A united CNN-LSTM deep learning model detected arrhythmia from HRV sequences with an average accuracy of 99.06%, sensitivity of 98.29%, and specificity of 99.73%.
Does a united CNN-LSTM deep learning model accurately detect arrhythmia from HRV sequences?
A united CNN-LSTM deep learning model demonstrates high accuracy (>99%) in detecting arrhythmias from heart rate variability sequences.
Arrhythmia (ARR) is the defective origin and conduction of heart activity leading to an abnormal frequency and rhythm of heartbeats. ARR can cause chest tightness, weakness, sinoatrial node blockages, tachycardia and even sudden death. ARR, 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 ARRs. However, the diagnosis of ARR 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 ARR based on RR intervals. In this article, 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 ARR 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 ARR and has potential value in clinical applications.
Zhang et al. (Mon,) conducted a other in Arrhythmia. United CNN-LSTM Algorithm was evaluated on Classification of normal sinus intervals and ARR intervals. A united CNN-LSTM deep learning model detected arrhythmia from HRV sequences with an average accuracy of 99.06%, sensitivity of 98.29%, and specificity of 99.73%.