In the present age, heart disease is one of the most common medical conditions, which results in various adversities, and nearly 26 million patients suffer from critical heart conditions every year. To eradicate the risks of heart failures, patient monitoring and diagnosis can act as a substantial component of the healthcare system that can be integrated either at hospitals or at home according to the patient’s convenience. Recently, the Internet of Things (IoT) paradigm offers an interconnected network for a plethora of sensors and devices that can be leveraged to address the growing health challenges by monitoring cardiovascular failure, lung failure and cardiovascular diseases to provide an early warning to the caregivers. Indeed, these problems need continuous health monitoring in a non-intrusive manner so that the daily activities of the patients/observed users are not hindered. Portable yet accurate Electrocardiography (ECG) systems for monitoring heart rates have become very popular in the medical domain; however, their scopes for next-generation security applications are yet to be considered from a practical viewpoint. Hence, this chapter couples the application of IoT with ECG monitoring by demonstrating the construction of an edge hardware setup with reasonable accuracy for localized detection of supraventricular arrhythmia that can be leveraged for a bio-signal authentication use-case in the edge. The system architecture can be employed to measure and monitor the patient’s ECG parameters in real-time and determine whether the patient is suffering from arrhythmia. ECG sensor (Pulse Heart Rate Sensor) collects the target user’s ECG and transfers that data to edge devices such as the Raspberry Pi model, and then the ECG prediction is performed locally. Using the publicly available ECG signals (MIT-BIS Supraventricular dataset), a lightweight machine learning model is trained in the cloud and then transferred to the edge device for ECG monitoring. We demonstrate a portable prototype construction that has two applications. First, it can be easily deployed to the users (e.g., patients) for remote home monitoring for ECG data acquisition (with a live display) that can be locally processed, and anomalous events can be reported to caregivers. The second application consists of the concept of an ECG-based continuous biometric authentication scheme for a secure application use-case. The considered system aims to identify the change from an authorized user to an unauthorized user for user monitoring apps using ECG signals and discuss the challenges.