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.com
Volume 8, Issue 4 (Suppl)
J Health Med Inform, an open access journal
ISSN: 2157-7420
Medical Informatics 2017
August 31- 01 September, 2017
August 31- 01 September, 2017 | Prague, Czech Republic
5
th
International Conference on
Medical Informatics & Telemedicine
J Health Med Informat 2017, 8:4 (Suppl)
DOI: 10.4172/2157-7420-C1-019
PREMATURE VENTRICULAR CONTRACTION (PVC) CAUSED BY DISTURBANCES IN
CALCIUMAND POTASSIUM CONCENTRATIONS: A STUDY USINGARTIFICIALNEURAL
NETWORKS
Julio Cesar Dillinger Conway
a
and
Jadson Claudio Belchior
b
a
Federal University of Minas Gerais, Brazil
b
Sussex University, England
Statement of the Problem:
Abnormalities in the concentrations of metallic ions such as calcium and potassium can, in principle,
lead to cardiac arrhythmias. Unbalance of these ions can alter the electrocardiogram (ECG) signal. Changes in the morphology of
the ECG signal can occur due to changes in potassium concentration, and shortening or extension of this signal can occur due to
calcium excess or deficiency, respectively. The determination of this disorders in a conventional manner may require a long and
thorough analysis of the ECG signal and specific blood tests. Besides, the diagnosis of these disorders can be complicated, making
the modeling of such a system complex.
Methodology &Theoretical Orientation:
An Artificial Neural Network (ANN) was utilized to model the relationships between
disturbances in calcium and potassium concentrations and the morphology of the ECG signal and also for pattern recognition
of an ECG signal of an individual. The procedure can be, in principle, used to identify changes in the morphology of the ECG
signal due to alterations in calcium and potassium concentrations. An arrhythmia database of a widely used experimental data
was considered to simulate different ECG signals and for training and validation of the methodology.
Findings:
The proposed approach can recognize premature ventricular contractions (PVC) arrhythmias, and tests were performed
on ECG data of 47 individuals, showing significant quantitative results, on average, with 94% of confidence. The model was also
able to detect ions changes and showed qualitative indications of what ion is affecting the ECG.
Conclusion & Significance:
These results indicate that the method can be efficiently applied to detect arrhythmias as well as to
identify ions that may contribute to the development of cardiac arrhythmias. Accordingly, the actual approachmight be used as an
alternative tool for complex studies involving modifications in the morphology of the ECG signal associated with ionic changes.