Real-time computer acquisition, analysis, and display of electrophysiology studies

Greenhut, Saul E.; MacDonald, R.S.; Jenkins, Janice M.; Arzbaecher, Robert

doi:10.1109/cic.1990.144283

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The measurement of 'A', 'V' and 'H' are not straight forward because AH interval is changing a lot in EP procedures. The related work and algorithms to find these waves can be found in [6], [7], [8] and [9].…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Novel algorithm for analysis and classification of atrio-ventricular nodal re-entry tachycardia (AVNRT) using intracardiac electrograms

Shahzad¹,

Khan²,

Zaidi³

et al. 2013

2013 3rd IEEE International Conference on Computer, Control and Communication (IC4)

View full text Add to dashboard Cite

Cardiac Electrophysiology (EP) is an established clinical technique for the examination and handling of cardiac rhythm disorders especially arrhythmias since past couple of years. Among several types of arrhythmias, Atrioventricular Nodal Reentrant Tachycardia (AVNRT) is one of the most common arrhythmia seen in the EP Lab. AVNRT is detected in EP Lab by inducing tachycardia in the patient and then by looking on monitor screen and manually evaluating its key features from recorded intra cardiac data which can indicate AVNRT presence and all this process consumes precious time of the Electrophysiologist. The proposed algorithm uses the intracardiac data and by using signal processing, it extracts the AVNRT related features which are used by the classifier for the detection of AVNRT. This will save the time of manual calculations by Electrophysiologist. More than 20 patient data was used to test the algorithm for feature extraction part which shows precision between 92.8% and 96.5%. Among these 20 patients 4 belong to AVNRT and they are classified by the classifier to AVNRT successfully.

show abstract

Section: Algorithm Descriptionmentioning

confidence: 99%

Novel algorithm for analysis and classification of atrio-ventricular nodal re-entry tachycardia (AVNRT) using intracardiac electrograms

Shahzad¹,

Khan²,

Zaidi³

et al. 2013

2013 3rd IEEE International Conference on Computer, Control and Communication (IC4)

View full text Add to dashboard Cite

show abstract

Improved beat-to-beat timing measurements of His-bundle signal

LeBlanc

Mokrane

1994

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

A simplified bioelectric model of the atrioventricular (AV) junction is proposed to better understand the underlying biophysical generation of intracardiac His-bundle signal and to analyze the effects of electrocatheter displacements on waveform morphology and estimation of AV node conduction time (AVCT). Based on this model, an inverse problem approach has been developed to estimate electrode displacements occurring in real recordings. A measurement correction method is proposed to improve estimation of AVCT. Results illustrate autonomic influences on AVCT, a phenomenon hardly measured with common techniques.

show abstract

Real-time computer acquisition, analysis, and display of electrophysiology studies

Cited by 2 publications

References 7 publications

Novel algorithm for analysis and classification of atrio-ventricular nodal re-entry tachycardia (AVNRT) using intracardiac electrograms

Novel algorithm for analysis and classification of atrio-ventricular nodal re-entry tachycardia (AVNRT) using intracardiac electrograms

Improved beat-to-beat timing measurements of His-bundle signal

Contact Info

Product

Resources

About