Biological Signal Analysis
Learn signal processing and classification for biological signals with MATLAB.
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- • Build a complete workflow for biological signal analysis with MATLAB.
- • Learn filtering, spectral analysis, and feature extraction from core principles.
- • Understand supervised classification methods and how to validate results.
- • See how the techniques apply to ECG/EEG/PCG and other biosignals.
الوصف
This book provides a fundamental understanding of signal processing techniques and classification algorithms for analysing biological signals. It introduces the origins and characteristics of common biosignals such as ECG, EEG, evoked potentials, EMG, and phonocardiogram, and explains why raw signals need processing to reveal relevant information. Using MATLAB throughout, the text guides readers through key steps in a practical analysis pipeline: signal conditioning, filtering, feature extraction, classification, and statistical validation. Chapters cover discrete-time signals and systems, Fourier transform and spectral estimation, digital filter design (FIR and IIR), feature extraction methods (including power spectral density and Hjorth descriptors), and supervised classification approaches such as k-nearest neighbour and multilayer perceptron. The final chapter ties the methods to real-world applications.
فهرس المحتويات
Preface About the author 1 Introduction 1.1 A Typical Biological Signal Analysis Application 1.2 Examples of Common Biological Signals 1.3 Contents of this book 1.4 References 2 Discrete-time signals and systems 2.1 Discrete-time signal 2.2 Sequences 2.3 Basic Discrete-time System 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 3.8 References 4 Digital Filtering 4.1 Filter Specifications 4.2 Direct filtering in frequency domain 4.3 Time domain filtering 4.4 Simple FIR filters 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 5.3 Autoregressive model 5.3 Spectral features – Power spectral density 5.4 Power spectral density derived features 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 classification? 6.2 Nearest Neighbour classifier 6.3 Artificial neuron 6.4 Multilayer-Perceptron neural network 6.5 MLP-BP classifier architecture 6.6 Performance measures 6.7 Cross validation 6.8 Statistical measure to compare two methods 6.9 References 7 Applications 7.1 Ectopic beat detection using ECG and BP signals 7.2 EEG based brain-computer interface design 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 Endnotes
معاينة
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 ...
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