Название: Advanced Methods in Biomedical Signal Processing and AnalysisАвтор: Kunal Pal, Samit Ari, Arindam Bit
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 419
Язык: английский
Формат: pdf
Размер: 31.4 MB
Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate variability, analysis of the RRI time-series signals, joint time-frequency analyses, wavelet transforms and wavelet packet decomposition, empirical mode decomposition, modeling of biosignals, Gabor Transform, empirical mode decomposition. The book also gives an understanding of feature extraction, feature ranking, and feature selection methods, while also demonstrating how to apply Artificial Intelligence and Machine Learning to biosignal techniques.
Projects related to Machine Learning are being developed according to common practices which are formalized in a form of standards and frameworks. One of the most popular one is called CRISP-DM: cross-industry standard process in data mining. The process of machine learning model development starts with understanding the business domain and formulating the problem which should be solved. This problem is associated with dataand ultimately should besolved using the data available. Then starts the first stage of the Feature Engineering process: Data Understanding and exploration. After the characteristics of data are available and it is clear what it is possible to dowith them, engineers start preparing the data for training the models. In terms of Feature Engineering, the stages of data processing, feature extraction and feature reduction are implemented and they constitute the Data Preparation stage. Then the features are ready for model training and evaluation of their performance.
- Gives advanced methods in signal processing
- Includes machine and deep learning methods
- Presents experimental case studies
Research in Medical Devices and Robotics is at the forefront of technology due to the focus on automation and technology in this era. Incorporation of safe, sustainable, and intelligent robotics systems is one of the goals of modern technology-driven development. This can be the driving force of the technological revolution that we are standing on the brink of. The First Industrial Revolution used steam power to mechanize production. The Second Revolution utilized electric power to enhance production and bring living comfort to population at large. The Third Revolution used electronics and information technology to automate and create connected production. The Fourth Industrial Revolution is using biohuman connectivity to technology to enable realistic safe, sustainable, and naturalistic products. Although the revolution has been occurring since the middle of the last century, last few years have shown tremendous growth in this arena. Significant challenges do remain on enabling such revolutions and technological developments. In this survey, we highlight recent developments in various aspects of this domain including:
1. Robotics in healthcare
2. Noninvasive robotic application
3. Surgical continuum and soft robotics
4. AI inspired medical robots
5. Augmented Reality assisted Robotics
6. Robotics in covid scenario
7. Regulatory issues
Readership:
Biomedical engineering, signal processing, speech processing, and Computer Science researchers and graduate students.
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