Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning

This book explores the modern technological advancements and revolutions in the biomedical sector, focusing on sensing, IoT, machine learning, and cloud computing. It discusses the use of biomedical signals and images for monitoring and diagnosing health conditions, and the development of automated healthcare systems based on these technologies. The book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning, and will have a significant impact on the design and development of modern and effective healthcare systems.

Format: Hardback
Length: 373 pages
Publication date: 02 March 2023
Publisher: Springer International Publishing AG

The biomedical industry has seen significant advancements and revolutions in recent years, driven by modern technological developments. These advancements have introduced new approaches in mobile healthcare, enabling continuous observation of patients with critical health situations. One key area of focus is the integration of modern sensing, Internet of Things (IoT), and machine learning algorithms and architectures. By intelligently hybridizing these technologies, healthcare professionals can monitor patients' health status during daily life activities, such as sports, walking, and sleeping. This is made possible by leveraging the capabilities of the IoT framework, wireless biomedical implants, and cloud computing.

Currently, healthcare and governmental institutions, research laboratories, and biomedical companies are actively developing and testing these solutions. Biomedical signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG), phonocardiogram (PCG), chronic obstructive pulmonary (COP), electrooculography (EoG), photoplethysmography (PPG), and image modalities like positron emission tomography (PET), magnetic resonance imaging (MRI), and computerized tomography (CT), are non-invasively acquired, measured, and processed through biomedical sensors and gadgets. These signals and images provide valuable insights into the activities and conditions of human cardiovascular, neural, vision, and cerebral systems.

To effectively monitor and diagnose these complex systems, multi-channel sensing of these signals and images with appropriate granularity is required. However, the sheer volume of data generated by these sensors poses challenges for manual analysis. As a result, automated healthcare systems are evolving to address these challenges. These systems are primarily based on biomedical signal and image acquisition, pre-conditioning, features extraction, and classification stages.

By leveraging advanced technologies, automated healthcare systems can analyze large datasets and provide accurate diagnoses and predictions. They can also assist healthcare professionals in making informed decisions and improving patient outcomes.

In conclusion, the integration of modern sensing, IoT, machine learning, and cloud computing has opened up new possibilities for healthcare. Continuous observation of patients with critical health situations is now possible, and automated healthcare systems are evolving to handle the vast amounts of data generated by biomedical signals and images. These advancements have the potential to revolutionize the way we approach healthcare and improve patient outcomes.

Weight: 752g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031232381
Edition number: 1st ed. 2023