Integrated Platform for Patient Abnormality Detection through GEM enabled Deep Transnet

A patient monitoring system is necessary to monitor patient data remotely and precisely, at all times. It is necessary to give a high degree of care to individuals who are at high risk following post-surgical situations. Recent patient monitoring systems are being used for further diagnostic analyses and treatments that are open sourced and might benefit people all around the world. The system's goal is to provide a comprehensive monitoring and analysis platform that provides a one-stop solution for patient monitoring systems. The major goal is to develop a model utilizing Gaussian Expectation Maximization (GEM) enabled Deep Transnet (GEDT) algorithm, for effective detection of patient abnormality. Various physiological parameters are considered for analysis. The data acquired for ECG analysis is compared to the MIT BIH dataset from Physio Net. The standard values for heart rate, temperature, pressure, and oxygen saturation are used. Real-time testing is suggested, with volunteers helping to evaluate the gear. Only a subset of real-time values is used to collect training data. 70% are utilized for training, 15% for testing, and 15% for validation.