## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### Learning Algorithms on ECG
#### 6.2.1 Learning on Reconstructed ECG Signal
[224] developed an efficient and fast deep learning method for classification of cardiac arrhythmias in up to 17 classes. This method used beat-wise ECG signal analysis using iris spectrogram where a single ECG beat was analyzed and calculated the iris spectrogram. Later, this spectrogram was given to 2D CNN for classification. This model was implemented using 744 ECG signals from 45 different persons and was able to obtain an overall classification accuracy of 99.13. This approach was found to be faster and more efficient for classification and can be implemented for real-time arrhythmia detection. The study proposed by [PERSON] et al. [225] developed an automatic classification method for identifying myocardial infarction using 3-D ECG image and a Grad-CAM ++ method based on a VGG network. Here the ECG data were segmented into heartbeats based on the R-peak and normalized. Then, the heartbeat data was converted into a top view of a colored 3-D ECG and divided into three parts: ST, whole heartbeat, and QRS images for a multi-VGG19 BN for training and classification. Later, the classification outputs were summed to obtain the final results. Grad-Cam ++ method was used to provide visually interpretable heatmaps. The experimented output demonstrated that the proposed model effectively classified 3-D ECG images with high accuracy and obtained accuracy, sensitivity and specificity of 95.65%, 97.34% and 90.80% for the PTB database. Another such paper based on CNN by [PERSON] et al. [226] for heartbeat classification was proposed. In order to overcome the imbalance between classes a batch-weighted loss function was used to quantify the loss. Experiments were carried out on a single-lead raw ECG signal as it is without any data preprocessing and feature extraction and multiple heartbeats were also considered for more effective classification. This method outperformed existing methods for intra-patient and inter-patient paradigm and achieved an accuracy, positive productivity, sensitivity and specificity of 99.48%, 98.83%, 96.97% and 99.87% for intra-patient and 88.34%, 48.25%, 90.90% and 88.51% for inter-patient respectively. This paper by [PERSON] et al. [227] developed a robust algorithm to classify the ECG signal effectively in the presence of environmental noise. 1D CNN with two convolutional layers, two down-sampling layers, and a fully connected layer was used in the proposed model. Classification accuracy was improved by transforming the 1D data into 2D images. Later the 2D CNN model consisting of three 2D-convolutional layers, three down-sampling layers, and a fully connected layer was also used. The effectiveness of the proposed method was tested on MIT-BIH arrhythmia database and achieved the classification accuracy of 97.38% and 99.02% for 1D and 2D model respectively. The obtained performance indicates that the 2D CNN model is more effective than 1D for classification. The paper proposed by [PERSON] et al. [228] discusses the study on dictionary selection for ECG signals in CS domain. This article proposes the construction of dictionaries which are directly constructed from R waves. Authors examined three different types of projection matrices in various types of dictionaries. The K-nearest neighbors (KNN) classifiers are used to classify the reconstructed signals and in addition to this an MLP was also used to classify the recovered signals. Experiments were carried out using MIT-BIH database for cardiac patterns compressed sensing (CPCS) and Patient specific classical compressed sensing methods (PSCCS). Authors concluded that the dictionaries-based method used for reconstruction has more impact for reconstructing the original signal. Another approach presented by [PERSON] et al. [229] discusses an effective method for Real-Time ECG signal reconstruction based on CS. In this work CS and Generative adversarial networks (GAN) are concatenated with each other for recovery of signals over long time period. Sparse binary matrix was used as sensing matrix.