CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

Abstract

ECG analysis is commonly used by medical practitioners and cardiologists for monitoring cardiac health. A high-performance automatic ECG classification system is a challenging area because there is difficulty in detecting and clustering various waveforms in the signal, especially in the manual analysis of electrocardiogram (ECG) signals. In this paper, an accurate (ECG) classification and monitoring system are proposed using the implementation of 1D Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM). The learned features are captured from the CNN model, and then fed to the LSTM model. No handcraft features are required for the model for the ECG classification. The result of the CNN-LSTM model has demonstrated superior performance than several state-of-the-arts that cited in the result section. The proposed models are evaluated on MIT-BIH arrhythmia and PTB Diagnostics datasets. Based on the obtained results, the CNN-LSTM method can improve the accuracy rate, such that 98.1 % and 98.66 % on Myocardial Infarction (MI) and arrhythmia classification, respectively.

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

“…As shown in Table 5, the accuracy of our hybrid model is higher than the accuracy achieved from CNN model [14,64], the LSTM model [56], and the CNN and LSTM models used in this work. Also, the accuracy of our hybrid model is higher than that achieved from hybrid CNN-LSTM [52,54,72] and is comparable to that achieved by Oh et al [71].…”

“…Most of the models published in literature don't focus on the number of model parameters although it is an important measurement to check whether the model is suitable for the embedded system or not. Comparing our hybrid model with other similar hybrid models in recent works [52,54,71,72], our model is capable to successfully do classification for six ECG beats classes with comparable accuracy and least number of parameters. Furthermore, our model can do automated feature extraction in faster manner compared with the slow manual feature extraction presented by Banerjee et al [52].…”

“…They have used A Convolutional Neural Network (CNN) structure to extract the morphological ECG features. They measured the extent of HRV by applying a composite structure based on Long Short-Term Memory (LSTM) [53][54][55][56] and a set of hand-crafted statistical features. Their model has a degraded accuracy since they have used binary classification only, and it requires more processing time because they have used manual feature extraction.…”

HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification

“…They also utilized evolutionary neural network to compare with their proposed model. Abdullah and Ani [7] proposed a 1D CNN and long short term memory (LSTM) based framework for ECG classification. They utilized two extensively common databases in their validation.…”

“…Because of numerous contemporary medical applications where such problem may be mentioned, the relevance of ECG classification is currently quite high. However, the usage of heuristic hand-crafted or manufactured features with shallower feature learning frameworks is one of the primary drawbacks of these ML solutions [7, 8].…”