Recurrent Neural Network Circuit for Automated Detection of Atrial Fibrillation from Raw ECG

Abstract

A recurrent neural network (RNN) is presented in this work for automatic detection of atrial fibrillation from raw ECG signals without any hand-crafted feature extraction. We designed a stacked long-short term memory (LSTM) network -a special RNN with capability of learning long-term temporal dependencies in the ECG signal. The RNN is digitally synthesized in 65nm CMOS process, and consumes 21.8nJ/inference at 1kHz operating frequency, while achieving state-of-the-art classification accuracy of 85.7% and f1-score of 0.82. The energy consumption of the proposed RNN is 8× lower than state-of-the-art integrated circuits for arrhythmia detection.

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique

“…To accomplish the aforementioned tasks, neural networks such as Recurrent Neural Networks (RNN) [ 20 , 21 , 22 ], Long Short-Term Memory (LSTM) [ 23 , 24 ], Convolutional Neural Networks (CNN) [ 25 , 26 , 27 ], as well as hybrid models [ 28 , 29 , 30 , 31 ] etc., are being integrated to overcome the hindrances of conventional machine-learning strategies that were subject to manual and inaccurate selection of features that may incite inconvenient impacts for the current applications. The drawbacks of the hybrid approaches accumulate the increasing cost and lack of quality datasets which, however, can be considered negligible in some viable cases because the precise classification of heartbeats along with the accurate detection of arrhythmia requires a substantial amount of data to work with [ 32 ].…”