Congestive heart failure detection based on attention mechanism-enabled bi-directional long short-term memory model in the internet of medical things

Abstract

As congestive heart failure (CHF) has become an important medical problem facing the world, the detection of the CHF is very important. Internet of medical things (IoMT) has been increasing popularity as its application and function in industry 4.0 era. IoMT systems provide an opportunity for innovations and cross-discipline applications in the medical fields, that can significantly improve the efficiency. With the help of IoMT systems, patients can have in-home and on-body sensors that monitor their vitals easily and constantly, which can provide massive data for detecting disease. However, the most important thing is to find a good model to the automatic detection for massive medical data collected by IoMT systems. In this case, the paper proposes attention mechanism-enabled bi-directional long short-term memory (ABLSTM) model based on Electrocardiogram (ECG) signals, to automatically detect the CHF in IoMT systems. This method makes full use of the model features that can record long-term and short-term signal information, as well as the functions of attention mechanism with adaptive learning in local features, and effectively extracts complex features of ECG signals and performs detection. ECG signal data is from two public datasets, to train and test the proposed ABLSTM model. At the same time, for the cases with noise and data differences, we propose a preprocessing process for ECG signals, and discuss the impact of different data segmentation methods on the model performance. The experimental results show that the proposed ABLSTM model has the highest accuracy rate with 96.6% for the CHF detection, which is higher than other four baseline methods. Therefore, this proposed method can achieve a good result in the detection of the CHF.