Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4079
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dc.contributor.authorVenkatesh, N. Prasanna-
dc.contributor.authorKumar, R. Pradeep-
dc.contributor.authorNeelapu, Bala Chakravarthy-
dc.contributor.authorKunal Pal-
dc.contributor.authorSivaraman, J.-
dc.date.accessioned2023-10-19T10:34:52Z-
dc.date.available2023-10-19T10:34:52Z-
dc.date.issued2023-01-
dc.identifier.citationIEEE Consumer Communications & Networking Conference, Las Vegas, Nevada, USA, 8–11 January 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4079-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractSinus Tachycardia (ST) reveals pathological dysfunctions and differentiates distinct arrhythmias. The progression of Atrial Fibrillation (AF) from paroxysmal to persistent is frequently associated with tachycardias. Moreover, the most effective AF detection methods suggest incorporating both atrial and R-R interval features. Therefore, the study aims to use the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model to investigate the influence of atrial characteristics on predicting tachycardia. Electrocardiograms (ECGs) from 10 healthy volunteers 26 ± 3.4 years (4 females) were recorded for Sinus Rhythm (SR) and ST conditions along with 10 AF data. For ST, the 5-day follow-up recording was performed with each volunteer. ECG recordings were performed for a duration of 10 s. Atrial features, along with R-R interval and Heart Rate (HR), were utilized as inputs for the developed LSTM-RNN multivariate time series forecasting model. The features were statistically analyzed before training the LSTM-RNN model. The correlation is positive and significant between HR and atrial amplitude (pen_US
dc.subjectSinus Tachycardia (ST)en_US
dc.subjectLSTM-RNN modelen_US
dc.titleID163_Atrial features-based prediction of sinus tachycardia using LSTM-RNN modelen_US
dc.typePresentationen_US
Appears in Collections:Conference Papers

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