Harnessing Deep Learning for Early Detection of Cardiac Abnormalities

Abstract

Sudden Cardiac Arrests (SCAs) are potentially fatal situations that strike suddenly, frequently without warning, and can have dire repercussions if left untreated. These incidents result in an abrupt loss of heart function caused by an electrical malfunction in the heart. For the purpose of increasing survival rates and reducing long-term damage, early detection and intervention are essential. In this context, there is great potential to improve response mechanisms and deepen our understanding of SCA by utilizing fog computing and Deep Learning (DL) for Internet of Things (IoT) devices. This study's main goal is to investigate how DL algorithms and fog computing can be used with IoT devices to better understand and anticipate sudden cardiac arrests. The goal is to create a reliable, real-time system that can recognize possible SCA events, examine pertinent data, and enable prompt intervention. The study uses a multidisciplinary methodology, combining fog computing for Internet of Things devices with machine learning techniques. With fog computing, real-time data from wearables-like smartwatches and health monitors-is gathered and processed at the edge. After that, patterns and anomalies in the data are analyzed using DL, this work utilizes the Multilayer Perceptron with ReLu as activation function for faster convergence, to find possible signs of an approaching SCA. The model achieved an average accuracy of 98.65%, out-performing previous models and converging faster. Another novel feature is the alert system which sends out an alert message whenever there is a predicted SCA. The study's findings show that the comprehension of SCA is greatly improved when DL and fog computing are combined with IoT devices. Real-time data processing and analysis capabilities of the system enable prompt and focused interventions that may even save lives.