Wearable and Wireless Systems for Healthcare II (eBook)

Dr. Robert LeMoyne is currently serving as an Adjunct Professor for Northern Arizona University for the Department of Biological Sciences and Center for Bioengineering Innovation. At Northern Arizona University he is researching advanced technology concepts for wearable and wireless systems for biomedical applications. He earned his PhD in Biomedical Engineering from University of California Los Angeles (UCLA) during 2010. From 2010 to 2012 he served Sandia National Laboratories, and since 2013 he has been serving Northern Arizona University. Dr. LeMoyne has first authored over 100 technical proceedings that have been cited over 1000 times. From a biomedical engineering perspective his research interests emphasize deep brain stimulation systems, prosthetic technologies, machine learning applications, and wearable and wireless systems for biomedical applications, such as through smartphones and portable media devices, for accessing health status, such as Parkinson’s disease and Essential tremor. Timothy Mastroianni is a Cognitive Scientist, Researcher, Entrepreneur. First to develop and use computer vision and pattern recognition in a non-invasive manner to discover the internal states of the random number generator in machines (HiLoClient). Later presented these algorithms and methods to Carnegie Mellon University to map the human brain using machine learning and fMRI to discover brain states during specific tasks. Nestor D. Tomycz MD was born and raised in Flint, MI.  He graduated from Harvard College summa cum laude in chemistry in 2001 and earned his MD from Harvard Medical School in 2005.  His general surgery internship and neurosurgery residency were completed at the University of Pittsburgh Medical Center and he completed a fellowship in functional and stereotactic neurosurgery at Allegheny General Hospital under Dr. Donald Whiting.  He is currently director of stereotactic/functional neurosurgery and director of neurosurgical pain division at Allegheny General Hospital.  His research interests include deep brain stimulation, spinal cord stimulation, chronic pain, and neurodegenerative disorders.  Donald Mark Whiting was raised in Altoona, Pennsylvania and began his undergraduate education at Grinnell College in Iowa in 1976. He received an MS in Physiology at Georgetown University in 1981 and was awarded his medical degree in 1985 after training at Thomas Jefferson Medical College in Philadelphia. Donald then completed his neurosurgical residency at The Cleveland Clinic and a Neurotrauma fellowship at Allegheny General Hospital. In 1992, Donald Whiting joined a practice in Washington, PA that would later merge with the health system now known as Allegheny Health Network. He became the System Chair for their Neuroscience Institute and a Professor of Neurosurgery for Drexel University in 2014. Three years later, he also accepted the role of President of Allegheny Clinic. Amongst his peers, Donald Whiting is a respected physician-leader that is passionate about changing the way health care is delivered. Donald has authored over 60 scientific articles and chapters in medical textbooks and various publications such as The Journal of Neurosurgery & Current Concepts in Movement Disorder Management. He is an active member in many of his field’s leading professional and scientific organizations, including the Congress of Neurological Surgeons, the American Association of Neurological Surgeons, and the American Society of Stereotactic and Functional Neurosurgery. It’s no wonder Donald Whiting has been the annual recipient of “Top Doctor/Surgeon” awards on both local & national levels. He is regarded as one of the nation’s foremost experts in the use of deep-brain stimulation to control the debilitating motor symptoms of patients with Parkinson’s disease and other movement disorders, helping establish Allegheny General Hospital in Pittsburgh, Pennsylvania as one of the top centers in the world for DBS treatment. His sub-specialties include complex spine surgery, disc replacement/motion preservation spine surgery, the surgical treatment of movement disorders & Neurosurgical management of pain. In his spare time, he enjoys spending time with his family, making pizza & wine.

Chapter 1: Wearable and wireless systems for movement disorder evaluation and deep brain stimulation systems1.1 Introduction 1.2 Perspectives of the chapters1.2.1 Perspective of Chapter 2:      Movement disorders: Parkinson’s disease and Essential tremor, a general perspective1.2.2 Perspective of Chapter 3:      Traditional ordinal strategies for establishing the severity and status of movement disorders, such as Parkinson's disease and Essential tremor1.2.3 Perspective of Chapter 4:      Deep brain stimulation for the treatment of movement disorder regarding Parkinson's disease and Essential tremor with device characterization1.2.4 Perspective of Chapter 5:      Surgical procedure for deep brain stimulation implantation and operative phase with post-operative risks1.2.5 Perspective of Chapter 6:      Preliminary wearable and locally wireless systems for quantification of Parkinson's disease tremor and Essential tremor characteristics1.2.6 Perspective of Chapter 7:      Wearable and wireless systems with Internet connectivity for quantification of Parkinson's disease tremor and Essential tremor characteristics1.2.7 Perspective of Chapter 8:      Role of machine learning for classification of movement disorder and deep brain stimulation status1.2.8 Perspective of Chapter 9:      Assessment of machine learning classification strategies for the differentiation of deep brain stimulation ‘On’ and ‘Off’ status for Parkinson’s disease1.2.9 Perspective of Chapter 10:      New perspectives for Network Centric Therapy for the treatment of Parkinson's disease and Essential tremor1.3 ConclusionReferences  Chapter 2: Movement disorders: Parkinson’s disease and Essential tremor, a general perspective2.1 Introduction2.2 Parkinson's disease2.3 Essential tremor2.4 Traditional strategies for assessing progression and treatment of Parkinson's disease and Essential tremor2.5 Advanced strategies for assessing progression and treatment of Parkinson's disease and Essential tremor2.6 Extrapolation to Network Centric Therapy2.7 ConclusionReferences Chapter 3: Traditional ordinal strategies for establishing the severity and status of movement disorders, such as Parkinson's disease and Essential tremor3.1 Introduction3.2 Clinical assessment of Parkinson's disease 3.3 Clinical assessment of Essential tremor3.4 Wearable and wireless systems for the quantification of movement disorder tremor3.5 Extrapolation to Network Centric Therapy3.6 ConclusionReferences  Chapter 4: Deep brain stimulation for the treatment of movement disorder regarding Parkinson's disease and Essential tremor with device characterization4.1 Introduction4.2 The foundations of deep brain stimulation4.3 Long term efficacy and the quest to define the mechanisms of deep brain stimulation4.4 Benefit and risk consideration of deep brain stimulation4.5 Target selection for deep brain stimulation 4.5.1 Essential tremor4.5.2 Parkinson's disease4.6 Deep brain stimulation system device description 4.6.1 Electrode leads and the implantable pulse generator4.6.2 Battery4.6.3 Electrical signal4.7 Deep brain stimulation system programmer4.8 Attaining the optimal parameter configuration for deep brain stimulation an issue with tuning4.9 Future perspectives for deep brain stimulation 4.10 ConclusionReferences Chapter 5: Surgical procedure for deep brain stimulation implantation and operative phase with post-operative risks5.1 Introduction5.2 General surgical perspective and considerations for the application of deep brain stimulation5.3 Deep brain stimulation operative risks and complications5.4 Post-operative risk regarding energy interaction5.5 Adverse neurological and neuropsychological effects for deep brain stimulation5.6 Operative technique advocated by Allegheny General Hospital5.7 Applied deep brain stimulation programming from by Allegheny General Hospital5.8 ConclusionReferences Chapter 6: Preliminary wearable and locally wireless systems for quantification of Parkinson's disease and Essential tremor characteristics6.1 Introduction6.2 Preliminary applications for accelerometers quantifying Parkinson’s disease6.3 Wireless accelerometer feedback for optimal tuning of deep brain stimulation parameter settings, a conceptual perspective6.4 Preliminary demonstration of wearable and wireless accelerometer systems for quantifying Parkinson's disease tremor6.5 Evolution of wearable and wireless systems, from local wireless connectivity to Internet connectivity6.6 ConclusionReferences  Chapter 7: Wearable and wireless systems with Internet connectivity for quantification of Parkinson's disease and Essential tremor characteristics7.1 Introduction7.2 Smartphone for quantifying Parkinson's disease hand tremor7.3 Smartphone for quantification of Essential tremor regarding deep brain stimulation in ‘On’ and ‘Off’ mode7.4 Smartwatches, Bluetooth wireless connectivity, and other wearable and wireless systems for the quantification of movement disorder status7.5 Network Centric Therapy7.6 ConclusionReferences Chapter 8: Role of machine learning for classification of movement disorder and deep brain stimulation status8.1 Introduction8.2 Waikato Environment for Knowledge Analysis (WEKA) for machine learning classification of movement disorder ameliorated through deep brain stimulation using wearable and wireless systems for quantified feedback8.2.1 J48 decision tree8.2.2 K-nearest neighbors 8.2.3 Logistic regression8.2.4 Support vector machine 8.2.5 Multilayer perceptron neural network8.2.6 Random forest8.2.7 Attribute-Relation File Format (ARFF) 8.3 The role of machine learning for Network Centric Therapy8.4 Conclusion References  Chapter 9: Assessment of machine learning classification strategies for the differentiation of deep brain stimulation ‘On’ and ‘Off’ status for Parkinson’s disease9.1 Introduction 9.2 Background9.3 Method and materials9.4 Results and discussion 9.5 Network Centric Therapy integrating wearable and wireless systems as quantified feedback for deep brain stimulation using machine learning classification9.6 ConclusionReferences  Chapter 10: New perspectives for Network Centric Therapy for the treatment of Parkinson's disease and Essential tremor