## 1 Introduction  
In the United States, 200-450 thousand adults die yearly from sudden cardiac death (SCD). Annually, approximately 350,000 are out-of-hospital cardiac arrests (OHCA), according to the American Heart Association's 2020 report [1]. Statistics based on research reveal only a 10% chance for the out-of-hospital SCA survival rates [2]. Sixty-five percent of these OHCAs occur at home or in residence [3]. For accurate prediction of sudden cardiac arrests (SCAs), it is imperative to recognize high-risk patients and provide immediate treatment. Due to the retrospective nature of sudden cardiac arrest categorization, it is difficult to determine the circumstances surrounding the death [4]. Research suggests that there are many instances where discrepancies occur but are not diagnosed, resulting in SCD [5]. Several ECG markers have been identified as potential SCA markers, including\(QT\) prolongation, \(QRS\) duration, fragmented \(QRS\) complexes, early re-polarization, signal-averaged ECGs, \(T\)-wave alternans, and \(T\)-peak to \(T\)-end that may occur repetitively [6]. The timely intervention has an increased chance of survival, and it is possible to predict SCA in the early stages of Ventricular Fibrillation (VF) through continuous monitoring [7]. There is a dire need for an effective and efficient life-saving system to detect and prevent SCA. A functional wireless ECG system can facilitate monitoring of the heart's activity outside the clinical setting. It also allows for continuous (\(\geq\)24 h) monitoring of heart function, thus enabling detection and diagnosis of SCA at its early onset. One of the main challenges for the early detection and prevention of SCA is developing a ubiquitous, continuous, and efficient ECG transmission that is secure and reliable. The requirement is a wireless ECG monitoring system that provides an unlimited monitoring period, decreases the time to diagnosis, and improves patient care significantly. There has been prior work on wireless ECG transmission using Arduino boards and RF wireless modules. [PERSON] et. al. provides a detailed transmitter and receiver design, notes, and connections between Arduino Mega 2560 boards (Manufacturer: Arduino) and nRF24L01, (Manufacturer: Nordic Semiconductor ASA), focusing on minimization of the noise factor during wireless transmission [8]. However, performance parameters such as power consumption, data rate, the distance between transmitter and receiver, and bit error rate are not measured [9]. For continuous ECG monitoring, battery life and signal integrity are critical. Previous research has developed a wireless transmission through Bluetooth technology. This work uses the MAX30003 (Manufacturer: Analog Devices) system on a chip, which has an amplifier, filters, and processing components best suited for analog time-series signals such as ECG. MAX30003 is a large, bulky unit measuring 3.5 \(\times\) 1.5 inches, and Bluetooth technology increases power consumption due to a current of 600 mA [10]. A miniaturized, portable wireless transmission with at least a week (\(\geq\)24 h) of uninterrupted battery life is a factor to consider. [PERSON] et. al. has provided the current during wireless transmission and reception with a data rate of 1-2 Megabits per second (Mbps). However, the transmission range and the signal transmission accuracy are not measured [11]. An efficient ECG wireless system requires the transmission distance and the effect on the bit error rate. We aim to build a wireless system with low-power consumption and test continuous ECG transmission and reception over a small distance within lab premises with a negligible bit error rate to detect early signs of SCA. In addition to transmission, it is required to process the continuous ECG data collected over time. A machine learning (ML) model is well-suited and can easily handle extensive time-series-based biosignals such as ECG.