Compressive Sensing in Healthcare

Mahdi Khosravy, Nilanjan Dey, Carlos A. Duque

• Publisher: Academic Press
• Year: 2020
• Print ISBN: 9780128212479
• eBook ISBN: 9780128212486
• Topic: Technology & Engineering
• Subtopic: Engineering General
• Index: Technology & Engineering

Compressive Sensing in Healthcare, part of the Advances in Ubiquitous Sensing Applications for Healthcare series gives a review on compressive sensing techniques in a practical way, also presenting deterministic compressive sensing techniques that can be used in the field. The focus of the book is on healthcare applications for this technology. It is intended for both the creators of this technology and the end users of these products. The content includes the use of EEG and ECG, plus hardware and software requirements for building projects. Body area networks and body sensor networks are explored.- Provides a toolbox for compressive sensing in health, presenting both mathematical and coding information- Presents an intuitive introduction to compressive sensing, including MATLAB tutorials- Covers applications of compressive sensing in health care

Chapter 1

Compressive sensing theoretical foundations in a nutshell

Mahdi Khosravy; Naoko Nitta; Kazuaki Nakamura; Noboru Babaguchi Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan

Abstract

Compressive sensing is a well-established technique for signal/image acquisition with a considerably low sampling rate. It efficiently samples the data in a rate much lower than the classic requirement in uniform sampling by the Nyquist–Shannon sampling rate. Compressive sensing is based on a sparsity consideration of the information sources and it results in much lower requirement as regards the rate of data collection, sensory devices, required memory storage, and the power needed for sensory devices. This chapter briefly reviews the theoretical fundamental requirements for compressive data acquisition if it is to maintain the possibility of original data recovery. The connection of compressive sensing with sparseness of information and its confronting with the Nyquist sampling theorem is discussed. Compressive sensing comprises two main challenges: (i) How to design a compressive sensing matrix which senses a signal segment with a much smaller number of measurements than the signal segment length, ensuring that the information inside the signal is preserved. (ii) How to recover the signal from a segment of less shorter measurements. The chapter mainly explains the answer to the first question, as it clarifies the connection of compressive sensing with sparsity. The recovery methods in compressive sensing are out of this chapter's scope, and it mainly focuses on how to compressively sense data to keep the possibility of...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Chapter 1: Compressive sensing theoretical foundations in a nutshell
• Chapter 2: Recovery in compressive sensing: a review
• Chapter 3: A descriptive review to sparsity measures
• Chapter 4: Compressive sensing in practice and potential advancements
• Chapter 5: A review of deterministic sensing matrices
• Chapter 6: Deterministic compressive sensing by chirp codes: a descriptive tutorial
• Chapter 7: Deterministic compressive sensing by chirp codes: a MATLAB® tutorial
• Chapter 8: Cyber physical systems for healthcare applications using compressive sensing
• Chapter 9: Compressive sensing of electrocardiogram
• Chapter 10: Multichannel ECG reconstruction based on joint compressed sensing for healthcare applications
• Chapter 11: Neural signal compressive sensing
• Chapter 12: Level-crossing sampling: principles, circuits, and processing for healthcare applications
• Chapter 13: Compressive sensing of electroencephalogram: a review
• Chapter 14: Calibrationless parallel compressed sensing reconstruction for rapid magnetic resonance imaging
• Index