Free Download Biological Signal Analysis
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 Author
Dr Ramaswamy Palaniappan
BE, MEngSc, PhD, SMIEEE, MIET, MBMES
School of Computer Science and Electronic Engineering
University of Essex, United Kingdom
Content
1. Introduction
1.1 A Typical Biological Signal Analysis Application
1.2 Examples of Common Biological Signals
1.2.1 Electrocardiogram
1.2.2 Electroencephalogram
1.2.3 Evoked Potential
1.2.4 Electromyogram
1.2.5 Phonocardiogram
1.2.6 Other Biological Signals
1.3 Contents of this book
1.4 References
2. Discrete-time signals and systems
2.1 Discrete-time signal
2.1.1 Sampling
2.1.2 Aliasing
2.2 Sequences
2.3 Basic Discrete-time System Operations
2.3.1 Product (modulation)
2.3.2 Addittion
2.3.3 Multiplication
2.3.4 Time reversal (folding)
2.3.5 Branching
2.3.6 Time shifting
2.3.7 Time scaling
2.3.8 Combination of operations
2.4 Examples on sequence operations
2.5 Bibliography
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 Periodogram
3.7.1 Welch method
3.8 References
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
4.7 References
5. Feature extraction
5.1 Simple features
5.2 Correlation
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
5.9 References
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
6.9 References
7. Applications
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
7.5 References
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