A deep learning-based long short-term memory (LSTM) approach for classifying five classes of arrhythmias demonstrated promising and better performance than other artificial intelligence methods.
Does a deep learning-based LSTM approach improve arrhythmia classification performance compared to other AI methods on the MIT-BIH database?
A deep learning-based LSTM approach provides high-precision automatic ECG recognition for arrhythmia classification, outperforming other AI methods.
Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, which can be life-threatening. Electrocardiogram (ECG) is the principal diagnostic tool used to detect arrhythmias or heart abnormalities. It contains information about the different types of arrhythmias. However, due to the complexity and nonlinearity of ECG signals, such as the presence of noise, the time dependence of ECG signals and the irregularity of the heartbeat, it is troublesome to analyse ECG signals manually. Moreover, the interpretation of ECG signals is subjective and might vary among experts in the field. Therefore, an automatic, high-precision ECG recognition method is important to arrhythmia detection. For such, a method is proposed in this paper for arrhythmia classification, which is based on deep learning-based approach long short-term memory (LSTM), where five classes of arrhythmias as recommended by the Association for Advancement of Medical Instrumentation (AAMI) are analysed. The method has been tested on the MIT-BIH arrhythmia database with a number of useful performance evaluation measures, showing that is a promising and better performance than other artificial intelligence methods used.
Zhou et al. (Tue,) conducted a other in Arrhythmia. Deep learning-based long short-term memory (LSTM) approach vs. Other artificial intelligence methods was evaluated on Arrhythmia classification performance. A deep learning-based long short-term memory (LSTM) approach for classifying five classes of arrhythmias demonstrated promising and better performance than other artificial intelligence methods.