Recurrent neural network architecture based classification of atrial fibrillation using single lead ECG

US11571162B2 · kind B2 · utility

Assignee

• Tata Consultancy Services Limited · IN

Inventors

• Rohan Banerjee · Sherghati, IN
• Avik Ghose · Sherghati, IN
• Sundeep Khandelwal · Noida, IN

Key dates

• Filing date: Mar 24, 2020
• Grant date: Feb 7, 2023
• Expiry date: Jul 16, 2041

Classification

• Technology area (CPC A): Human Necessities
• CPC primary: A61B5/364
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

Conventionally, Atrial Fibrillation (AF) has been detected using atrial analyses which is vulnerable to background noise. Again there is a dependency on statistical features which are extracted from R-R intervals of long ECG recordings. The present disclosure addresses AF detection from single lead short ECG recordings of less than one minute wherein automatic detection of P-R and P-Q intervals is difficult, which introduces error in feature computing from the segregated intervals and compromises the performance of the classifier. In the present disclosure, a Recurrent Neural Network (RNN) based architecture comprising two Long Short Term Memory (LSTM) networks is provided for temporal analysis of R-R intervals and P wave regions in an ECG signal respectively. Output sates of the two LSTM networks are merged at a dense layer along with a set of hand-crafted statistical features to create a composite feature set for classification of the AF.