## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### Learning Algorithms on ECG
#### 6.2.1 Learning on Reconstructed ECG Signal  
The IoT framework considered for the review has an important decision-making block implemented using AI. [PERSON] et al. [213] quantified the effect of CS for monitoring ECG remotely using deep learning technique based on non-iterative method. The combination of CNN and LSTM was made to learn directly the mapping relationship between the original signal and the measurements. This method has the ability to reconstruct original ECG signal more accurately without any prior knowledge. The results proved the reconstruction error is lower than other methods. The clinical requirement was achieved at CR \(\leq\) 70%, for Normal Sinus Rhythm Database (NSRDB), MIT-BIH Atrial Fibrillation Database (AFDB) and CR \(\leq\) 90% for EDB dataset. [PERSON] et al. [214] implemented a singular value decomposition (SVD) based compression procedure including period normalization, for ECG Arrhythmia signals. The decompressed data is given to a CNN and SVM models to classify abnormal ECG signal, certain commonly used indicators such as CR, PRD, PRDN, Root Mean Square Error (RMS), SNR and Quality Score (QS) achieved an Average value of 53.77, 9.23, 12.81, 5.28, 18.07 and 5.83 respectively. Even if some information is lost, a high-quality classification result can be achieved  
The study proposed by [PERSON] et al. [215] relates to multi-objective optimization-based ECG signal compression using a neural network. The neural network learns the changes in ECG characteristics and its structural parameters are adjusted under the guidance of the multi-objective function. By learning the diverse properties of ECG data compression, neural networks will adapt the parameters of network structure. The computational time required for obtaining original signal was less. For CR = 9, average PRD = 20 and average encoding time taken was around 7 s.  
In [216] [PERSON] et al. proposed 1-D CNN based Compressed Learning algorithm to classify compressed on-device multi-class ECG signal directly without reconstruction. Consequently, the processing power is drastically reduced and the performance of the proposed hardware design is validated by implementing the algorithm on various hardware design such as FPGA, Artix-7 Low power board and UMC 40 nm Low Power Process technology, the hardware architecture with the 1-D CL classifier achieves an energy efficiency of 0.83 \(\mu\)J/Classification under a 1.1 volt power supply at a frequency of 5 MHz. This strategy yielded a significant performance boost with an average accuracy of 98.35%, under CR = 0.2 and displayed an increase in average accuracy. Article [217] quantifies the impact of compressed sensing and reconstruction methods on ECG arrhythmia detection with SVM classifiers. [PERSON] et al. focused their work to determine acceptable compression ratios for ECG signals that retain critical information. Reconstruction of original signal is done through two widely used algorithms, such as BP and OMP and identification of the suitable CR which retains the necessary information of ECG signal. Outcome of the investigation illustrates that few sparsely sampled signals are sufficient for SVM classifier to detect type of arrhythmia, for the CR value up to around 1:7 ECG signals are recovered and then classified with the same quality for BP and OMP algorithms. Thereafter, after increasing the compression ratio BP outperformed OMP in detecting the arrhythmia for ECG signal, as a trade-off negative correlation was witnessed. In order to deal with missing data, [PERSON] et al. [218] proposed unsupervised deep blind compressed sensing concept and combined the signal reconstruction and classification in a single frame. The analysis of the signals is done directly from the partially observed or compressed domain. The results concluded that this method outperformed compared to other methods for 2:1 compression the average classification accuracy was 100% which contributed a superior result. [PERSON] et al.