## 1 Introduction  
The most common biomedical signal is the ECG signal, which is the ECG signal, and the arrhythmia are analyzed using Machine learning (ML). The ECG signal is a collection of _all-life_ data, which is a collection of  
## 1 Introduction  
Internet of Things (IoT) is a digital revolution which happens to be larger than any other technological revolution and its presence is felt in each and every domain, from huge commercial applications to small domestic applications, and thanks to the usage of smart connected devices, the phenomenon tends to become Internet of Every Thing [1; 2; 3]. IoT is intended to connect things of the physical world to the Internet for the exchange of information with a framework that comprises of various sensors, actuators wireless link, communication protocols, and data processing technologies that interact with each other to provide greater communication ability for various applications [4; 5]. This huge data collected from large sensor network causes a delay in network traffic which leads to the degradation of overall performance with respect to computational abilities, battery lifetime of devices and so on [6; 7; 8]. To optimize data transmission, various approaches have been explored, where CS seems to be a suitable contender to be incorporated [9; 10; 11]. The need for an IoT framework is vital in biomedical applications, especially in sharing the remote patient's body conditions and biomedical signals to a healthcare worker. One of the important biomedical signals under consideration is the Electrocardiograph (ECG) signal to diagnose the heart function of patients remotely. There is a compulsory need to compress the ECG signal before sending it to the cloud to meet the bandwidth capacity and to serve multiple parallel remote patients. It is also important that the quality of the decompressed signal should be good enough to yield accurate data analytics results in machine learning algorithms to predict arrhythmia [12; 13].  
In applying CS to the ECG signal and to communicate the information to IoT, a detailed study has to be carried out on the key research and technical aspects which includes the investigation of the best sensing phenomenon suitable for ECG, the selection of optimal sparsifying methods, the study of optimal reconstruction algorithms, the right choice of hardware for implementation, strategies of the ML methods and IoT modalities. These key points will be elaborated more in Section 6 after the fundamental background is elaborated upon in Sections 2-5. In a nutshell, the contribution of this paper is to address the above key research and technical aspects through a detailed review on various transforms and sensing matrices that are used for CS for ECG, a review on various sparsifying methods and compression algorithms for ECG, a review of CS reconstruction algorithms and the proper performance metrics, a review of ML models for ECG classification, a study of compressive learning and a review of hardware implementations and IoT methodologies. We have also recommended some possible outcomes from this review.  
This review article is organized as follows: Section 2 gives the theoretical concepts of CS with basic mathematical formulation, summarizes CS algorithms with various performance metrics used for classification and evaluation of reconstructed signal quality and applications. Section 3 starts with introduction to ECG signal, heart and its related diseases and implementation of CS on the ECG Signal. Section 4 discusses the IoT Framework used for remote patient monitoring. Section 5 focusses on various Deep Learning algorithms and their application for Bio-Medical signal analysis. Section 6 presents a detailed survey of previous work on CS in compressing the ECG signal, its merits, demerits, application and comparative analysis to quantify better CS algorithm, revealing the path for new research directions. Finally, Section 7 outlines the paper's conclusions.