## 1 Introduction
The software implementation of the ML model, which includes training and testing on the ECG data with high accuracy, will benefit Physicians in processing data. Until now, there has been no test or system to predict when SCA will occur and who is at high risk for needing a doctor's attention. A portable hardware unit must implement the software ML model to quickly process the large ECG data in real-time and may be combined with a wireless SCA prediction system. The choice of the ML model is an important factor for an efficient system. Prior research indicates that simulation-based ML helps predict out-of-hospital cardiac arrests. [PERSON] et. al. proposed a classification model using MIT-BIH SCA data. These data contain four non-linear features: approximate entropy (ApEN), Hurst exponent, sample entropy (SampEN), and the most prominent Lyapunov exponent (LLE) are calculated from 1-min ECG segments to predict SCA occurrence before the VF onset (actual SCA trigger point). This work recommends a support vector machine (SVM) as the best-suited model with a maximum mean classification rate of 100%. A comparison between the SVM model and a neural network concludes that a neural network increases the complexity and is not the best option for ECG heartbeat classification [5]. However, the non-linear feature calculation at the receiver end requires additional processing and is a time-consuming part, delaying the inputs to the ML model. [PERSON] et. al. utilized four-time domain features with a Multi-Layer Perceptron (MLP) algorithm and reported 67.44% accuracy in predicting SCA using heart rate variability (HRV). The model's accuracy is low and did not discuss the reasons for choosing the MLP model. Hardwareimplementation of the MLP model is not discussed. Another HRV-based prediction model was developed for detecting SCA at an early stage with an accuracy of 86.8% and 94.7% using the k-Nearest Neighbor (kNN), and support vector machine (SVM) classifiers [12]. The disadvantage of HRV signals is that their processing is computationally intensive [7]. Also, the detection of SCA is not accurate and dependent on HRV data. ECG abnormality detection has recently been based on neural network algorithms. Prior work with an improved prediction performance used a CNN model on data collected from the Pediatric ICU at the Hospital for Sick Children in Toronto. By aggregating risk predictions, cardiac arrest prediction was made possible. However, the model has limited applications and cannot be applied to new subjects [13]. Our purpose is to build an optimized convolutional neural network (CNN) model that will enhance SCA prediction from the ECG data of any new person. The primary contribution of this paper is to build a prototype for wireless transmission of digitized ECG bits using a microcontroller and transceiver and test the prototype within the lab for efficient communication and reception. The wireless module combines our prior work on a readout circuit and zeolite-based dry electrodes for a complete ECG wireless SCA prediction system. The first work to demonstrate and test a wireless transmission of ECG bits at a 250 kbps data rate, determine the power consumption along with bit error rate percentages (loss of bits or improper communication), and demonstrate SCA heartbeat classification on a portable NVIDIA Jetson Nano System-on-Module. Thus, the paper's novelty is integrating an ECG wireless transmission system with a CNN model toward building a complete SCA heartbeat classification system best suited for a new subject's ECG data. The overview of the compact wireless ECG transmission system is shown in Figure 1, in which a wireless module, using the analog-digital converter (ADC) unit from the Arduino Nano microcontroller and RF Transceiver (nRF24L01), is shown using a dotted line. The transceiver part is the current focus of this paper. Our prior work involving the FR4 substrate-based readout circuit and zeolites-based dry electrodes is used to acquire and pre-process the ECG data [14; 15].