## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### Learning Algorithms on ECG
#### 6.2.1 Learning on Reconstructed ECG Signal
[219] proposed ECG signal Classification based on CNN using the ADADELTA and biased dropout algorithms to improve performance where the ADADELTA optimizer is used to increase the learning rate and convergence speed. The 1D information fusion vector was transformed into a 2D image with the help of one-hot encoding technique to improve the accuracy and speed of classification. This model was experimented on theMIT-BIH arrhythmia database, which was capable of achieving an average accuracy of 99.1% and 97% which displayed a fair performance in terms of the sensitivity and positive predictive rate. A deep-learning strategy has been presented by [PERSON] et al. [220] in which the network classifies the scalogram image obtained by CWT based on the signature associated to arrhythmia. The 2D CNN is trained for automated arrhythmia identification using the recordings of CWT. The suggested method is trained and tested to identify five various types of heartbeats. The proposed method displayed an average sensitivity, specificity, and accuracy of 98.87%, 99.85%, and 99.65%, respectively. The outcome demonstrated that the proposed model can effectively identify arrhythmia for small segments of ECG signal which made the model computationally faster and simpler. [PERSON] et al. [167] proposed an automated heart disease recognition technique based on 1-D CNN 5-layer architecture using ECG signals. Here the signals were directly fed to a well-trained CNN network. This model was validated using the MIT-BIH Arrhythmia Database, which consisted of more than 4000 ECG signal samples extracted from 25 male and 22 female subjects. The experimented output result gave an outstanding result of an average classification accuracy of 98.33%. The confusion matrix from the testing dataset indicated 99% accuracy. The sensitivity and the specificity were 98.33% and 98.35%, respectively. [PERSON] et al. [221] developed an automated system for ECG classification which classifies 4 classes of ECG signal which are normal, coronary artery disease (CAD), myocardial infarction (MI) and congestive heart failure (CHF) classes. The classification is done by using CNN and unique GaborCNN models. Weight balancing was used to balance the dataset, as the ECG data used in this work were imbalanced. Lead II ECG signals from 92 healthy controls, 7 CAD, 148 MI and 15 CHF patients were considered for categorization. The performance of GaborCNN was better due to its less computational complexity and the classification accuracy was more than 98.5% for all the 4-classes, while on the other hand, CNN also gave its best performance. [PERSON] et al. [222] proposed a combined network of CNN and RNN designed for 5 classes of ECG signal classification. This model consists of 2 convolutional layers, ReLU layer, residual blocks, 2 bidirectional long short-term memory (biLSTM) layers and 2 fully connected layers where each residual block involved the structure of a Squeeze-and-Excitation Network (SENet) with lightweight property to recalibrate the feature map of the network. The last dense layer has 5 outputs equivalent to the classes considered. Experimentation on an MIT-BIH dataset the developed network achieved a sensitivity, accuracy and specificity of 95.90%, 95.90% 96.34% for classification of 5 ECG classes and compared the obtained result with other existing models where the proposed method exhibited better performance. [PERSON] et al. [223] proposed an inverted residual block-embedded deep neural network (IRBEDNN) to classify arrhythmia diseases based on processed ECGs. VGG-like architecture was used for extracting the features of different arrhythmia diseases and on the other hand inverted residual block was used to reduce network complexity. The effectiveness of the proposed system was verified by conducting experiments on the MIT-BIH database and achieved the overall classification accuracy of 96.326% and compared the result with other methods. This model has also been tested on the INCARTDB and achieves an overall accuracy of 97.110%. [PERSON] et al.