Smart Healthcare System Design E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Sprache: Englisch
SMART HEALTHCARE SYSTEM DESIGN
This book deeply discusses the major challenges and issues for security and privacy aspects of smart health-care systems.
The Internet-of-Things (IoT) has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface and, since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security.
The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications, and to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions. The 14 separate chapters address various aspects of the IoT system, such as design challenges, theory, various protocols, implementation issues, as well as several case studies.
Smart Healthcare System Design covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus is on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations.
Audience: Researchers and industry engineers in information technology, artificial intelligence, cyber security, as well as designers of healthcare systems, will find this book very valuable.
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Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title Page
Copyright
Dedication
Preface
Acknowledgments
1 Machine Learning Technologies in IoT EEG-Based Healthcare Prediction
1.1 Introduction
1.2 Related Works
1.3 Problem Definition
1.4 Research Methodology
1.5 Result and Discussion
1.6 Conclusion
2 Smart Health Application for Remote Tracking of Ambulatory Patients
2.1 Introduction
2.2 Literature Work
2.3 Smart Computing for Smart Health for Ambulatory Patients
2.4 Challenges With Smart Health
2.5 Security Threats
2.6 Applications of Fuzzy Set Theory in Healthcare and Medical Problems
2.7 Conclusion
3 Data-Driven Decision Making in IoT Healthcare Systems—COVID-19: A Case Study
3.1 Introduction
3.2 Experimental Analysis
3.3 Multi-Criteria Decision Making (MCDM) Procedure
3.4 Conclusion
4 Touch and Voice-Assisted Multilingual Communication Prototype for ICU Patients Specific to COVID-19
4.1 Introduction and Motivation
4.2 Proposed Prototype of Touch and Voice-Assisted Multilingual Communication
4.3 A Sample Case Study
4.4 Conclusion
5 Cloud-Assisted IoT System for Epidemic Disease Detection and Spread Monitoring
5.1 Introduction
5.2 Background & Related Works
5.3 Proposed Model
5.4 Methodology
5.5 Performance Analysis
5.6 Future Research Direction
5.7 Conclusion
6 Impact of Healthcare 4.0 Technologies for Future Capacity Building to Control Epidemic Diseases
6.1 Introduction
6.2 Background and Related Works
6.3 System Design and Architecture
6.4 Methodology
6.5 Performance Analysis
6.6 Future Research Direction
6.7 Conclusion
7 Security and Privacy of IoT Devices in Healthcare Systems
7.1 Introduction
7.2 Background and Related Works
7.3 Proposed System Design and Architecture
7.4 Methodology
7.5 Performance Analysis
7.6 Future Research Direction
7.7 Conclusion
8 An IoT-Based Diet Monitoring Healthcare System for Women
8.1 Introduction
8.2 Background
8.3 Necessity of Wearable Approach?
8.4 Different Approaches for Wearable Sensing
8.5 Description of the Methodology
8.6 Description of Various Components Used
8.7 Strategy of Communication for Wearable Systems
8.8 Conclusion
9 A Secure Framework for Protecting Clinical Data in Medical IoT Environment
9.1 Introduction
9.2 Medical IoT Application Domains
9.3 Medical IoT Concerns
9.4 Need for Security in Medical IoT
9.5 Components for Enhancing Data Security in Medical IoT
9.6 Vulnerabilities in Medical IoT Environment
9.7 Solutions for IoT Healthcare Cyber-Security
9.8 Execution of Trusted Environment
9.9 Patient Registration Using Medical IoT Devices
9.10 Trusted Communication Using Block Chain
9.11 Conclusion
10 Efficient Data Transmission and Remote Monitoring System for IoT Applications
10.1 Introduction
10.2 Network Configuration
10.3 Data Filtering and Predicting Processes
10.4 Experimental Setup
10.5 Conclusion
11 IoT in the Current Times and its Prospective Advancements
11.1 Introduction
11.2 How IIoT Advances Industrial Engineering in Industry 4.0 Era
11.3 IoT and its Current Applications
11.4 Application Areas of IIoT
11.5 Challenges of Existing Systems
11.6 Future Advancements
11.7 Case Study of DeWalt
11.8 Conclusion
12 Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0
12.1 Introduction to Artificial Intelligence
12.2 AI and its Related Fields
12.3 What is Industry 4.0?
12.4 Industrial Revolutions
12.5 Reasons for Shifting Towards Industry 4.0
12.6 Role of AI in Industry 4.0
12.7 Role of ML in Industry 4.0
12.8 Role of Deep Learning in Industry 4.0
12.9 Applications of AI, ML, and DL in Industry 4.0
12.10 Challenges
12.11 Top Companies That Use AI to Augment Manufacturing Processes in the Era of Industry 4.0
12.12 Conclusion
13 The Implementation of AI and AI-Empowered Imaging Systems to Fight Against COVID-19—A Review
13.1 Introduction
13.2 AI-Assisted Methods
13.3 Optimistic Treatments and Cures
13.4 Challenges and Future Research Issues
13.5 Conclusion
14 Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19
14.1 Introduction
14.2 Data Analysis
14.3 Methodology
14.4 Results and Discussions
14.5 Conclusions
Index
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Data mining features.
Figure 1.2 Data mining classification process.
Figure 1.3 Block diagram of the EEG classification.
Figure 1.4 Working model of IoT-based Smart Healthcare kit.
Figure 1.5 Proposed block diagram.
Figure 1.6 Mindwave sensor.
Figure 1.7 Home pages for EEG signal design.
Figure 1.8 Pseudocode for proposed EEG prediction system.
Figure 1.9 Graph between TPR vs TFR.
Figure 1.10 Output result accuracy predictions for based on patient EEG data.
Figure 1.11 Accuracy predictions for based on type of epilepsy.
Chapter 2
Figure 2.1 Technical Scenario 2, normal healthcare.
Figure 2.2 Structure of transformation research. Visits to the study occur at 2,...
Figure 2.3 The no. of patients visits after Post Discharge [36].
Chapter 3
Figure 3.1 Overall design.
Figure 3.2 Kernel Function on non-linear SVM.
Figure 3.3 Working of random forest algorithm.
Chapter 4
Figure 4.1 Home view of touch interaction.
Figure 4.2 Cross language selection display for both doctor and patient.
Figure 4.3 Categorical interactions initiated by doctor.
Figure 4.4 Categorical interactions initiated by patient.
Figure 4.5 A sample interactive interface in English.
Figure 4.6 Figure 4.5’s interactions in Tamil.
Figure 4.7 A sample interactive interface in English and Tamil.
Figure 4.8 A sample case study for native language selection for patient and doc...
Figure 4.9 Doctor interaction view of his/her language.
Figure 4.10 Patient receives doctor’s interaction in native voice & reply throug...
Chapter 5
Figure 5.1 Framework of proposed system.
Figure 5.2 Flowchart of the proposed model.
Figure 5.3 Snapshot of the home screen on the website.
Figure 5.4 Snapshot of the ‘Registration Details’ page on the website.
Figure 5.5 Snapshot of the ‘Patient’s Test Details’ page on the website.
Figure 5.6 Snapshot of the ‘Sensor Readings’ page of a patient on the website.
Figure 5.7 Snapshots of the screen of the phone application showing the differen...
Figure 5.8 Snapshot from the phone application showing the Pulse Rate reading of...
Chapter 6
Figure 6.1 General methodology of our proposed model.
Figure 6.2 Algorithm of proposed framework.
Chapter 7
Figure 7.1 Proposed system design and architecture model.
Figure 7.2 Login window for Doctor/Nurse/Health official login from computer ter...
Figure 7.3 Login window for Doctor/Nurse/Health official login from Android devi...
Figure 7.4 Patient monitoring window from computer terminal.
Figure 7.5 Patient monitoring window from Android tablet.
Figure 7.6 Flowchart for patient clearance.
Figure 7.7 Flowchart for relative clearance.
Figure 7.8 Flowchart for pharmacy services clearance.
Figure 7.9 Flowchart for researcher clearance.
Figure 7.10 Flowchart for emergency services clearance.
Figure 7.11 Flowchart for Doctor clearance.
Figure 7.12 UI of unsuccessful login authentication.
Figure 7.13 UI of unsuccessful OTP/MFA authentication.
Chapter 8
Figure 8.1 Important physiologic roles of non-essential amino acids (NEAA) in hu...
Figure 8.2 Overall block diagram of IOT-based health monitoring system.
Chapter 9
Figure 9.1 Body area network [18].
Figure 9.2 Various interconnection protocols in neighborhood area network.
Figure 9.3 Various IoT networking devices.
Figure 9.4 Smart medical network.
Figure 9.5 Level of data security.
Figure 9.6 Security constraints in medical IoT.
Figure 9.7 Layers of healthcare security.
Figure 9.8 Root of trust.
Figure 9.9 Chain of trust.
Figure 9.10 Architecture diagram of smart healthcare.
Figure 9.11 Blockchain based IoT smart healtcare.
Chapter 10
Figure 10.1 Overview of system architecture.
Figure 10.2 Schematic diagram of the proposed system.
Figure 10.3 An example of MQTT protocol use-case with different topics and clien...
Figure 10.4 Quality of service (0) of MQTT.
Figure 10.5 Quality of service (1) of MQTT.
Figure 10.6 Quality of service (2) of MQTT.
Figure 10.7 Raspberry Pi board (model RasPi B).
Figure 10.8 Custard Pi 3A.
Figure 10.9 Huba type 663 differential pressure transmitter.
Figure 10.10 Flow chart of the publisher algorithm.
Figure 10.11 Flow chart of the subscriber algorithm.
Figure 10.12 Remote monitoring system for gas turbine engine using IoT.
Figure 10.13 The experimental setup.
Figure 10.14 Data from sensor 1.
Figure 10.15 Data from sensor 2.
Figure 10.16 Data from sensor 3.
Figure 10.17 Data from sensor 4.
Figure 10.18 Data from sensor 5.
Figure 10.19 Data from sensor 6.
Figure 10.20 Total energy consumed by each sensor with and without the proposed ...
Figure 10.21 The overall energy consumption in the system with and without the p...
Figure 10.22 Total bytes transmitted by RasPi with and without the proposed algo...
Figure 10.23 Data storage with and without the proposed algorithm.
Figure 10.24 Traceroute command in Linux.
Figure 10.25 Traceroute command in Windows.
Figure 10.26 Total number of transmitted bytes from the RasPi to the monitoring ...
Chapter 12
Figure 12.1 Views of AI [10].
Figure 12.2 Different types of artificial intelligence.
Figure 12.3 Intelligent agent.
Figure 12.4 Various disciplines of AI [15, 16].
Figure 12.5 The relation between AI, ML, and DL.
Figure 12.6 Industry 4.0 [19].
Figure 12.7 Types of industrial revolutions.
Figure 12.8 Role of ML in Industry 4.0.
Figure 12.9 Challenges in Industry 4.0 [29, 30].
Figure 12.10 Other Industries that adopt new trends.
Chapter 13
Figure 13.1 Preliminary screening of COVID-19 patients.
Chapter 14
Figure 14.1 Flow chart—symptoms of COVID-19 and predictive measures.
Figure 14.2 Linear regression model.
Figure 14.3 Adequacy model.
Figure 14.4 Stationarity analysis.
Figure 14.5 Auto correlation functional values—time and lag.
Figure 14.6 Partial autocorrelation.
Figure 14.7 Time plot of daily COVID-19 new cases in the world during 22-Jan 22-...
Figure 14.8 Time plot of daily COVID-19 new deaths in the world during 22-Jan-20...
Figure 14.9 Plots of ACF values for daily COVID-19-new cases in the world.
Figure 14.10 Plots of PACF values for daily COVID-19 new cases in the world.
Figure 14.11 Plots of ACF values for daily COVID-19 deaths in the world.
Figure 14.12 Plots of PACF values for daily COVID-19 new new deaths in the world...
Figure 14.13 Time plot of new cases across the globe during 22 Jan-2020 to 31-Ma...
Figure 14.14 Time plot of second differencing for new cases and deaths in the wo...
Figure 14.15 Plots of ACF values of second differencing of daily new cases in th...
Figure 14.16 Plots of PACF values of second differencing of daily new cases in t...
Figure 14.17 Plots of ACF values of second differencing of daily new deaths in t...
Figure 14.18 Plots of PACF values of second differencing of daily new deaths in ...
Figure 14.19 Plotting of ACF and PACF values for estimated residuals of daily ne...
Figure 14.20 Plotting of ACF and PACF values for estimated residuals of daily ne...
Figure 14.21 Normal P–P plot of estimated residual daily new cases.
Figure 14.22 Normal P–P plot of estimated of residuals of daily new deaths.
Figure 14.23 Forecast graph of daily new cases in the world.
Figure 14.24 Forecast graph of daily new deaths in the world.
Figure 14.25 Scatter plot of daily new cases vs daily COVID-19 new deaths.
Figure 14.26 Normal P–P plot of estimated residuals.
List of Tables
Chapter 1
Table 1.1 Defined epileptic state in transient EEG signal.
Table 1.2 Brainwaves frequency characteristics.
Table 1.3 EEG signal mathematical transform with feature.
Table 1.4 EEG signal seizures proposed HANNSVM system result.
Table 1.5 Initially defined epileptic states.
Table 1.6 Feature sets that resulted in the highest prediction accuracy for pati...
Table 1.7 Feature sets that resulted in the highest prediction accuracy for each...
Chapter 2
Table 2.1 Very recent articles focusing on applications of fuzzy set theory in h...
Table 2.2 Abbreviations with descriptions.
Chapter 3
Table 3.1 Classification results for COVID-19 data set.
Table 3.2 Weights of different criteria.
Table 3.3 Performance scores and rank by SMART.
Table 3.4 Presentation determined by WPM.
Table 3.5 Presentation determines by TOPSIS.
Chapter 9
Table 9.1 Protocols used for interconnection in medical IoT.
Chapter 10
Table 10.1 A list of all parameters used in this study.
Chapter 13
Table 13.1 Country-wise statistical table for affected area.
Table 13.2 AI-assisted diagnosis of COVID-19 and drug discovery.
Chapter 14
Table 14.1 Auto correlation of second differenced daily new cases.
Table 14.2 Partial auto correlation of second differenced daily new cases.
Table 14.3 Autocorrelation with second differenced values are obtained by transf...
Table 14.4 Partial auto correlation of second differenced daily new deaths.
Table 14.5 ARIMA models and normalized BIC value for daily new cases in the worl...
Table 14.6 ARIMA models and normalized BIC value for daily COVID-19 new deaths i...
Table 14.7 Model statistics of daily new cases in the world.
Table 14.8 Model statistics of daily new deaths in the globe.
Table 14.9 ARIMA model parameters of daily new cases in the world.
Table 14.10 ARIMA model parameters of daily new deaths in the world.
Table 14.11 Forecasting values of daily new cases in the world (June 2020–August...
Table 14.12 Forecasting values of daily new deaths in the world (June 2020–Augus...
Table 14.13 Statistical measures.
Table 14.14 Coefficients.
Table 14.15 Residuals statistics.
Guide
Cover
Table of Contents
Title page
Copyright
Dedication
Preface
Acknowledgments
Begin Reading
Index
End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Series Editor: Dr. Souvik Pal and Dr. Dac-Nhuong Le
The role of adaptation, learning analytics, computational Intelligence, and data analytics in the field of cloud-IoT systems is becoming increasingly essential and intertwined. The capability of an intelligent system depends on various self-decision-making algorithms in IoT devices. IoT-based smart systems generate a large amount of data (big data) that cannot be processed by traditional data processing algorithms and applications. Hence, this book series involves different computational methods incorporated within the system with the help of analytics reasoning and sense-making in big data, which is centered in the cloud and IoT-enabled environments. The series publishes volumes that are empirical studies, theoretical and numerical analysis, and novel research findings.
Submission to the series:Please send proposals to Dr. Souvik Pal, Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishna Nagar, West Bengal, India.E-mail: [email protected]
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Smart Healthcare System Design
Security and Privacy Aspects
Edited by
SK Hafizul Islam
Department of Computer Science and Engineering, Indian Institute of Information Technology, Kalyani, India
and
Debabrata Samanta
Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2021 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-79168-3
Cover image: Pixabay.ComCover design: Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Dedications
To my parents Mr. Dulal Chandra Samanta and Mrs. Ambujini Samanta, my elder sister Mrs. Tanusree Samanta and daughter Ms. Aditri Samanta
Dr. Debabrata Samanta
To my son Mr. Enayat Rabbi
Dr. SK Hafizul Islam
Preface
The Internet-of-Things (IoT) interconnects humans with uniquely identifiable embedded computing devices within the existing internet infrastructure. It has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface. And since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security. Nowadays, the IoT has received massive attention for applications in different domains, the healthcare sector being one of them. A healthcare system serves society by taking care of its citizens’ physiological and neurological conditions through sensors by amassing information on their current health conditions and passing it along to the healthcare center for necessary actions. Accordingly, physicians can examine these health conditions and take the steps required to prevent the deterioration of the patient’s health. The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions; which is why this book will prove invaluable to professionals who want to increase their understanding of recent challenges in the IoT-enabled health-care domain. The separate chapters herein address various aspects of the IoT system, such as design challenges, theory, various protocols, and implementation issues, and also include several case studies. Furthermore, this book has been designed for both undergraduate students and researchers to easily understand and apply IoT in the healthcare domain.
About the Book
Smart Healthcare System: Security and Privacy Aspects covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus will be on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations. More particularly, this volume consists of 14 chapters contributed by authors well-versed in the subject who are devoted to reporting the latest findings on smart healthcare system design.
Chapter 1 explores a framework that can use real-time electroencephalogram (EEG) signals from multiple channels to predict the occurrence of an epileptic seizure. A selected number of EEG channels are provided as input to the system, and the corresponding epileptic seizure state is recorded at every second. A hybrid artificial neural network with a support vector machine-based classification is created as a simulation of real-time dynamic predictions in this system.
Chapter 2 discusses the critical factors to be considered in mHealth applications, such as mobility awareness, location-based medication, data, distance, and measurement protection for eHealth. Most of the mHealth apps operate with the patient’s background, which involves disease and environmental observation. Many problems face creating these applications, such as protection, smartness, decision-making, application size, and timely actions. This study presents the health sector dilemma by using it fuzzy logic for changes in health. For the health application to enhance well-being, all features addressed in this chapter are imperative.
Chapter 3 includes the design of a decision-making framework that gathers, preprocesses, and analyzes data from IoT-based healthcare systems and produces comprehensive information reports for better diagnosis. It implements data preprocessing methods, such as data washing, munging, normalization, elimination, and noisy data removal. The integration of the IoT with data analytics technologies results in healthcare systems becoming smarter and smarter. In the preliminary stage alone, the collected IoT data, such as pulse rate, temperature, oxygen level, and heart rate from connected devices, can be used to analyze the need and severity using appropriate machine learning techniques. Multi-criteria decision-making (MCDM) strategies, such as SMART, WPM, and TOPSIS, are often used to create comprehensive, insightful diagnostic reports at the conclusion of the development process.
In Chapter 4, the proposed work deals with touch and native voice-assisted prototype design and development to allow intuitive communication and interaction between health professionals and patients affected by severe acute respiratory infection (SARI), who are dependent on a ventilator and admitted for quarantine treatment. It also ensures that the multilingual capacity to communicate effectively in most of the ten Indian languages is established so that patients are relieved of pain, etc., as health professionals answer their queries. Touch-based gesture patterns can be effectively used as an interactive module in this prototype and let doctors frequently track and react to ICU patient inquiries by updating it to easily communicate the patient’s emotions or pains to caregivers. The planned prototype would be made available and public in an open source software repository.
Chapter 5 discusses the critical importance, especially in developing countries, of identifying the cause of a pandemic, such as COVID-19, and monitoring the spread of the disease. Included in our proposed system presented in this chapter is a network model that incorporates wireless body sensors, wearable devices, and cloud computing to manage patient data in the form of text or images, or cloud voice. To keep track of the real-time data, a cell phone application is installed along with a website.
In Chapter 6, Healthcare 4.0 technologies are adopted so that patients can be tracked remotely for surgical operations. Biosensors are also adopted in handheld gadgets. The proposed framework uses machine learning techniques to analyze the data obtained by the sensors. This method gathers the medical records of patients for review. It is challenging to provide a bed for treatment in the current COVID-19 pandemic situation, especially in developing and highly populated countries. Thus, the proposed Healthcare 4.0 system is designed to move therapies with a high-precision disease detection rate and testing from hospitals to patients’ homes.
Chapter 7 explains why even though smart technology offers several healthcare benefits, the same systems have a more significant effect on both confidentiality and security. Hacks on other frameworks, personal security risks, privacy threats, data eavesdropping, etc., are potential threats. Therefore, together with a cloud server, the framework proposed in this chapter uses the wireless body area network (WBAN) to hold patients’ records and make them available to only the individuals concerned by creating a role-based assignment and least privilege access system. It gathers the medical history of patients for potential reference.
In Chapter 8, the proposed system is a fully automated diet monitoring solution consisting of food quality assessment sensors operated by Wi-Fi and a smart-phone application that collects nutrition information about food ingredients. The food weighing sensor calculates the food’s weight, which is transmitted to the cloud over the internet via a microcontroller integrated with wireless module synchronization that is included in the monitoring system. To achieve the required nutrient values, two separate approaches are used. The first process is an optical character recognition (OCR) process which tests the nutrient value using the FDA-mandated nutrition facts label. In the other process, the barcode of the food is scanned, and nutritional data is collected from the internet using free application programming interfaces (APIs). Food is thus categorized based on the highest nutritional value, the relationship between the food consumed, and the lack of nutrients.
Chapter 9 discusses the gradually increasing usage of smart devices in various domains, with a particular focus on fusing the IoT into the medical sector to enhance clinical consideration based on the patient. Maintaining the protection of the information generated and obtained by IoT devices is the most severe problem in administering medical services, so the main objective of this chapter is to establish a system for safeguarding the IoT data developed in medical services. Security mechanisms used in the IoT setting must also communicate from end to end and must be adopted by low-cost IoT devices.
Chapter 10 explores why the energy consumption of WSNs and IoT devices is considered to be the aggregation and transmission of data. In processing and transmitting redundant and unnecessary data, these devices waste their power. Therefore, this chapter presents a means of eliminating redundant data and reducing the number of data transmissions, thus reducing the energy consumption of the IoT devices. Also included is an end-user remote monitoring system that monitors and verifies performance during real-time communication of these smart objects.
Chapter 11 explores the stability, data storage, and performance of various IoT devices that reflect the disadvantages of integrating these kinds of tools in the business sector. Data on the cloud server is more often than not compromised, and data storage is inefficient due to the growing number of users and devices on the internet. However, as new ways of using the IoT are taking shape, these disadvantages must be rectified. The world awaits many developments in the coming decades that will gracefully upgrade current systems; for instance, the advent of edge computing will transubstantiate cloud computing by eliminating technicalities while retaining the appropriate use of bandwidth for data privacy. Besides which, the IoT is bound to change industries, healthcare, traffic control, cyber-security, etc. With its success and steady progress, the future of the IoT is auspicious, with the intent of paving a new path for technological growth. This chapter’s focus is on current IoT developments, their drawbacks, and the potential for future advances.
Chapter 12 discusses the use of artificial intelligence (AI) to make machines learn from the environment and make them capable of completing tasks, which helps to optimize their goals. AI, which has subfields such as machine learning, deep learning, and others, is interdisciplinary. Machine learning, which allows computers to automatically learn from their experience, may be achieved with computer programs that access and use them to understand. Deep learning is a subfield of machine learning, which processes or filters knowledge in the same way as the human brain. Here, to predict and classify the content, it uses a computer model that takes the input and filters it through various layers. These areas, such as artificial intelligence, machine learning, and deep learning, have made several developments in technology that have given the world a whole new dimension in each area.
Chapter 13 summarizes the important roles of certain AI-driven techniques (machine learning, deep learning, etc.) and AI-enabled imaging techniques for the study, prediction, and diagnosis of COVID-19 disease. Through social networking knowledge, the combined effort of powerful AI and image processing techniques can predict the initial trend of COVID-19 disease, identifying the most affected areas in each country, and predicting drug-protein interactions for the development of new drug vaccines. AI-empowered X-ray and computed tomography image acquisition and segmentation methods, however, help classify and diagnose patients minimally affected by COVID-19. This chapter also addresses an important set of open problems and future research concerns about AI-empowered COVID-19 handling procedures.
Chapter 14 mainly deals with the design of a machine learning model for the study of the transmission dynamics of COVID-19, a disease which is affecting the entire world. Ventilated patients with extreme acute respiratory distress being treated while quarantined in the ICU often face difficulties with their most basic human interactions, including communication, due to the respiratory disease, language issues or intubation. There are significant physical and psychological consequences to the inability of ICU patients to communicate. Researchers have created various types of software programs, such as Speech-Language Pathologist, in order to provide both health practitioners and caregivers with augmentative and complementary communication assistance.
SK Hafizul IslamDepartment of Computer Science and EngineeringIndian Institute of Information Technology KalyaniWest Bengal, IndiaEmail: [email protected]
Debabrata SamantaDepartment of Computer ScienceCHRIST (Deemed to be University)Bengaluru, KarnatakaEmail: [email protected] 2021
Acknowledgments
It is with great pleasure that we express our sincere gratitude and appreciation for all those who significantly helped in the completion of this book with their contributions and support. We are sincerely thankful to Dr. G. P. Biswas, Professor, Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India, for his encouragement, support, guidance, advice, and suggestions towards the completion of this book. Our sincere thanks to Dr. Siddhartha Bhattacharyya, Professor, Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, Karnataka, India, and Dr. Arup Kumar Pal, Assistant Professor, Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India, for their continuous support, advice and cordial guidance from the very beginning to the completion of this book.
We would also like to express our honest appreciation to our colleagues at the Indian Institute of Information Technology Kalyani, and CHRIST (Deemed to be University), Bengaluru, Karnataka, India, for their guidance and support.
We must also thank the series editors, Dr. Souvik Pal and Dr. Dac Nhuong Le, for accepting our proposal and also for their valuable suggestions for shaping this book.
We also thank all the authors who have contributed chapters to this book, which would not have been possible without their contributions. We are also very thankful to those who reviewed the chapters of the book, whose continuous support and commitment made it possible to complete the chapter reviews on time. We are very grateful to the entire publishing team at Scrivener Publishing, who extended their kind cooperation, timely response, expert comments, and guidance. Finally, we sincerely express our special and heartfelt respect and gratitude to our family members and parents for their endless support and blessings.
1Machine Learning Technologies in IoT EEG-Based Healthcare Prediction
Karthikeyan M.P.1*, Krishnaveni K.2 and Muthumani N.3
1Department of Computer Science, PPG College of Arts and Science,Coimbatore, India
2Department of Computer Science, Sri Ramasamy Naidu Memorial College,Sattur, India
3PPG College of Arts and Science, Coimbatore, India
Abstract
The classification of medical data is the demanding challenge to be addressed among all research issues since it provides a larger business value in any analytics environment. Medical data classification is a mechanism that labels data enabling economical and effective performance in valuable analysis. Proposed research has indicated that the quality of the features may cause a backlash to the classification performance. Also squeezing the classification model with entire raw features can create a bottleneck to the classification performance. Thus, there is necessity for selecting appropriate features for training the classifier. In this proposed, a system is proposed that can use multiple channel real-time EEG signals to predict the onset of an epileptic seizure. The system is given a select number of EEG channels as input and reports back the corresponding epileptic seizure state at every second and the Hybrid Artificial Neural Network with Support Vector Machine (HANNSVM) based classifications are done as a simulation of real-time dynamic predictions and are dependent upon past predictions that were made. As a result, the sensitivity must be controlled such that seizures aren’t predicted more often than they actually occur. Statistical analysis of accuracy values and computational time portrays that the proposed schemes provide compromising results over existent methods.
Keywords: Computer aided diagnosis, K-nearest neighbor, artificial neural network, electroencephalography, Internet of Things, support vector machine, brain modeling feature exraction
1.1 Introduction
IoT (Internet of Things) is utilized as a part of a great deal of medical uses. A portion of the uses of Internet of Things are savvy stopping, shrewd home, brilliant city, keen condition, mechanical spots, horticulture fields and wellbeing observing procedure [38]. One such application in medicinal services to screen the patient’s wellbeing status by means of Internet of Things makes therapeutic gear more effective by permitting ongoing checking of patient’s wellbeing, in which sensor get information of patient’s and decreases the human blunder. The Internet of Things in the therapeutic field draws out the answer for compelling continuous checking of rationally impaired individual at diminished cost and furthermore lessens the exchange off between tolerant result and infection administration [33]. So far we have seen the wellbeing observing framework which gathers data of fundamental parameters, for example, heartbeat, temperature, circulatory strain and development parameters. The medical data stored in cloud in the form of huge dataset, need to analyze and predict the diseases based on IoT data is very important [1, 37].
The progress of science has driven every individual to mine and consume medical data for analyses of business, customer, bank account, medical, etc. made privacy break or intrusion also in most circumstances. The IoT-based medical data is all over in the pattern of text, number, images and videos [35, 36]. This type of data continues to grow bigger, thereby organizing these data as a necessary process. The collected enormous data should produce logical use unless it would be waste of time, effort and storage. The action of grabbing or collection of huge data is called datafication. Clinical data can be used effectively as it is datafied. The organizing of data alone cannot make useful but should identify what can be performed by its use. Optimal processing power, analytical capabilities and skills are needed for squeezing essential information from medical data. The data mining features are shown in Figure 1.1.
Medical data is of various types, formats and shapes which are brought together from various sources. Data Analytics is the action of studying and extracting big data which can yield functional and business knowledge in a remarkable form. The behavior of business is reconstituted in different ways by big data analytics [15]. Approaches like information technology, statistics, quantitative methods and various methods are used by medical analytics to deliver results. Data mining analytics is divided into three main types. They are descriptive analytics, predictive analytics and prescriptive analytics. The traditional database systems are not sufficient to progress huge data characteristics (elements) [2].
Figure 1.1 Data mining features.
1.1.1 Descriptive Analytics
Descriptive analytical type is the best accepted one being the basis for uplifted analytical models. It benefits leaders, researchers, planners, etc. to build a guideline for forthcoming activities by reviewing the database to determine knowledge on current or past medical data proceedings [16]. This model does a detailed review of data to expose particulars like operation costs, cause for false steps and frequency of events. Descriptive analytics assists locating the root cause of the issue. Descriptive analytics also deal with it Proposed modal EEG classification [4].
1.1.2 Analytical Methods
The different analytical methods of data mining are
• Predictive Analysis
• Behavioral Analysis
• Data Interpretation.
1.1.3 Predictive Analysis
The probable questions in predictive analysis are
• In various domains, how does a data utilize the available data for predictive and real time analysis?
• How does a medical data make accuracy from unstructured data?
• How does a business influence unique varieties of data like social media data, sentiment data, multimedia, etc.?
1.1.4 Behavioral Analysis
Behavioral analysis deals with how a business influences complicated data to develop advanced models for
• Motivating results
• Making a medical budget
• Motivating revolution in medical approach
• Cultivating long-term consumer fulfilment.
1.1.5 Data Interpretation
The probable questions in data interpretation are
• What new analyses can be done from the available data?
• Which data should be analyzed for new product innovation?
1.1.6 Classification
Data classification is considered as a critical and challenging problem to be addressed in IoT medical data analytics. Classification is a method of labeling data for better productive usage depending upon necessity [34]. It functions with two paces: first includes learning activity and the second performs classification activity. The required data can be detected and obtained using well-organized classification model. The action of classifying the data using issues and difficulties opened by the data controllers is called data classification. Figure 1.2 shows the paces connected to big data classification [30]. The different paces connected to classification are input data collection, data understanding, data shaping and data mining environment understanding. The success in data classification requires the understanding about design and structure of algorithms. It demonstrates activities such as configuration of huge data, management of big data and the methodology advancement related to classification [17]. The distinguishing parameters which impact the big data controller management and drive to issues in the advancement of learning layouts. Explores the paces associated with the machine learning algorithms and the flow of various phases is demonstrated. The cross validation and early stopping decision methods are applied for solving problems seen in the validation phase [16].
Figure 1.2 Data mining classification process.
Data classification is the task of applying computer vision and machine learning algorithms to extract meaning from a medical data. This could be as simple as assigning a label to the contents of an image, or data it could be as advanced as interpreting the contents of a data and returning a human-readable sentence [18]. Image and signal classification, at the very core, is the task of assigning a label to a data from a pre-defined set of categories. In practice, this means that given an input image, the task is to analyze the image and return a label that categorizes the image. This label is (almost always) from a pre-defined set. Open-ended classification problems are rarely seen when the list of labels is infinite [2].
In this proposed system examine about checking patient’s mind flags and recognizing the status of the patient progressively. To gather the information of cerebrum signals, we are utilizing Neurosky Mindwave Mobile Headset which deals with the EEG innovation. Figure 1.3 demonstrate the proposed system design for EEG classification. It demonstrates the yield result in waveform design [33]. The overall system is given a multi-channel EEG stream in segments of 3 s every second, and a set of features are extracted at each time point and denoted as a sample. These samples are taken every second such that the subsequent window taken overlaps. As a result, the samples collected show a more gradual transition from one epileptic seizure state to the next [19]. A rectangular window is applied to each 3-second segment such that there is minimal distortion in the frequency response (some distortion will be present due to Gibb’s Phenomenon).
Figure 1.3 Block diagram of the EEG classification.
An FIR signal filter is applied to decompose the incoming EEG stream into its respective brain waves. Features are extracted from the incoming data streams starting from the beginning to the end of the EEG such that it simulates a real-time scenario. If a sample is extracted that contains mathematical anomalies resulting in values of NaN, the sample is simply discarded and skipped over. The training data used is 80% of a new random permutation of the entire training set for every classification performed, and the testing data is the sample that was extracted from the current window [30, 31]. Once the classifiers have each made a prediction, a decision fusion algorithm uses a set of rules to come up with an initial prediction. This prediction is given to the state decision neurons, which use a closed-loop algorithm to determine if a state change is necessary. Table 1.1 shows the various epileptic state of transient EEG cerebrum signals received and stored cloud from IoT-based mindset devices [3, 32].
Table 1.1 Defined epileptic state in transient EEG signal.
State
Epileptic state
1
Postictal state
2
Interictal state
3
Preictal state
4
Ictal state
Since the postictal and interictal states have signal characteristics that are similar (both represent nonictal states), it was necessary to place the states next to each other (i.e. States 1 and 2). This way, if State 2 is misclassified as State 1, or vice versa, then the average of several classifications will also be in the range of States 1 and 2. If these states were defined as States 1 and 4, the average of several classifications would result in increased misclassifications of these states as States 2 or 3, which is incorrect [20].
The reminder of paper is organized as follows. Section 1.2, big data medical dataset prediction and its related work, Section 1.3 discussed about Hybrid Hierarchical clustering feature subsets classifier algorithm, Section 1.4 presents proposed system and existing systems experimental results comparison. Finally, Section 1.5 provides the concluding remarks and future scope of the work.
1.2 Related Works
The following chapter discusses the IoT-based machine learning classification techniques in medical field. Internet of Things-based health outweigh structures affair a giant contribution toward enhancement of clinical statistics structures thru automation over events control regarding patients and real-time transmission of medical records. Figure 1.4 shows general IoT healthcare monitoring system. However, digitization of identification, monitoring or power over patients remains a undertaking in bucolic areas regarding Africa, no longer after point out concerning related rule or web connectivity constraints defined by Arefin et al. [5].
Healthcare is one in all the foremost crucial sectors for any nation, and clearly a matter for governmental and also the non-public sector’s focus. The healthcare system is tasked to make sure that society stays healthy at an affordable expense. Roibu Crucianu et al. [6]. The means healthcare organizations square measure managed impacts the skilled growth and satisfaction of doctors, nurses, counselors and alternative healthcare professionals the application of psychological feature computing in early intervention of cancer, targeted antineoplastic drug delivery techniques like nanobots, 3D bio printed organs like covering for effective wound care, and somatic cell therapies, can alter the transition toward value-based personalized drugs [7]. Value-based healthcare have physicians assume the role of healthcare adviser to patients, therefore informing them of the outcomes, the worth of the designation, and also the treatments that square measure best prescribed for up the standard of life. Data analytics can play a large role in shaping healthcare organizations and their money forecasting. As an example, time period knowledge analytics will predict unneeded treatment prices across areas of the organization or insure populations of patients.
Figure 1.4 Working model of IoT-based Smart Healthcare kit.
Sahu and Sharma [8] have suggested the proposed result regarding the challenge is in conformity with assign good yet environment friendly medical capabilities to patients by way of connecting and gathering data records thru health repute monitors as would include patient’s morale rate, gore pressure and ECG or sends an fortune wary in conformity with patient’s medical doctor together with his present day reputation or complete scientific information.
Rghioui et al. [9] have suggested an emergency scenario in imitation of ship an fortune mail yet tidings after the medical doctor including patient’s current reputation and full clinical data can additionally lie labored on.
The proposed mannequin execute also can be deployed as much a mobile app then that the mannequin becomes extra mobile and effortless in conformity with access somewhere across the globe.
Predictive modeling can play a key role in victimization giant sets of population health records to spot the risks of an unwellness, therefore serving to doctors exclude unneeded treatments that square measure possible to cut back the standard of lifetime of patients, or haven’t any result the least bit [10]. Gope and Hwang [14] and Satija [15] analyze defects can occur beside malformation, injury, and disease. The quantity on deprivation fast is composite according to the celerity of damage. Brain malformations may end result into undeveloped areas, odd growth, and incorrect Genius share in hemispheres yet lobes, in total, 24 EEG datasets containing both ictal and interictal data were provided for analysis. These 24 sets can be further subdivided into 6-channel and 32-channel sets. The scheme of the locations of surface electrodes used is based on the standard international 10–20 system.
Abualsaud et al. [11] have suggested the comparison of various methods for EEG dataset provided was an example of one severe occurrence of a seizure (possibly atonic–clonic) and the second dataset was an example of a complex partial seizure. In one hemisphere of the brain followed by a generalized seizure several minutes later. Both of these data sets were sampled at 500 Hz. The third and fourth data sets contained several minutes of interictal EEG data as the “baseline”, and were both followed by episodes of ictal activity. These two data sets were sampled at 250 Hz.
1.3 Problem Definition
We have seen the health monitoring system, monitoring the patients by checking the vital parameters such as pulse rate, blood pressure, body temperature, growth parameters, etc. But in this thesis we are introducing EEG to detect abnormalities related to brain via wearable sensors. In this research we are using Neurosky Mind wave sensor in order to read the brain wave signals which runs on EEG technology. These sensors display the output in wave pattern. If the values are critical then it will alert the particular doctor of the patient.
1.4 Research Methodology
In Proposed provision permanency including the according setup along performing Electroencephalography (EEG) then Electromyography (EMG) in conformity with analyzed fearful law feature be able to remain analyzed for longevity, Figure 1.5 shows the proposed EEG prediction block diagram.
Figure 1.5 Proposed block diagram.
1.4.1 Components Used
• Arduino Uno
• Temperature sensor LM35
• Pulse sensor
• EEG sensor
• Bluetooth module HC 05
• Raspberry pi 3
1.4.2 Specifications and Description About Components
1.4.2.1 Arduino
ArduinoIDE is a model stage in view of a moderate-to-implemented equipment and software coding. It comprises of a PCB, which can be programed and software coding environment called ArduinoIDE, which is utilized compose and transmitted the PC coding to the physical board.
1.4.2.2 EEG Sensor—Mindwave Mobile Headset
The EEG Brainwave Starter Kit is the principal proficient EEG headset for home and versatile utilization. Figure 1.6 shows the proposed system mind wave sensor reads the EEG signal. Table 1.2 differentiate the brain wave signal categorization according to the frequency in terms of hertz (δ, α, β) [24].
Figure 1.6 Mindwave sensor.
Table 1.2 Brainwaves frequency characteristics.
With Neurosky ease MindWave Mobile headset and neuro feedback software sensor measures the mind’s electrical action and exchanges the information [21].
In order to make this system accessible to epileptic patients, a small device with this algorithm could be implemented. Six probes, three on the epileptogenic focus, and three on the opposite lobe, would have to be installed on the patient which would input the signals into the processing unit, possibly by a wireless protocol such as bluetooth or WiFi. The processing unit would have to be attached to the body in a discrete manner, such as a belt or something that can be worn at all times [26, 27].
1.4.2.3 Raspberry pi
Now attach the Arduino board in raspberry pi by pressing the ls/dev/tty in command terminal of raspberry pi. We will get a list of devices available. Paste this /dev/ttyACM0 in the code. The values from the Arduino go to Raspberry Pi. These values are send to the cloud To see the uploaded data go to the webpage “health monitoring system website we created” and login into it, you will see the particular details as shown in Figure 1.7 below.
Figure 1.7 Home pages for EEG signal design.
1.4.2.4 Working
We are utilizing Arduino for mix of sensors i.e., Temperature sensor LM35, Pulse sensor, and EEG sensor. Raspberry Pi is incredible instrument for installed designs yet it needs ADC. One more downside is all its IOs are 3.3V level. On the opposite side Arduino is great at detecting the physical world utilizing sensors. To get advantages of both the frameworks one may need to interface them. EEG sensor is associated with Arduino utilizing Bluetooth module HC-05. Here HC-05 go about as ace and EEG sensor as slave [25]. Its fills in as TTL Master/Slave UART convention correspondence. Outlined by Full fastest Bluetooth task with full piconet bolster. It enables us to accomplish the business’ largest amounts of affectability, precision, with least power utilization [28].
Here we are using cloud of smart bridge to store the data. The data which is collected from the sensors is send to the cloud of domain smart bridge and sub domain health monitoring system through API. The patient can view his health details after logging-in. In this research we are using pulse sensor to know the patient heartbeat, LM35 to know his body temperature and EEG sensors to know his brain signals. So after login he will get a display of readings in tabular form as shown in the figure. In this research, we are using mindwave headset which works on EEG technology. This sensor consists of one main sensor and one reference electrode. This research can be implemented in future by making more sophisticated by expanding the sensors used to read the brain waves. The main working of mindwave mobile headset goes in ThinkGear ASIC module chip. In this research, we are using TGAM chip in the sensor [22].
The EEG Sensor (values of Attention, Meditation), for calculation Range of 1–100 was taken
• Range from 40 to 60 is considered “neutral”.
• Range from 60 to 80 is slightly high, and interpreted as higher over normal.
• Range from 80 to 100 are considered “high”, that mean it is strong indication levels Severe levels.
1.4.3 Cloud Feature Extraction
The most main role in creating an EEG signal classification system is generating mathematical representations and reductions of the input data which allow the input signal to be properly differentiated into its respective classes. These mathematical representations of the signal are, in a sense, a mapping of a multidimensional space (the input signal) into a space of fewer dimensions. This dimensional reduction is known as “feature extraction”. Ultimately, the extracted feature set should preserve only the most important information from the original signal [23].
Table 1.3 EEG signal mathematical transform with feature.
Set
Mathematical transform
Feature number
1
Linear predictive codes taps
1–5
2
Fast Fourier transform statics
6–12
3
Mel frequency cepstral coefficients
13–22
4
Log (FFT) analysis
23–28
5
Phase shift correlation
29–36
6
Hilbert transform statics
37–44
7
Wavelet decomposition
45–55
8
1st, 2nd, 3rd derivatives
56–62
9
1st, 2nd, 3rd derivatives
63–67
10
Auto regressive parameters
68–72
Table 1.3 above describes feature classification for EEG signal. First, a feature set optimization algorithm is presented which is used to do a feature set study to reveal the mathematical transforms that are most useful in predicting the preictal state. After this, a set of algorithms are given that became the framework of the seizure on set prediction system described.
1.4.4 Feature Optimization
In order to find the features with the most potential, an algorithm was implemented to approximate individual feature strength with respect to every other feature [30]. The strength of a feature was determined by the accuracy with which the preictal state was classified as an average of several classifications. Similar to Cross-Validation by Elimination HANNSVM algorithm repartitions the feature set, performs a set of classifications, finds the best feature sets to drop, and then adjusts the feature space to only contain features that improve the accuracy.
1. Evaluate the accuracy of the classification using all N feature sets.
2. Dropping one feature set at a time, repartitions the feature space into N, N − 1 feature subsets and save the accuracy of each sub set at position K in vector P along with the resulting accuracy.
3. Denote the index of P with the maximum accuracy as B, and drop all the features listed in P from B to N from the final feature space.
The resulting feature set P has accuracy similar to the accuracy found at position B in P. Under training and overtraining must still be taken into consideration since it can have an effect on the accuracy of a prediction.
1.4.5 Classification and Validation
The two methods in this section were developed to complement the classification algorithms and enhance their classification potential for noisy dynamical systems that change state over time.
The first method SVM, which is called Cross-Validation by Elimination, is used to classify samples by testing the amount of correlation (determined by the accuracy of classifications) each sample has to every state and then remove classes that are least correlated to improve classification accuracy. The algorithm isolates each of the classes, compares the prediction results, and then makes a final decision based on a function of the independent predictions [23, 29].
Figure 1.8 is represented as EEG signal hybrid artificial neural network with support vector machine based (HANNSVM) classification, block diagram represents brain signal capture from EEG sensor with unit of hertz, artifact removed from the input signal, preprocessed data is segment, then sampled at Hz and a rectangular window function is applied [31]. An FIR filter is applied to the incoming EEG stream to decompose the incoming signals in to their respective brain waves. However, due to time constraints, only the original signals (unfiltered) are tested with the system. Next to extract the information/feature from segmented output signal. Extracted signal applied to the HANNSVM machine learning algorithm [32, 33].
This method puts testing samples that were weakly classified into classes that make accuracy. The second method, State Decision Neurons, Artificial Neural Network (ANN) is used to automatically make decisions about when to transition to the next defined state [34]. This algorithm, when used in conjunction with a set of classifiers, enables the system to make decisions based on previous predictions, a closed-loop system if you will. When there are three or more states to distinguish between in a noisy system, state decision neurons are useful in determining the appropriate moments to transition to another state. Figure 1.9 completes flow of EEG proposed EEG-based classification system.
1.5 Result and Discussion
1.5.1 Result
The proposed methodology is applied by making use of PYTHONIDE on Intel(R) Core(TM) i5-2410M CPU @ 2.30 GHz and 16 GB RAM. The performance evaluation of the researcher’s proposed HCFS-Hierarchical clustering is done on particular medical field disease since it affects lifetime motion inability. The statement of facts relating to EEG data is collected from different unsorted sources in various ways.
Figure 1.8 Pseudocode for proposed EEG prediction system.
A set of experiments were performed to determine characteristics about the response of ictal EEG data to several mathematical techniques. Finally, an emulator of a system that could take in a multi-channel EEG stream and return seizure status indicators was created.
