technologic developments, we revisit Holter ECG from recording to understanding. Methods: A fundamental change is foreseen towards "computational ECG" (CECG), where continuous monitoring is producing big data volumes that are impossible to be inspected conventionally but require efficient computational methods. We draw parallels between CECG and computational biology, in particular with respect to computed tomography, computed radiology, and computed photography. From that, we identify technology and methodology needed for CECG. Results: Real-time transfer of raw data into meaningful parameters that are tracked over time will allow prediction of serious events, such as sudden cardiac death. Evolved from Holter's technology, portable smartphones with Bluetooth-connected textile-embedded sensors will capture noisy raw data (recording), process meaningful parameters over time (analysis), and transfer them to cloud services for sharing (handling), predicting serious events, and alarming (understanding). To make this happen, the following fields need more research: i) signal processing, ii) cycle decomposition; iii) cycle normalization, iv) cycle modeling, v) clinical parameter computation, vi) physiological modeling, and vii) event prediction. Conclusions: We shall start immediately developing methodology for CECG analysis and understanding.

show abstract

Section: Discussionmentioning

confidence: 99%

Computational Electrocardiography: Revisiting Holter ECG Monitoring

Deserno

2016

Methods Inf Med

View full text Add to dashboard Cite

show abstract

“…The results obtained with the described ANN approach are clearly very good compared with results obtained by other means [5].…”

Section: Discussionsupporting

confidence: 70%

“…Porto System (in routine use at the main Oporto Hospital since 1983) [5,6]. This data set consists of 1075 Frank lead ECGs distributed among four classes: Normal (N), Left Ventricular Hypertrophy (LVH), Right Ventricular Hypertrophy (RVH) and Myocardial Infarction (MI).…”

Section: Introductionmentioning

confidence: 99%

Comparison of artificial neural network based ECG classifiers using different features types

Gonçalves

Ferreira

et al.

Computers in Cardiology 1994

Self Cite

View full text Add to dashboard Cite

Artificial neural networks (ANN) have been applied for some years in the field of signal classification with the aim of outperforming the traditional classifiers.This paper addresses the results of a study that comprehended the design and training of ANNs for ECG classification in four classes. Distinct ANNs having as inputs distinct ECG features types were designed and trained with the aim of attaining a reduced and "best" discriminating features set.

show abstract

Feature extraction from ECG for classification by artificial neural networks

Pretorius

Nel

[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems

View full text Add to dashboard Cite

The abilig of properly trained artificial neural networks to correctly classifi, patterns makes them particularly suitable for the interpretation of ECG signals. For the purposes of this research, attention was given to three classes of ECG's, namely: normal and two cardiac myopathies; anterior and inferior infarctions. Suitable features were extractedji-om the digitized bipolar limb lead ECG signals and results are presented to show that a multilayer perceptron can correctly discriminate between the three chosen classes.

show abstract

Interpretation of Short ECGs with a Personal Computer: The Porto Program

Cited by 4 publications

References 4 publications

Computational Electrocardiography: Revisiting Holter ECG Monitoring

Computational Electrocardiography: Revisiting Holter ECG Monitoring

Comparison of artificial neural network based ECG classifiers using different features types

Feature extraction from ECG for classification by artificial neural networks

Contact Info

Product

Resources

About