A United CNN-LSTM Algorithm Combining RR Wave Signals to Detect Arrhythmia in the 5G-Enabled Medical Internet of Things

Arrhythmia is the defective origin and conduction of heart activity leading to an abnormal frequency and rhythm of heartbeats. Arrhythmia can cause chest tightness, weakness, sinoatrial node blockages, tachycardia and even sudden death. Arrhythmia therefore seriously affects the safety of human life. An electrocardiogram (ECG) can record the changes in electrical activity produced in each heartbeat cycle. Due to its simplicity and noninvasiveness, ECGs are used clinically to diagnose arrhythmias. However, the diagnosis of arrhythmia by experts is an inefficient diagnostic method. Heart rate variability (HRV) analysis is a common method for analyzing heart-related diseases, especially for the automatic diagnosis of arrhythmia based on RR intervals. In this paper, we extracted the linear and nonlinear characteristics collected from the 5G-enabled medical Internet of Things to construct a time-frequency spectrogram from HRV sequences and used a deep learning model based on the combination of a deep convolutional neural network (CNN) and a long short-term memory (LSTM) network in order to classify normal sinus intervals and arrhythmia intervals. The average accuracy, sensitivity and specificity of this model were 99.06%, 98.29% and 99.73%, respectively, using a tenfold cross validation strategy. The united CNN-LSTM model can accurately detect arrhythmia and has potential value in clinical applications.