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Fractals: Applications in Biological Signalling and Image Processing [Kõva köide]

(RMIT University, Melbourne, Australia), (RMIT University, Melbourne, Australia), (RMIT University, Melbourne, Australia)
  • Formaat: Hardback, 192 pages, kõrgus x laius: 234x156 mm, kaal: 498 g, 10 Illustrations, color; 30 Illustrations, black and white
  • Ilmumisaeg: 04-Oct-2016
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498744214
  • ISBN-13: 9781498744218
Teised raamatud teemal:
Fractals: Applications in Biological Signalling and Image ProcessingSuurem pilt
  • Formaat: Hardback, 192 pages, kõrgus x laius: 234x156 mm, kaal: 498 g, 10 Illustrations, color; 30 Illustrations, black and white
  • Ilmumisaeg: 04-Oct-2016
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498744214
  • ISBN-13: 9781498744218
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781498744218.html
Märksõnad:

The book provides an insight into the advantages and limitations of the use of fractals in biomedical data. It begins with a brief introduction to the concept of fractals and other associated measures and describes applications for biomedical signals and images. Properties of biological data in relations to fractals and entropy, and the association with health and ageing are also covered. The book provides a detailed description of new techniques on physiological signals and images based on the fractal and chaos theory.

The aim of this book is to serve as a comprehensive guide for researchers and readers interested in biomedical signal and image processing and feature extraction for disease risk analyses and rehabilitation applications. While it provides the mathematical rigor for those readers interested in such details, it also describes the topic intuitively such that it is suitable for audience who are interested in applying the methods to healthcare and clinical applications. The book is the outcome of years of research by the authors and is comprehensive and includes other reported outcomes.

Preface v
List of Figures xiii
1 Introduction 1(7)
Abstract
1(1)
1.1 Introduction
1(3)
1.2 History of Fractal Analysis
4(1)
1.3 Fundamentals of Fractals
4(1)
1.4 Definition of Fractal
5(1)
1.5 Complexity of Biological Systems
6(1)
1.6 Fractal Dimension
7(1)
1.7 Summary of this Book
7(1)
References
7(1)
2 Physiology, Anatomy and Fractal Properties 8(16)
Abstract
8(1)
2.1 Introduction
8(2)
2.2 Conceptual Understanding
10(1)
2.3 Chaos, Complexity, Fractals and Entropy
10(1)
2.4 Chaos Theory
11(2)
2.5 Complex Systems
13(1)
2.6 Entropy
14(2)
2.7 Fractal and Fractal Dimension
16(1)
2.8 Computing Fractal Dimension
16(2)
2.8.1 Box-counting
17(1)
2.8.2 Power spectrum fractal dimension
18(1)
2.9 Relationship of Fractals and Self-similarity
18(1)
2.9.1 Sierpinski triangle
18(1)
2.9.2 Fractal dimension of the Menger Sponge
19(1)
2.10 Fractals in Biology
19(1)
2.11 Properties of Natural and Synthetic Objects
20(1)
2.12 Human Physiology
21(1)
2.12.1 Fractals and Electrocardiogram (ECG), Electromyogram (EMG) and Electroencephalogram (EEG)
21(1)
2.12.2 Fractal dimension for human movement and gait analysis
22(1)
2.13 Summary
22(1)
References
22(2)
3 Fractal Dimension of Biosignals 24(18)
Abstract
24(1)
3.1 Introduction
24(1)
3.2 Fractal Dimension and Self-similarity
25(4)
3.2.1 Self-similarity
26(1)
Exact self-similarity
26(1)
Approximate self-similarity
26(1)
Statistical self-similarity
27(1)
3.2.2 Fractal dimension
27(2)
3.3 Different Methods to Estimate Fractal Dimension of a Waveform
29(5)
3.3.1 Box-counting method
29(1)
3.3.2 Katz's algorithm
30(1)
3.3.3 Higuchi's algorithm
31(1)
3.3.4 Petrosian's algorithm
31(1)
3.3.5 Sevcik's algorithm
32(1)
3.3.6 Correlation dimension
33(1)
3.3.7 Adapted box fractal dimension
33(1)
3.3.8 Fractal dimension estimate based on power law function
33(1)
3.4 Fractals and Electrocardiogram (ECG), Electromyogram (EMG) and Electroencephalogram (EEG)
34(2)
3.5 Fractal Dimension for Gait Analysis
36(2)
3.5.1 Example
37(1)
3.6 Summary
38(1)
References
39(3)
4 Fractals Analysis of Electrocardiogram 42(18)
Abstract
42(1)
4.1 Introduction
42(4)
4.1.1 Recording cardiac activity
44(2)
4.2 Heart Rate Variability
46(2)
4.2.1 Computing heart rate variability
47(1)
4.3 Fractal Properties of ECG
48(1)
4.4 An Example
49(1)
4.5 Poincare Plot of Heart-rate Variability
49(2)
4.6 Application—ECG and Heart Rate Variability
51(6)
Time domain analysis
53(1)
Frequency domain analysis
54(1)
Poincare analysis
55(2)
Fractal dimension
57(1)
4.7 Summary
57(1)
References
58(2)
5 Fractals Analysis of Surface Electromyogram 60(14)
Abstract
60(1)
5.1 Introduction
60(2)
5.2 Surface Electromyogram (sEMG)
62(3)
5.2.1 Principles of sEMG
63(1)
5.2.2 Factors that influence sEMG
63(1)
5.2.3 Signal features of sEMG
64(1)
Amplitude analysis
64(1)
Spectral analysis
64(1)
Statistical and chaos based features
65(1)
5.3 Fractal Analysis of sEMG
65(6)
5.3.1 Self-similarity of sEMG
65(2)
5.3.2 Algorithms to compute fractal dimension of sEMG
67(1)
Signals in the time domain
67(1)
Signals in the phase space domain
67(1)
5.3.3 Fractal features of sEMG
67(4)
5.4 Summary
71(1)
References
72(2)
6 Fractals Analysis of Electroencephalogram 74(15)
Abstract
74(1)
6.1 Introduction
74(2)
6.1.1 History of EEG
75(1)
6.1.2 Fundamentals of EEG
75(1)
6.2 Techniques for EEG Analysis
76(2)
6.3 Fractal Properties of EEG
78(1)
6.4 An Example—Measuring Alertness Using Fractal Properties of EEG
79(7)
6.4.1 Experimental setup
80(1)
6.4.2 Subjects
81(1)
6.4.3 Stimuli
81(1)
6.4.4 EEG recording and processing
81(1)
6.4.5 Experimental procedure
82(1)
6.4.6 Alertness measure
82(1)
6.4.7 Data analysis
82(1)
Correlation analysis
83(1)
Correlation coefficient
83(1)
6.4.8 Discussion
83(3)
6.5 Summary
86(1)
References
87(2)
7 Fractal Analysis of Biomedical Images 89(13)
Abstract
89(1)
7.1 Introduction
89(1)
7.2 Fractal Geometry and Self-similarity
90(1)
7.3 Entropy, Fractals and Tortuosity
91(3)
7.4 Binary Box-count Fractal Dimension
94(1)
7.5 Differential (3D) Box-counting Dimension
95(2)
7.6 Spectral Fractal Dimension
97(1)
7.7 Higuchi's Fractal Dimension
98(3)
7.8 Summary
101(1)
References
101(1)
8 Fractal Dimension of Retinal Vasculature 102(12)
Abstract
102(1)
8.1 Introduction to Human Eye Anatomy
102(1)
8.2 Eye Fundus Retinopathy—Disease Manifestation in Retina
103(1)
8.3 FD and Age Related Changes of Retinal Vasculature
104(1)
8.4 FD and Hypertensive Retinopathy
105(1)
8.5 FD and Risk of Stroke Event
106(2)
8.6 FD and Diabetic Retinopathy
108(2)
8.7 Summary
110(1)
References
111(3)
9 Fractal Dimension of Mammograms 114(13)
Abstract
114(1)
9.1 Introduction
114(1)
9.2 Mammography and Properties of Breast Tissue
115(3)
9.3 Fractal Irregularities of Breast Tissues
118(3)
9.4 Fractal Based Detection of Breast Cancer and the Tumor Types
121(4)
9.5 Summary
125(1)
References
125(2)
10 Fractal Dimension of Skin Lesions 127(13)
Abstract
127(1)
10.1 Introduction
127(1)
10.2 Fundamentals of Skin
128(3)
10.2.1 Epidermis
129(1)
10.2.2 Dermis
130(1)
10.2.3 Hypodermis layer
131(1)
10.3 Skin Lesions and Abnormalities
131(3)
10.3.1 Benign abnormalities of skin
132(1)
10.3.2 Malignant lesions—skin cancer
132(10)
Basal cell skin cancer
133(1)
Squamous skin cancer
133(1)
Melanoma
133(1)
10.4 Skin Cancer and Associated Changes to FD
134(1)
10.5 FD of the Ageing Skin
135(3)
10.6 Summary
138(1)
References
138(2)
11 Case study I: Age Associated Change of Complexity 140(13)
Abstract
140(1)
11.1 Introduction
140(1)
11.2 Physiological Basis
141(1)
11.3 Ageing Muscles and Fractal Properties
142(4)
11.3.1 Materials
144(1)
11.3.2 Methods
145(1)
11.3.3 Results and discussion
145(1)
11.4 Ageing Heart and Changes to ECG
146(1)
11.4.1 Materials
147(1)
11.4.2 Methods
147(1)
11.4.3 Results and discussion
147(1)
11.5 Ageing Eyes and FD of Eye-fundus Images
147(3)
11.5.1 Materials
148(1)
11.5.2 Methods
148(1)
11.5.3 Results
149(1)
11.5.4 Discussion
150(1)
11.6 Summary
150(1)
References
150(3)
12 Case Study 2: Health, Well-being and Fractal Properties 153(20)
Abstract
153(1)
12.1 Introduction
153(1)
12.2 Risk of Stroke and Retinal Fractal
154(6)
12.2.1 Materials
154(1)
12.2.2 Method
155(2)
12.2.3 Data analysis
157(1)
12.2.4 Results
157(3)
12.2.5 Discussion and conclusion
160(1)
12.3 Diabetes and Retinal Fractals
160(7)
12.3.1 Materials
161(1)
12.3.2 Method
162(1)
12.3.3 Data analysis
163(1)
12.3.4 Results
164(2)
12.3.5 Discussion and conclusion
166(1)
12.4 Muscle Fatigue and Fractal Properties
167(3)
12.4.1 Materials
168(1)
12.4.2 Methods
169(1)
12.4.3 Results and discussion
169(1)
12.5 Summary
170(1)
References
170(3)
Index 173
Dinesh Kumar, Sridhar P. Arjunan, Behzad Aliahmad