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E-grāmata: Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation

(-), (Northern Arizona Univ, Usa)
  • Formāts: 248 pages
  • Izdošanas datums: 26-Aug-2021
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789811235979
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Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain StimulationPalielināt
  • Formāts: 248 pages
  • Izdošanas datums: 26-Aug-2021
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789811235979
Citas grāmatas par šo tēmu:

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The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremor, and machine learning. The machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This achievement demonstrates preliminary insight with respect to the concept of Network Centric Therapy, which essentially represents the Internet of Things for healthcare and the biomedical industry, inclusive of wearable and wireless inertial sensor systems, machine learning, and access to Cloud computing resources.Imperative to the realization of these objectives is the organization of the software development process. Requirements and pseudo code are derived, and software automation using Python for post-processing the inertial sensor signal data to a feature set for machine learning is progressively developed. A perspective of machine learning in terms of a conceptual basis and operational overview is provided. Subsequently, an assortment of machine learning algorithms is evaluated based on quantification of a reach and grasp task for essential tremor using a smartphone as a wearable and wireless accelerometer system.Furthermore, these skills regarding the software development process and machine learning applications with wearable and wireless inertial sensor systems enable new and novel biomedical research only bounded by the reader's creativity.Related Link(s)
Preface vii
List of Figures
xv
Chapter 1 Introduction
1(24)
1.1 Introduction
1(2)
1.2 Perspective of
Chapter 2
3(1)
1.3 Perspective of
Chapter 3
4(1)
1.4 Perspective of
Chapter 4
5(1)
1.5 Perspective of
Chapter 5
5(1)
1.6 Perspective of
Chapter 6
6(1)
1.7 Perspective of
Chapter 7
7(1)
1.8 Perspective of
Chapter 8
7(1)
1.9 Conclusion
8(17)
References
9(16)
Chapter 2 General Concept of Preliminary Network Centric Therapy Applying Deep Brain Stimulation for Ameliorating Movement Disorders with Machine Learning Classification using Python Based on Feedback from a Smartphone as a Wearable and Wireless System
25(38)
2.1 Introduction
25(2)
2.2 Movement Disorders, such as Essential Tremor and Parkinson's Disease, a General Perspective and Treatment
27(2)
2.3 Deep Brain Stimulation for the Treatment of Movement Disorders, such as Essential Tremor and Parkinson's Disease
29(5)
2.4 Quantification of Movement Disorder Status for Ascertaining Therapeutic Intervention Efficacy
34(5)
2.5 Smartphone Wearable and Wireless Inertial Sensor System
39(5)
2.6 Machine Learning and Software Automation
44(3)
2.7 The Objective of Machine Learning Classification for `On' and `Off' Status of Deep Brain Stimulation for the Treatment of Essential Tremor with Python Applied to Automate the Consolidation of the ARFF File
47(2)
2.8 Conclusion
49(14)
References
50(13)
Chapter 3 Global Algorithm Development
63(24)
3.1 Introduction
63(1)
3.2 Software Development Process
64(1)
3.3 Waterfall Model
64(1)
3.4 Incremental Development
65(1)
3.5 Requirements Definition
66(2)
3.6 Significance of Requirements
68(1)
3.7 Fagan Inspection
68(1)
3.8 The Value of Commenting
69(1)
3.9 Design and Implementation
70(1)
3.10 Software Testing
70(1)
3.11 The Comma-Separated Values File Storing the Acceleration Signal Derived from the Vibration Smartphone Application
71(1)
3.12 Pseudo Code Development
72(1)
3.13 Select an Appropriate Programming Language
73(1)
3.13.1 Python
74(1)
3.13.2 R
74(1)
3.13.3 Octave
74(1)
3.14 Selecting the Appropriate Programming Language: Python
74(1)
3.15 Anaconda Distribution and Jupyter Notebook
75(1)
3.16 Relevant Python Libraries
76(1)
3.17 Python Online Resources
77(1)
3.18 Additional Concept: Kaizen
78(1)
3.19 Conclusion
79(8)
References
80(7)
Chapter 4 Incremental Software Development using Python
87(20)
4.1 Introduction
87(1)
4.2 Review Requirements with Pseudo Code Interleaved
88(3)
4.3 Incremental Conversion of Requirements and Pseudo Code to Python Syntax
91(5)
4.4 Preliminary Testing and Evaluation of the Python Software
96(4)
4.5 Conclusion
100(7)
References
101(6)
Chapter 5 Automation of Feature Set Extraction using Python
107(30)
5.1 Introduction
107(1)
5.2 Information Organization Strategy
108(1)
5.3 Provisional Evolution of Requirements and Associated Pseudo Code
109(2)
5.4 Syntax Implementation using Python
111(2)
5.5 Rearranging Existing Python Code Outside of the Global For Statement
113(7)
5.6 Software Reuse
120(9)
5.7 Conclusion
129(8)
References
129(8)
Chapter 6 Waikato Environment for Knowledge Analysis (WEKA) a Perspective Consideration of Multiple Machine Learning Classification Algorithms and Applications
137(44)
6.1 Introduction
137(1)
6.2 Operational Perspective of WEKA
138(6)
6.2.1 Opening WEKA
138(1)
6.2.2 Weka Explorer Preprocess Panel
139(4)
6.2.3 Weka Explorer Classify Panel
143(1)
6.3 Prevalent WEKA Algorithms
144(25)
6.3.1 J48 Decision Tree
144(3)
6.3.2 K-Nearest Neighbors
147(2)
6.3.3 Logistic Regression
149(1)
6.3.4 Naive Bayes
150(2)
6.3.5 Support Vector Machine
152(2)
6.3.6 Random Forest
154(3)
6.3.7 Multilayer Perceptron Neural Network
157(1)
6.3.7.1 Backpropagation
158(2)
6.3.7.2 Additional Perspectives for the Multilayer Perceptron Neural Network
160(9)
6.4 Test Options for Machine Learning Classification
169(2)
6.5 Classifier Output for Machine Learning Classification
171(1)
6.6 Conclusion
172(9)
References
173(8)
Chapter 7 Machine Learning Classification of Essential Tremor using a Reach and Grasp Task with Deep Brain Stimulation System Set to `On' and `Off' Status
181(26)
7.1 Introduction
181(1)
7.2 Support Vector Machine
182(3)
7.3 J48 Decision Tree
185(2)
7.4 K-Nearest Neighbors
187(2)
7.5 Logistic Regression
189(1)
7.6 Naive Bayes
190(2)
7.7 Random Forest
192(1)
7.8 Multilayer Perceptron Neural Network
193(2)
7.9 Consideration of Most Appropriate Machine Learning Algorithms
195(3)
7.10 Conclusion
198(9)
References
200(7)
Chapter 8 Advanced Concepts
207(10)
8.1 Introduction
207(1)
8.2 Conformal Wearable and Wireless Inertial Sensor System for Quantifying Movement Disorder Response to an Assortment of Deep Brain Stimulation Parameter Configurations with Machine Learning
207(5)
8.3 Deep Learning for Differentiating Movement Disorder Tremor Response to Deep Brain Stimulation Parameter Configurations
212(1)
8.4 Conclusion
212(5)
References
213(4)
Index 217
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