Monitoring and Diagnosis of Health Using Deep Learning Methods

Abstract

The smart sustainable healthcare system is mainly focused on artificial Intelligence (AI), 3-dimensional (3D) printing, augmented reality/virtual reality (AR/VR), 5G (fifth generation) communication development, Internet of Health Things (IoHT), robotics, and nano-biotechnology. 5G technology provides gigantic connectivity, high-speed internet, and improved data transfer efficiency. Real-time data and visualization of high-quality video displays create significant medical care and consultation for patients lacking the support of medical experts. It shows sophisticated software-enabled networks, blockchain analysis, and cloud computing techniques making huge data transfer with limited time. Digital technology found a novel way to connect medical experts and patients to form a healthy community. In the past decades deep learning deployed to recognize objects, segment, speech observation, and translate. The modern system focused more on health monitoring and diagnosis carried out in the presence of abundant sensors at low cost and the Internet of Things. The artificial intelligence model is applied to analyze huge volume samples, and its interpretation is easy through heat maps; models provide more accuracy. The performance of human pathologists to diagnose cancer increased with the application of a deep-learning model. This chapter focuses more on the medical applications, especially in the monitoring of health conditions using ECG signal analysis by lab view and discrete wavelet transform (DWT) and appropriate diagnosis with proteomics in the field of the human health system.