Within a small cohort of refractory LNF patients, Stretta resulted in sustained improvement over 10 years with equivalent outcomes to non-LNF standard Stretta patients. Refractory LNF patients are a subpopulation that may be safely, effectively, and robustly aided by Stretta with fewer complications compared to redo of Nissen or chronic medication use.

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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.

No abstract