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CALIFORNIA STATE UNIVERSITY, NORTHRIDGE IMPLEMENTATION OF CLOUD-BASED IOMT ENABLED PLATFORM FOR PEDIATRIC VENTRICULAR ASSIST DEVICES A thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in Electrical Engineering by Meharaj Khanam May 2018 The thesis of Meharaj Khanam is approved: Prof. Ashley Geng. Date Prof. John Valdovinos. Date Prof. Shahnam Mirzaei, Chair Date California State University, Northridge ii DEDICATION I would like to thank everyone who helped me to be there, where I am now in my life. iii ACKNOWLEDGMENT I would like to thank my committee members who supported my efforts and gave me a chance to work on this innovative project To my chair, Prof. Shahnam Mirzaei, To Prof. John Valdovinos, To Prof. Ashley Geng iv TABLE OF CONTENTS Signature Page ii Dedication iii Acknowledgment iv List of Tables vii Abstract viii CHAPTER I – INTRODUCTION 1 1.1 - Statement of the Problem 1 1.2 - Purpose 1 1.3 – Research Analysis 2 1.4 – Report Organization 4 CHAPTER II – REVIEW OF LITERATURE 5 2.1 – Overview 5 2.2 – Evolution of Ventricular Assistive Devices 5 2.3 – VAD Complications 7 2.4 – Biomedical Applications and health monitoring 8 CHAPTER III – METHODOLOGY 11 3.1 – Brief Overview of Hardware Components 11 3.1.1 – Monitor 12 3.1.2 – LVAD Pump 12 3.1.3 – LVAD Controller 13 3.1.4 – Pressure Transducer 13 3.1.5 – Pressure and flow sensors 14 3.2 - Brief Overview of Hardware Components 16 3.2.1 – R-Studio 16 3.2.2 – Internet of Things Basics 16 3.2.3 – Amazon Web Services 17 3.2.4 – Amazon Dynamodb 18 3.2.5 – Amazon Simple Storage Service(S3) 18 3.2.6 – Amazon Quicksight 20 3.2.7 – Amazon Simple Notification Service 20 CHAPTER IV – DESIGN IMPLEMENTATION 22 4.1 – Introduction 22 4.2 – Pump Setup 23 4.3 – Data Extraction 24 4.4 – Data Preparation 24 4.5 – Cloud Deployment 25 4.6 – Data Visualization 27 4.7 – Subscription and Notification 29 v CHAPTER V – CONCLUSION 31 5.1 - Conclusion 31 5.2 – Future Work 32 REFERENCES 33 APPENDIX 36 vi LIST OF FIGURES Figure 1: Architecture of the Implementation Figure 2: Generation of Ventricular Assistive Devices Figure 3: Graphical Analysis for Thrombosis Figure 4: Biomedical Applications of IoT Figure 5: Components of Ventricular Assist System Figure 6: LVAD Monitor Display Figure 7: LVAD Controller Figure 8: Data Acquisition System Figure 9: Flow Sensor Figure 10: Design Flow Figure 11: Pump Setup Mimicking Heart Figure 12: Pressure Calibration in LabChart Figure 13: R Analysis Figure 14: Running Scripts through Command Line Figure 15: Successful Deployment into Dynamodb Figure 16: Graph Showing Pressure Difference versus Flow rate Figure 17: Graph showing the variation of inlet and outlet pressure by time Figure 18: Graph showing Pump Outlet pressure with respect to time Figure 19: Graph showing Pump Outlet pressure with respect to Pump Inlet Pressure Figure 20: Topic Subscribers Figure 21: Email Notification received Figure 22: Email Notification received vii ABSTRACT IMPLEMENTATION OF CLOUD-BASED IOMT ENABLED PLATFORM FOR PEDIATRIC VENTRICULAR ASSIST DEVICES by Meharaj Khanam Master of Science in Electrical Engineering Healthcare is a basic human right and a privilege that aims to maintain better health and fitness. Smart medical devices help in maintaining better health since they make people aware about being fit and healthy thereby improving the human life style and their life time. In this innovative era, technologies are not still and the concept of the Internet of Medical Things introduces many new possibilities like smart sensing devices and solutions for user applications to gain knowledge making human life better. Among the innovative suppliers, the biomedical industry is a fast-growing field that is continuously developing new applications to improve human lives. However, there are also many pros and cons that come into picture while using these smart applications like the side effects and risks at various stages of development or implantation of such devices. This project mainly emphasizes on the patients with the cardiac insufficiency that leads to the implantation of Left Ventricular Assistive devices(LVAD) which helps the heart in its pump function. Although this technology has highly improved during the last few years, it still has also been limited by complications occurring after the transplantation which is life- threatening if it is not treated. If the pump thrombosis [1] or any clotting in the blood is viii detected late, an immediate treatment or a pump exchange are required. However, an early detection means that the patient can be treated immediately with anticoagulants thus avoiding dangerous interventions. Currently, patients with implanted LVAD are clinically inspected at various intervals that are long and lead to a late detection of the pump thrombosis or any other anomaly which leads to a deathly outcome. Therefore, a new approach is needed that allows the patients and the medical professionals to inspect the LVAD on a daily routine. It proposes a design in which the data analyzed is from the Left Ventricular Assist Device(LVAD) attached to the heart. Data like the pressure and the flow rate of the blood is acquired through a data acquisition system in LabChart and then imported into RStudio to clean and perform exploratory analysis in R. This excel data is then pushed to the cloud, stored in the AWS Dynamodb and S3 using python. A custom Dashboard with different graphs of the parameters is developed using Amazon Quicksight and the link is shared with the subscribed users (doctors and patients) to visualize. The link is shared using Amazon Simple Notification Service(SNS) to detect if there is an anomaly in the data like an increase in pressure, the flow rate of the blood and immediate treatment can be given in the emergency. ix CHAPTER I INTRODUCTION 1.1 Statement of the Problem Heart transplantation these days is a common treatment for end-stage heart failure in all ages. Many devices, mechanical and electrical are used to maintain the function of failing heart. This Cardiac insufficiency leads to an implantation of Left ventricular assist device (LVAD) which pumps the blood to the heart replacing its failing functionality. However, it is also limited by complications occurring after the transplantation is done which is life-threatening if it is not treated. If it is detected late, an aggressive lysis or a pump exchange are required. However, an early detection means that the patient can be treated and saved. 1.2 Purpose This proposed study is to investigate and implement a cloud-based platform that enables wireless transfer, storage, and visualization of clinical data. This platform helps to comprehend what IoT is, analysis of data for development in the telemedicine sector [2] and implementing an application for preventing dangerous conditions like strokes, where information to be stored in a secure manner, safe from any unauthorized parties and is accessible to medical professionals, family members from anywhere, on any platform whether it be a computer or a smartphone. This platform undoubtedly generates an enormous amount of clinical data every second which would help the healthcare industry and patients in many ways. 1 1.3 Research Analysis In this report, an effective monitoring dashboard for the Internet of Medical Things environment has been implemented to prevail over the past issues. The main intention of this project was to use the available sensors and the new technology to breakthrough in LVAD research providing advanced solutions to the users. It focusses on the implementation of the data analysis platform using the available resources of Amazon Web Services for sharing and monitoring the pump characteristics to prevent any serious problems due to the implantation of the LVADs. The research was analyzed using a mock circulatory loop of PVAD pump, sensors, data Acquisition system and bridging circuits. The architecture of the implementation is shown in Figure 1. The PVAD pump is placed in the abdomen below the diaphragm and connects to the left ventricle and gives feedback to the aorta. The flow control and pressure from the pump are read into LabChart using a data acquisition system for the flow rate of the blood and two bridge rectifiers are used one each for the pressure of the pump i.e. pump inlet pressure and pump outlet pressure. The data acquisition system transfers the raw data into the LabChart as a voltage with units L/mm. This voltage is then calibrated into the correct units which are L/min for the flow signals and mmHg for the pressure transducers using a two-point user-defined conversion technique for each channel using AD Instruments pressure gauge kit. This raw data of pressure and the flow rate is then live recorded with respect to time and extracted into excel sheet which is then imported in RStudio for Data Preparation and Data 2 cleaning using inbuilt libraries. Resource section like what AWS resources to use is done to implement the project simple and inexpensive in accordance with the amount of data collected from the LVAD. This Cleaned Data is then deployed into the cloud Database Dynamodb and is also stored in cloud storage S3 which is then imported into a cloud Visualization Service Amazon Quicksight where a dashboard is designed to visualize different graphs showing the change in inlet and outlet pressure with respect to time and pressure, difference of pressure with respect to flow rate etc. Figure 1: Architecture of the Implementation Amazon Simple Notification Service(SNS) is then used for creating a topic to share the link to Amazon Quicksight dashboard to the subscribed users like the patient’s family and Medical professionals.