Methodology of ECG Interpretation in the Louvain Program

Brohet, Christian; Derwael, C.; Robert, Annie; Fesler, Robert

doi:10.1055/s-0038-1634801

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…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

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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.

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Section: Discussionmentioning

confidence: 99%

Computational Electrocardiography: Revisiting Holter ECG Monitoring

Deserno

2016

Methods Inf Med

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“…ECG feature extraction [ 4 , 17 , 18 ] is required to remove noise signal before feature extract processing due to vulnerability from noise in the environment. Then, a Pareto-optimal solution is required to achieve the best data compression versus compromising high quality ( Fig 1 ).…”

Section: Methodsmentioning

confidence: 99%

ECG data compression using a neural network model based on multi-objective optimization

et al. 2017

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Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms of the real time aspect, short-time Fourier transformation is applied to the processing of signal wave for reducing computational time. For the lossless compression requirement, wavelet-transformation that is a coding algorithm can be used to avoid loss of data. In practice, compression is required to avoid storing redundant recording data that are not useful in the diagnosis platform. The obtained data can be preprocessed to remove noise by using wavelet transform, and then a multi-objective optimize neural network model is used to extract feature information. Compared with the existing traditional methods such as direct data processing method and transform method, our proposed compression model has self-learning ability to achieve high data compression ratio at 1:19 without losing important ECG information and compromising quality. Upon testing, we demonstrated that the proposed ECG data compression method based on multi-objective optimization neural network is effective and efficient in clinical practice.

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“…The 12-lead ECG was directly processed on-line by the MARQUETTE computer program [2] whereas the VCG was processed off-line by the LOUVAIN computer program [3].…”

Section: Methodsmentioning

confidence: 99%

Value of computer-assisted analysis of the electrocardiogram, the vectorcardiogram, and the combined electro-vectorcardiogram in the diagnosis of 'complex' abnormalities

Derwael

Dee

et al.

Proceedings Computers in Cardiology

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We assessed the usefulness of computerassisted interpretation of the Electrocardiogram (ECG), the Vectorcardiogram (VCG) and the combined ECG-VCG in 323 complex cases with associated abnormalities. In 136 cases with major ventricular conduction defects (QRS > 122 ms), the ability to diagnose a left ventricular hypertrophy (LVH) was less than 50% but the performance for myocardial infarction (MI) was higher than 70% There was no significant difference between ECG and VCG, nor was significant the sLight advantage of combining the two methods. In 41 cases with associated LVH and MI, the full diagnosis was quite dgficult Cfrom 10% to 22%) because of the high rate of missed LVH statements whereas the recognition of the MI component was more o f e n correct (> 85%). In 146 cases with MIX MI, i.e., combined anterior and inferior localizations, the VCG and the combined ECG-VCG were significantly more accurate than the ECG alone : 66 and 67% vs 46%. Thus, in this category, computer-assisted interpretation of the VCG brings a clinically useful gain in diagnostic performance.

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Methodology of ECG Interpretation in the Louvain Program

Cited by 13 publications

References 11 publications

Computational Electrocardiography: Revisiting Holter ECG Monitoring

Computational Electrocardiography: Revisiting Holter ECG Monitoring

ECG data compression using a neural network model based on multi-objective optimization

Value of computer-assisted analysis of the electrocardiogram, the vectorcardiogram, and the combined electro-vectorcardiogram in the diagnosis of 'complex' abnormalities

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