Heart model based ECG signal processing

2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

Enăchescu

2012

Vascular system recognition and spatial reconstruction using MR images consist an important element of modern health care. The developed reconstruction method successfully handles the intensity inhomogeneity or intensity non uniformity (INU), that is an undesired phenomenon during measurement and represents the main obstacle for MR image segmentation and registration methods. The segmentation is realized by a fuzzy c-means (FCM) algorithm together with the INU estimation. The proposed method determines the contours and using a medical knowledge base analysis determines the edges that are parts of the vascular system. Finally a spatial reconstruction is performed from the obtained data. Several MR databases were analyzed, and approximately a 98.5% recognition performance was obtained. The developed method can serve as excellent support for 3-D registration and visualization techniques.

Section: Introductionmentioning

confidence: 99%

Vascular system reconstruction from MR images using active appearance model

2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

Enăchescu

2012

“…In the case of WPW syndrome, the electrocardiogram (ECG) tracing is a mixture of the electrical activities [5] caused by the accessory AV connection and normal AV conduction system. The fast impulse conduction produces an initial deflection in the QRS complex (delta wave) [6].…”

Section: Introductionmentioning

confidence: 99%

Sensibility analysis of the Arruda localization method

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

Benyó

2006

This paper presents an analysis of the Arruda accessory pathway localization method (for patients suffering from Wolff-Parkinson-White syndrome) with suggestions to increase the overall performance. The Arruda method was tested on a total of 121 patients, and a 90% localization performance was reached. This was considered almost as performing result as the highest published (90%) by L. Boersma in 2002. After a deeper analysis of each decision point of Arruda localization method we considered that the lead AVF is not as relevant as other used leads (I, II, III, V1). The overall performance (90%) was slightly lower then the correct decision rate (91.67%) at the weakest decision element (AVF+) of the method. The vectorial space constructed from the most used leads (II, V1, AVF) is not orthogonal which can be a reason for weaker rate in case of AVF.

“…The performance was determined as the ratio of correct and total decisions. Table 1 reveals that the 3D heart simulation [17,18] succeeds in most cases, such as WPW (Wolf Parkinson White) syndrome, pre-excitations, and tissue activation modeling. The performance in case of re-entry mechanisms and triggered events is slightly decreased due to the hazard nature of the events.…”

Section: Resultsmentioning

confidence: 99%

“…The construction of a heart model [20] may allow computers to recognize the origin and the evolvement process of the ECG signal [5]. These systems may unify the vast empiric information applied in traditional systems with modelbased-recognition, creating hybrid processing structures [17]. These hybrid systems may activate the model-based-approach at any moment to handle correctly almost all unrecognizable waveform.…”

Section: Introductionmentioning

confidence: 99%

“…In the area of data processing, numerous interesting biomedical applications of artificial neural networks are included [7]. The best known neural solutions involve multilayer perceptrons [20], Kohonen self-organizing networks [17], fuzzy or neuro-fuzzy systems [16], genetic algorithms [19] and the combination of various solutions within a hybrid system [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unified Neural Network Based Pathologic Event Reconstruction Using Spatial Heart Model

Lecture Notes in Computer Science

Frigy

et al.

This paper presents a new way to solve the inverse problem of electrocardiography in terms of heart model parameters. The developed event estimation and recognition method uses a unified neural network (UNN)-based optimization system to determine the most relevant heart model parameters. A UNN-based preliminary ECG analyzer system has been created to reduce the searching space of the optimization algorithm. The optimal model parameters were determined by a relation between objective function minimization and robustness of the solution. The final evaluation results, validated by physicians, were about 96% correct. Starting from the fact that input ECGs contained various malfunction cases, such as Wolff-Parkinson-White (WPW) syndrome, atrial and ventricular fibrillation, these results suggest this approach provides a robust inverse solution, circumventing most of the difficulties of the ECG inverse problem.