Download for FREE in 4 easy steps...
You can also read this in Bookboon.com Premium
300+ Business books exclusively in our Premium eReader
- No adverts
- Advanced features
- Personal library
Users who viewed this item also viewed
Introduction to Cancer Biology
Micro- and Nano-Transport of Biomolecules
Large Scale Data Handling in Biology
Introduction to Clinical Biochemistry - Interpreting Blood Results
Introduction to Cognitive Neuroscience
Introduction to Scientific Research Projects
About the book
This textbook will provide the reader with an understanding of biological signals and digital signal analysis techniques such as conditioning, filtering, feature extraction, classification and statistical validation for solving practical biological signal analysis problems using MATLAB.
About the author
1.1 A Typical Biological Signal Analysis Application
1.2 Examples of Common Biological Signals
1.2.3 Evoked Potential
1.2.6 Other Biological Signals
1.3 Contents of this book
2. Discrete-time signals and systems
2.1 Discrete-time signal
2.3 Basic Discrete-time System Operations
2.3.1 Product (modulation)
2.3.4 Time reversal (folding)
2.3.6 Time shifting
2.3.7 Time scaling
2.3.8 Combination of operations
2.4 Examples on sequence operations
3. Fourier transform
3.1 Discrete frequency
3.2 Discrete Fourier transform
3.3 DFT computation using matrix relation
3.4 Picket fence effect
3.5 Effects of truncation
3.6 Examples of using DFT to compute magnitude spectrum
3.7.1 Welch method
4. Digital Filtering
4.1 Filter Specifi cations
4.1.1 Low-pass filter
4.1.2 High-pass filter
4.1.3 Band-pass and band-stop filters
4.2 Direct fi ltering in frequency domain
4.3 Time domain filtering
4.4 Simple FIR filters
4.4.1 Increasing the order of the simple filter
4.4.2 BPF design using sum and difference filter
4.5 FIR filter design using window method
4.6 IIR Filter design
5. Feature extraction
5.1 Simple features
5.2.1 Choosing the autoregressive model order
5.2.2 Autoregressive model to predict signal values
5.2.3 Autoregressive coeffi cients as features to discriminate mental tasks
5.3 Spectral features – Power spectral density
5.4 Power spectral density derived features
5.4.1 Asymmetry ratio PSD
5.4.2 Spectral correlation/coherence
5.4.3 Spectral peaks
5.5 Power spectral density computation using AR features
5.6 Hjorth descriptors
5.7 Time domain features
5.8 Joint time-frequency features
6. Classification methodologies
6.1 What is classifi cation?
6.2 Nearest Neighbour classifier
6.2.1 k-NN algorithm
6.2.2 Advantages and disadvantages of k-NN classifier
6.2.3 MATLAB program for k-NN
6.2.4 Reducing k-NN training dataset size
6.2.5 Condensed Nearest Neighbour
6.2.6 Edited Nearest Neighbour
6.3 Artificial neuron
6.4 Multilayer-Perceptron neural network
6.5 MLP-BP classifi er architecture
6.5.1 Training MLP-BP classifier
6.5.2 Testing the performance of MLP-BP classifier
6.5.3 MLP-BP classifi er implementation using MATLAB
6.5.4 MLP-BP problems
6.6 Performance measures
6.7 Cross validation
6.7.1 Equal class weight
6.7.2 Leave one out
6.8 Statistical measure to compare two methods
6.8.1 Hypothesis testing
7.1 Ectopic beat detection using ECG and BP signals
7.2 EEG based brain-computer interface design
7.2.1 BCI based on transient visual evoked potential
7.2.2 BCI based on mental tasks
7.3 Short-term visual memory impairment in alcohol abusers using visual evoked potential signals
7.4 Identification of heart sounds using phonocardiogram
The aim of this book is to provide readers with a fundamental understanding of signal processing techniques and classification algorithms for analysing biological signals. The text here will allow the reader to demonstrate understanding of basic principles of digital signals; awareness of physiology and characteristics of different biological signals; describe and apply pre- and post- processing techniques, such as conditioning, filtering, feature extraction, classification and statistical validation techniques for biological signals and solve practical biological signal analysis problems using MATLAB.
Final year undergraduates and graduates students in any field with interest in biological signal analysis (and related areas like digital signal processing) are the main target audiences. But the book will also be useful for the researchers in both industry and academia, especially those from non-technical background who would be interested in analysing biological signals - the text does not assume any prior signal processing knowledge and MATLAB is used throughout the text to minimise programming time and difficulty and concentrate on the analysis, which is the focus of this book.
I have tried to follow a simple approach in writing the text. Mathematics is used only where necessary and when used (and where possible), numerical examples that are suitable for paper and pencil approach are given. There are plenty of illustrations to aid the reader in understanding the signal analysis methods and the results of applying the methods. In the final chapter, I have given a few examples of recently studied real life biological signal analysis applications.
I hope I have done justice in discussing all four related sections to biological signal analysis: signal preprocessing, feature extraction, classification algorithms and statistical validation methods in this one volume. But by doing so, I had to skip some theoretical concepts which are not mandatory for implementing the concepts and I hope the learned ones will forgive these omissions.
I would like to acknowledge the efforts of my students, John Wilson, Cota Navin Gupta and Tugce Balli for their comments in various parts of the book. For over a decade, I have greatly benefited from discussions with students and fellow colleagues who are too many to name here but have all helped in one way or another towards the contents of this book. I must thank my wife and daughter for putting up with all the weekends and nights that I disappeared to complete this book. Finally, I trust that my proofreading is not perfect and some errors would remain in the text and I welcome any feedback or questions from the reader.
About the Author
Dr Ramaswamy Palaniappan
BE, MEngSc, PhD, SMIEEE, MIET, MBMES
School of Computer Science and Electronic Engineering
University of Essex, United Kingdom
Ramaswamy Palaniappan or fondly known as Palani among friends, received his first degree and MEngSc degree in electrical engineering and PhD degree in microelectronics/biomedical engineering in 1997, 1999 and 2002, respectively from University of Malaya, Kuala Lumpur, Malaysia. He is currently an academic with the School of Computer Science and Electronic Engineering, University of Essex, United Kingdom. Prior to this, he was the Associate Dean and Senior Lecturer at Multimedia University, Malaysia and Research Fellow in the Biomedical Engineering Research Centre-University of Washington Alliance, Nanyang Technological University, Singapore.
He is an expert reviewer for FWF Austrian Science Fund, Collaborative Health Research Projects Program, Natural Sciences and Engineering Research Council of Canada and Industry Grant Scheme, Malaysia. He founded and chaired the Bioinformatics division in Centre for Bioinformatics and Biometrics in Multimedia University, Malaysia. His current research interests include biological signal processing, brain-computer interfaces, biometrics, artificial neural networks, genetic algorithms, and image processing. To date, he has published over 100 papers in peer-reviewed journals, book chapters, and conference proceedings.
Dr. Palaniappan is a senior member of the Institute of Electrical and Electronics Engineers and IEEE Engineering in Medicine and Biology Society, member in Institution of Engineering and Technology, and Biomedical Engineering Society. He also serves as editorial board member for several international journals. His pioneering studies on using brain signals for brain-computer interfaces and biometrics have received international recognition.
The embed frame is free to use for private persons, universities and schools. It is not allowed to be used by any company for commercial purposes unless it is for media coverage. You may not modify, build upon, or block any portion or functionality of the embed frame, including but not limited to links back to the bookboon.com website.
The Embed frame may not be used as part of a commercial business offering. The embed frame is intended for private people who want to share eBooks on their website or blog, professors or teaching professionals who want to make an eBook available directly on their page, and media, journalists or bloggers who wants to discuss a given eBook
If you are in doubt about whether you can implement the embed frame, you are welcome to contact Thomas Buus Madsen on firstname.lastname@example.org and seek permission.