# Lightweight Ensemble Network for Detecting Heart Disease Using ECG Signals
## 2 Related Work
### Previous Study  
[PERSON] et al. [2] classified arrhythmia signals using the 1D-HLP technique. Using the 1D-HLP, 512 dimensional features are extracted from each of the five levels of the low-pass filter. These features are subjected to 1 Nearest Neighbor (1-NN) classifier for classification with four distance metrics. The authors obtained a classification accuracy of 95.0% when classifying 17 arrhythmia classes using the MIT-BIH arrhythmia ECG Database.  
[PERSON], H.D.M., et al. [30] proposed an algorithm that could classify the ECG signal of both healthy and sick people. The proposed lightweight solution uses quantized one-dimensional deep convolutional neural networks, and is ideal for real-time continuous monitoring of cardiac rhythm. It is capable of providing one output prediction per second. It is accurate (sensitivity of 98.5% and specificity of 99.8%) and can be implemented on a smartphone, which is energy-efficient and fast, requiring 7.65 ms per prediction  
[PERSON] et al. [31] proposed a new deep-learning approach for the detection of ventricular arrhythmias (VA). Initially, the ECG signals were transformed into images, and this had not been done before. Subsequently, these images were normalized and utilized to train the AlexNet, VGG-16, and Inception-v3 deep-learning models. The results were evaluated on the MIT-BIH Database, and an accuracy of 97.6% was achieved.  
[PERSON] et al. [32] developed a deep-learning-based approach for multi-label classification of ECG, named Multi-ECGNet, which can effectively identify patients with multiple heart diseases simultaneously. The experimental results show that Multi-ECGNet can achieve a high score of 0.863 (micro-F1-score) in classifying 55 types of arrhythmias.  
[PERSON] et al. [33] proposed an ECG signal multiclassification model using deep learning. The authors used a squeeze-and-excitation residual network (SE-ResNet), a residual network (ResNet) with a squeeze-and-excitation block. The authors compared SE-ResNet with ResNet as a baseline model for various depths of the layer (18/34/50/101/152). The SE-ResNet classifier with 152 layers achieved an F1 score of 97.05% for seven-class classifications. The model surpassed the baseline model, ResNet, by +1.40% for seven-class classification.  
[PERSON] et al. [34] proposed a novel method for generating a gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. The authors extracted the morphological features for automatic ECG signal classification. The extracted features were combined with six machine-learning algorithms to classify cardiac arrhythmias. Of the six machine-learning algorithms, combining XGBoost with the  
Figure 1: MIT-BIH database contained several ECG signals. The recordings were digitized at 360 samples per second per channel with a resolution of 11 bits: **(a)** NSR; **(b)** AFIB; **(c)** PVC; **(d)** LBBB. Two or more cardiologists independently annotated each record.  
proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895, and presented better results than wavelet features with XGBoost.  
Existing technology for arrhythmia diagnosis makes it difficult for patients to detect diseases in their daily lives. This is due to structural problems with the arrhythmia diagnosis technology. When deep learning is used to detect arrhythmia it involves a very large amount of computation, because it is developed with a focus on performance. This deep-learning computer uses high-performance GPUs and memory. However, it has spatial limitations because it is not portable. Although arrhythmia diagnosis technology is being developed, it is not practically helpful for patients with arrhythmia. To solve these problems, patients need solutions to be available on mobile devices that are now widely available to many people.