Biomedical signal processing


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


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


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


Chapter 4 signal detection

4.1 Overview

4.2 Common Test Guidelines (Test Criterion)

4.4 Multiple observation



Chapter 5 parameter estimation

5.1 Overview

5.2 Nonlinear Estimate

5.3 Application

5.4 Estimated Nature

5.5 Linear Estimate


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


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


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


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


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


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:


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>


QRS complex detection

13.1 Overview

13.1 Overview

13.2 ECG Power Spectrum

13.3 Body Filter Method

13.4 Differential Method

13.5 Template Match

13.6 QRS Repelling detection algorithm


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> Exercise

Chapter 15 to educate the EEG

15.1 Overview

15.2 Audiocreciator Extraction and Processing

15.3 Processing of visual induced potential


Chapter 16 Handling of cerebral neural network breasts

16.1 Overview

16.2 Classification of Cytokines

16.5 Related

16.5 Related


16.6 outbreak (BURST) signal processing



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