Artificial Intelligence (AI) has started to grow in the field of medicine at a tremendous rate. AI is also becoming one of the strong modes of diagnosis for a wide range of conditions. The major benefits offered by AI would be diagnosing the conditions, automating and making enhancements in them, and bringing about the ability to offer continuous monitoring of patients. This, in turn, would lead to better health outcomes resulting from improved monitoring and diagnosis. Such AI systems require large and varied patient data from the real world to build their clinical solutions. The data may be physiological signals such as electroencephalogram (EEG) and electrocardiogram (ECG), or imaging modalities that range from computed tomography and magnetic resonance imaging.
This book intends to report on state-of-the-art AI applications in human-machine interfaces, investigations of human attention, emotions, seizures, Alzheimer's disease, focal and non-focal disorders, abnormal heart rhythms, and leukemia detection. The book encompasses discussions of the in-depth techniques for the analysis and processing of both physiological such as EEG, and ECG, electronic health records, and physical signals such as human speech.
The book offers an in-depth exploration of advanced signal processing techniques, with a strong focus on incorporating machine learning and deep learning methods. It covers the entire process, from signal pre-processing to the automatic classification and identification of various phenogroups. This makes it an invaluable resource for anyone seeking a comprehensive understanding of how AI is being applied in modern healthcare diagnostics.
