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

Length: 248 pages
Edition: 1
Language: English
Publisher: WSPC
Publication Date: 2021-08-31
ISBN-10: 9811235953
ISBN-13: 9789811235955
Sales Rank: #0 (See Top 100 Books)

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 the organization of the software development process. Requirements and pseudo code are derived and software automation using Python for post-processing the inertial 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 provides. 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.

Contents
Preface
List of Figures
Chapter 1 Introduction
	1.1 Introduction
	1.2 Perspective of Chapter 2
	1.3 Perspective of Chapter 3
	1.4 Perspective of Chapter 4
	1.5 Perspective of Chapter 5
	1.6 Perspective of Chapter 6
	1.7 Perspective of Chapter 7
	1.8 Perspective of Chapter 8
	1.9 Conclusion
	References
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
	2.1 Introduction
	2.2 Movement Disorders, such as Essential Tremor and Parkinson’s Disease, a General Perspective and Treatment
	2.3 Deep Brain Stimulation for the Treatment of Movement Disorders, such as Essential Tremor and Parkinson’s Disease
	2.4 Quantification of Movement Disorder Status for Ascertaining Therapeutic Intervention Efficacy
	2.5 Smartphone Wearable and Wireless Inertial Sensor System
	2.6 Machine Learning and Software Automation
	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
	2.8 Conclusion
	References
Chapter 3 Global Algorithm Development
	3.1 Introduction
	3.2 Software Development Process
	3.3 Waterfall Model
	3.4 Incremental Development
	3.5 Requirements Definition
	3.6 Significance of Requirements
	3.7 Fagan Inspection
	3.8 The Value of Commenting
	3.9 Design and Implementation
	3.10 Software Testing
	3.11 The Comma-Separated Values File Storing the Acceleration Signal Derived from the Vibration Smartphone Application
	3.12 Pseudo Code Development
	3.13 Select an Appropriate Programming Language
		3.13.1 Python
		3.13.2 R
		3.13.3 Octave
	3.14 Selecting the Appropriate Programming Language: Python
	3.15 Anaconda Distribution and Jupyter Notebook
	3.16 Relevant Python Libraries
	3.17 Python Online Resources
	3.18 Additional Concept: Kaizen
	3.19 Conclusion
	References
Chapter 4 Incremental Software Development using Python
	4.1 Introduction
	4.2 Review Requirements with Pseudo Code Interleaved
	4.3 Incremental Conversion of Requirements and Pseudo Code to Python Syntax
	4.4 Preliminary Testing and Evaluation of the Python Software
	4.5 Conclusion
	References
Chapter 5 Automation of Feature Set Extraction using Python
	5.1 Introduction
	5.2 Information Organization Strategy
	5.3 Provisional Evolution of Requirements and Associated Pseudo Code
	5.4 Syntax Implementation using Python
	5.5 Rearranging Existing Python Code Outside of the Global For Statement
	5.6 Software Reuse
	5.7 Conclusion
	References
Chapter 6 Waikato Environment for Knowledge Analysis (WEKA) a Perspective Consideration of Multiple Machine Learning Classification Algorithms and Applications
	6.1 Introduction
	6.2 Operational Perspective of WEKA
		6.2.1 Opening WEKA
		6.2.2 Weka Explorer Preprocess Panel
		6.2.3 Weka Explorer Classify Panel
	6.3 Prevalent WEKA Algorithms
		6.3.1 J48 Decision Tree
		6.3.2 K-Nearest Neighbors
		6.3.3 Logistic Regression
		6.3.4 Naïve Bayes
		6.3.5 Support Vector Machine
		6.3.6 Random Forest
		6.3.7 Multilayer Perceptron Neural Network
			6.3.7.1 Backpropagation
			6.3.7.2 Additional Perspectives for the Multilayer Perceptron Neural Network
	6.4 Test Options for Machine Learning Classification
	6.5 Classifier Output for Machine Learning Classification
	6.6 Conclusion
	References
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
	7.1 Introduction
	7.2 Support Vector Machine
	7.3 J48 Decision Tree
	7.4 K-Nearest Neighbors
	7.5 Logistic Regression
	7.6 Naïve Bayes
	7.7 Random Forest
	7.8 Multilayer Perceptron Neural Network
	7.9 Consideration of Most Appropriate Machine Learning Algorithms
	7.10 Conclusion
	References
Chapter 8 Advanced Concepts
	8.1 Introduction
	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
	8.3 Deep Learning for Differentiating Movement Disorder Tremor Response to Deep Brain Stimulation Parameter Configurations
	8.4 Conclusion
	References
Index