Introduction
Liu Hai Long edits the national life science and technology talent training base series teaching materials
Main task
The main task is: according to biomedical signals Features, the basic theory and method of application information science, study how to extract information carried in various biomedical signals from the observation records of interference and noise, and progressively analyze, interpret and classify them.
Biomedical signal processing, According to the characteristics of biomedical signals, analysis, interpretation, classification, display, storage, and transmission of the collected biomedical signals.
Biomedical signal is a low frequency weak signal in a strong noise background, which is an unstable natural signal emitted by a complex life, from the signal itself, detection mode to the processing Technology is different from the general signal.
From the nature of the electricity, it can be divided into electrical signals and non-electrical signals such as electrical, muscle electricity, and eunda equivalent; others such as body temperature, blood pressure, breathing, blood flow, pulse, Heart, etc. belongs to the non-electrical signal, and the non-electrical signal can be divided into: 1 mechanical amount, such as vibration (heart sound, pulse, heart shock, Korotkov sound, etc.), pressure (blood pressure, blood and digestive tract, etc.), force (myocardium) Tension, etc.); 2 thermal volume, such as body temperature; 3 optical amount, light transmittance (photoelectric pulveroid, blood oxygen saturation, etc.); 4 chemical, such as pH, blood gas, breathing gas, etc. For example, from the perspective of processing dimensions, it can be divided into one-dimensional signals and two-dimensional signals, such as body temperature, blood pressure, breathing, blood flow, pulse, heart sound, etc. belong to one-dimensional signal; and EEG, ECG, Muscle Electric, X Lights, ultrasound pictures, CT pictures, and nuclear magnetic resonance (MM) images are two-dimensional signals.
The detection method of biomedical signal is a technique for detecting and quantifying signals comprising information such as life, state, nature, variable, and ingredients in the organism. Research on biomedical signal processing is based on the characteristics of biomedical signals, analyzed, interpret, classify, store, and transmits the collected biomedical signals. The purpose of its research purposes is the study of biological architecture and function. Second, it is assisted to diagnose and treat diseases. Biomedical signal detection technology is a pilot technology in the study of biomedical engineering disciplines. Due to the different positions, purposes of researchers, the classification of biomedical signal detection technology is diversified, and the specific introduction is as follows: 1 Non-invasive testing, minimally invasive testing, invasive testing; 2 in physical testing, exile detection; 3 direct detection, indirect detection; 4 non-contact detection, body surface detection, body detection; 5 bioelectric detection, biological non-electricity detection; 6 morphological detection, functional detection; 7 inactive detection in a restraint, organism detection in natural state; 8 transmission detection, reflection method; ⑨ 1-dimensional signal detection, multi-dimensional signal detection; ⑩ remote sensing detection, Multi-dimensional signal detection; ⑩ One amount detection, secondary analysis test; ⑩ molecule level detection, cellular detection, system level detection.
Content introduction
This book is divided into 16 chapters: the main content has the mechanism of biological electromagnetic phenomena and its measurement; the basic knowledge of the signal; the task and basic principle of detection and estimation; Match filter, Vina filter, Kalman filter, adaptive filtering theory, design, and application; power spectrum estimation classic method, the basic theory of modern methods and various estimation algorithms; high order spectrum analysis theory and technical foundation; electrocardiogram, brain Electrical map, brain educated potential analysis, extraction, and treatment; treatment of brain neural network breeding potential.
This book is currently related to the comprehensive and system of biological signal processing. The author has worked for the first line of scientific research and education, so that the classics of this book goes deeply and simple, and it is tight With the forefront of the subject. In addition, according to the author's multi-year teaching work, this book has more examples and exercises to help readers.
This book can be used as a textbook for undergraduate students in biomedicine engineering, as well as reference books for researchers who are engaged in biomedical signals.
Book catalog
Chapter 1 Biological electromagnetic phenomenon generating mechanism and its measurement
1.1 Overview
1.2 Biological electromagnetic phenomenon and its production Mechanism
1. Measurement and Analysis of Biological Electromagnetic Signals
1.4 Biological Electromagnetic Signal Measurement Technology
Exercise
Chapter 2 Random Signal Analysis
2.1 Overview
2.2 Random Signal
2.3 Common Random Processes
2.4 Random Signals Union Characteristics
2.5 discrete time random signals
2.6 non-white noise orthogonal deployment
exercise
Chapter 3 random signal by linearity Untrovable system
3.1 Overview
3.2 III Linear When the system
3.3 multi-end linear When the system is unchanged system
3.4 discrete Random signals pass linear constant system
exercise
Chapter 4 signal detection
4.1 Overview
4.2 Common Test Guidelines (Test Criterion)
4.4 Multiple observation
4.4
exercise
Chapter 5 parameter estimation
5.1 Overview
5.2 Nonlinear Estimate
5.3 Application
5.4 Estimated Nature
5.5 Linear Estimate
exercise
Chapter 6 Power Spectrum estimation classic method
6.1 Overview
6.2 Estimation of autocorrelation
6.3 Periodic Chart and Its Estimation Quality
6.4 Improves Periodic Quality Method
Exercise
Chapter 7 Power Spectrum Estimation Modern Method
7.1 Overview
7.2 Spectrum estimation parameter model method
7.3 AR model Yule-Walker equation
7.4 levinson-i) URBIN Algorithm
7.5 AR model stability and its order of determination
7.6 Ar spectrum estimation
7.7 Flat filter
7.8 Ar Model parameter extraction method
7.9 AR spectrum estimation exception and its remedy
7.10 mA and ARMA model spectrum estimation
exercise
Chapter 8 Deterministic Signal Extract
8.1 Overview
8.2 Matching Filter in White Noise Background
8.3 Discrete time-related matching filter
8.4 Related Detection - Application of Like Raising
8.5 Non-white Noise Known Signal of Known Signals
8.6 Application example
8.7 coherent average method Extract brain induced potential
exercise
Chapter 9 Variocal filter
9.1 Overview
9.2 Waveform Linear Evaluation orthogonal principles
9.3 Vaja Hof (Wiener-Horf) Integral Equation
9.4 nonaffordic Vihan filter problem < /> (p
9.6 prediction problem
9.7, WiQ filter and complementary Wiwan filter
9.8 vector Dispersion Variant Filter
9.9 Time and Space Multi-Channel Different Variant Filtration
9.10 Linear Transformation Equivalent Discrete Vina Filter
9.11 Application Example
exercise
Chapter 10 Karman filter
10.1 Overview
10.2 Purity Calman filter
10.3 pure One step to predict
10.4 vector Karman filter
10.5 application example
exercise
Chapter 11 Adaptive filtering
11.1 Overview
11.2 Randomized gradient method of lateral structure
11.3 Application example
11.4 Random gradient method
11.5 Randomized gradient method of a form structure
11.6 Remature class:
exercise
Chapter 12 High Order Analysis
12.1 Overview
12.2 definition of third-order correlation and dual profiles and its nature
12.3 Accumulation and spectrum definitions and its nature
12.4 accumulation and multiple spectrum Estimate
12.5 Based high order spectrum estimation
12.6 Based on high order spectrum parameter estimation
12.7 Using high order spectrum determination model < / P>
exercise
QRS complex detection
13.1 Overview
13.1 Overview13.2 ECG Power Spectrum
13.3 Body Filter Method
13.4 Differential Method
13.5 Template Match
13.6 QRS Repelling detection algorithm
exercises
Chapter 14 Processing from Broken EEG
14.1 Overview
14.2 EEG Extraction of Figure
14.3 Quasi-Stable Segment
14.4 Feature Extraction - Traditional Method
14.5 Feature Extraction - Modern Method
< P> ExerciseChapter 15 to educate the EEG
15.1 Overview
15.2 Audiocreciator Extraction and Processing
15.3 Processing of visual induced potential
exercise
Chapter 16 Handling of cerebral neural network breasts
16.1 Overview
16.2 Classification of Cytokines
16.5 Related
16.5 Related
16.6
16.6 outbreak (BURST) signal processing
exercise
reference