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El. knyga: Healthcare Monitoring and Data Analysis using IoT: Technologies and applications

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Edited by (QUEST University, Software Engineering Department, Nawabshah, Pakistan), Edited by (Lord Buddha Education Foundation (LBEF), Information Technology Department, Nepal), Edited by (Sharda University, Department of Computer Science and Engineering, India), Edited by
  • Formatas: EPUB+DRM
  • Serija: Healthcare Technologies
  • Išleidimo metai: 06-Apr-2022
  • Leidėjas: Institution of Engineering and Technology
  • Kalba: eng
  • ISBN-13: 9781839534386
Kitos knygos pagal šią temą:
Healthcare Monitoring and Data Analysis using IoT: Technologies and applicationsDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Serija: Healthcare Technologies
  • Išleidimo metai: 06-Apr-2022
  • Leidėjas: Institution of Engineering and Technology
  • Kalba: eng
  • ISBN-13: 9781839534386
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IoT-enabled healthcare technologies can be used for remote health monitoring, rehabilitation assessment and assisted ambient living. Healthcare analytics can be applied to the data gathered from these different areas to improve healthcare outcomes by providing clinicians with real-world, real-time data so they can more easily support and advise their patients.

The book explores the application of AI systems to analyse patient data and guide interventions. IoT-based monitoring systems and their security challenges are also discussed.

The book is designed to be a reference for healthcare informatics researchers, developers, practitioners, and people who are interested in the personalised healthcare sector. The book will be a valuable reference tool for those who identify and develop methodologies, frameworks, tools, and applications for working with medical big data and researchers in computer engineering, healthcare electronics, device design and related fields.



This edited book covers big data analysis methods of patient data gained via IoT-enabled monitoring systems. The information gathered can be processed to aid clinicians with diagnoses, prognoses and interventions. This book is a great reference to those using, designing, modelling and analysing intelligent healthcare services.

About the editors xv
Preface xvii
Acknowledgments xxi
1 COVID-19 pandemic analysis using application of AI
1(16)
Pawan Whig
Rahul Reddy Nadikattu
Arun Velu
1.1 Introduction
1(2)
1.2 Literature survey
3(2)
1.3 Dataset used for analysis
5(1)
1.4 Various machine learning libraries
5(3)
1.4.1 NumPy
6(1)
1.4.2 SciPy
6(1)
1.4.3 Scikit-learn
1.4.4 Theano
7(1)
1.4.5 TensorFlow
7(1)
1.4.6 Keras
7(1)
1.4.7 Py Torch
7(1)
1.4.8 Pandas
7(1)
1.4.9 Matplotlib
7(1)
1.5 Training and testing
8(1)
1.6 Bias and variance
9(1)
1.7 Result
10(2)
1.8 Conclusion
12(5)
References
13(4)
2 M-health: a revolution due to technology in healthcare sector
17(20)
Mayuri Diwakar Kulkarni
Ashish Suresh Awate
Jyotir Moy Chatterje
2.1 Introduction
18(9)
2.1.1 History of m-health
18(1)
2.1.2 What is m-health?
19(3)
2.1.3 Adoption of m-health by various countries
22(2)
2.1.4 Role of IoT in m-health
24(3)
2.1.5 M-health to maintain social distancing
27(1)
2.2 Discussion
27(6)
2.2.1 M-health to maintain social distancing
27(1)
2.2.2 Impact of m-health during COVID-19
27(2)
2.2.3 Global government initiatives on e-health and m-health
29(1)
2.2.4 Applications of m-health in monitoring health
30(2)
2.2.5 Benefits of m-health technology
32(1)
2.2.6 Barriers to m-health
32(1)
2.2.7 Challenges for m-health technology
32(1)
2.2.8 Future of m-health
33(1)
2.3 Conclusion and future work
33(4)
References
33(4)
3 Analysis of Big Data in electroencephalography (EEC)
37(20)
Sagar Motdhare
Garima Mathur
Ravi Kant
3.1 Introduction
37(2)
3.2 Methodology
39(1)
3.3 EEG signal recording
40(2)
3.4 Activity/action of EEG
42(1)
3.5 EEG applications
42(1)
3.6 Mathematical model
43(2)
3.7 Across the boundaries of small sample sizes
45(1)
3.8 EEG signal analytics and seizure analysis
45(2)
3.9 EEG digital video
47(2)
3.10 EEG data storage and its management
49(1)
3.11 Big Data in epileptic EEG analysis
50(1)
3.12 Conclusion
51(1)
3.13 Future scope
52(5)
References
53(4)
4 An analytical study of COVID-19 outbreak
57(14)
Shipra Gupta
Vijay Kumar
P. Patil
Lajwanti Kishnani
4.1 Introduction
58(1)
4.2 Review of literature
59(1)
4.2.1 The history of identification and spreading in the world
59(1)
4.3 Method
60(1)
4.4 Results
60(2)
4.5 Discussions
62(3)
4.6 Precautions
65(1)
4.7 Conclusions and future scope
66(5)
Acknowledgment
66(1)
References
66(5)
5 IoT-based smart healthcare monitoring system
71(28)
Hakan Ytiksel
5.1 Introduction
72(4)
5.2 Related work
76(3)
5.3 Proposed method
79(10)
5.3.1 Hardware
80(4)
5.3.2 Software
84(1)
5.3.3 ThingSpeak: an IoT web service
85(1)
5.3.4 Structure and working principle of the system
86(3)
5.4 Result and discussion
89(5)
5.4.1 Room temperature
89(1)
5.4.2 Humidity
90(1)
5.4.3 Body temperature
90(1)
5.4.4 Heart rate
91(1)
5.4.5 Oxygen saturation
92(2)
5.5 Conclusion and future scope
94(5)
References
94(5)
6 Development of a secured IoMT device with prioritized medical information for tracking and monitoring COVID patients in rural areas
99(34)
P.K. Jawahar
K. Indragandhi
G. Kannan
Yiu-Wing Leung
6.1 Introduction
99(9)
6.1.1 Different versions of IoT
101(1)
6.1.2 IoMT architecture and framework
102(3)
6.1.3 Technologies
105(1)
6.1.4 Sensors used in IoMT
106(2)
6.2 Security threats in IoMT
108(5)
6.3 Introduction to COVID-19
113(3)
6.3.1 Implementation of blockchain in IoMT systems
115(1)
6.4 Proposed system architecture
116(10)
6.4.1 IoMT device
117(4)
6.4.2 Results and discussion
121(5)
6.5 Conclusion and future scope
126(7)
References
127(6)
7 An IoT-based system for a volumetric estimation of human brain morphological features from magnetic resonance images
133(16)
S.N. Kumar
A. Lenin Fred
L.R. Jonisha Miriam
H. Ajay Kumar
I. Christina Jane
Parasuraman Padmanabhan
Baldzs Gulyds
7.1 Introduction
134(1)
7.2 Materials and methods
135(3)
7.2.1 Improved fuzzy C-means (FCM) clustering algorithm for the extraction of ROI
136(2)
7.2.2 IoT-based system for the extraction of ROI
138(1)
7.3 Results and discussion
138(7)
7.4 Conclusion and future scope
145(4)
Acknowledgments
145(1)
References
146(3)
8 Healthcare monitoring through IoT: security challenges and privacy issues
149(18)
S.O. Owoeye
A.S. Akinade
K.I. Adenuga
F.O. Durodola
8.1 Introduction
150(3)
8.2 IoT applications in personalized healthcare
153(3)
8.2.1 In-clinic care
153(1)
8.2.2 Remote monitoring
154(1)
8.2.3 Blood pressure monitoring
155(1)
8.2.4 Rehabilitation system
155(1)
8.2.5 Oxygen saturation monitoring
155(1)
8.2.6 Wheelchair management
155(1)
8.2.7 Healthcare solutions using smartphones
155(1)
8.3 Challenges of IoT in personalized healthcare
156(1)
8.4 Security of IoT in personalized healthcare
157(3)
8.4.1 The inherited security challenges in the IoT
157(2)
8.4.2 IoT new security challenges
159(1)
8.4.3 IoT security requirements
160(1)
8.5 Privacy
160(3)
8.5.1 Consent
162(1)
8.6 Conclusion and future scope
163(4)
References
163(4)
9 E-health natural language processing
167(14)
Saman Hina
Raheela Asif
Pardeep Kumar
9.1 Unstructured datasets for E-health NLP research
168(2)
9.2 Annotation challenges dealing with health-care corpora
170(2)
9.2.1 Semiautomatic approach for the development of gold standard corpus of medical narratives
171(1)
9.3 NLP methods that can be adopted to tackle semantics for medical text analysis
172(3)
9.3.1 Rule-based methods
172(2)
9.3.2 Machine learning methods
174(1)
9.4 E-health and Internet of Things (IoT)
175(1)
9.5 Contributions required from NLP researchers in health-care applications
176(1)
9.6 Conclusion and future work
177(4)
References
178(3)
10 Blockchain of things for healthcare asset management
181(18)
Sajid Nazir
Mohammad Kaleem
Hassan Hamdoun
Jafar Alzubi
Hua Tianfield
10.1 Introduction
182(1)
10.2 Healthcare asset management
183(1)
10.3 Challenges and opportunities in healthcare
184(2)
10.3.1 Health and safety
184(1)
10.3.2 Regulatory requirements
185(1)
10.3.3 Data privacy
185(1)
10.3.4 Data security
185(1)
10.3.5 Device security
185(1)
10.3.6 Equipment interoperability
186(1)
10.3.7 Resource constraints
186(1)
10.3.8 Sustainability
186(1)
10.4 Blockchain: concepts and frameworks
186(3)
10.4.1 Block structure
186(1)
10.4.2 Smart contracts
187(1)
10.4.3 Cryptography and distributed ledger technology
187(1)
10.4.4 Consensus protocols
188(1)
10.4.5 Blockchain classification
188(1)
10.4.6 Blockchain frameworks
188(1)
10.5 Blockchain of things architecture for healthcare asset management
189(3)
10.6 Major healthcare application areas
192(2)
10.6.1 Healthcare records
192(1)
10.6.2 Device location management
193(1)
10.6.3 Preventive and predictive analysis
193(1)
10.6.4 Data visualisation
193(1)
10.6.5 Forecasting
193(1)
10.6.6 Assisted living and patient monitoring
194(1)
10.6.7 Healthcare supply chain management
194(1)
10.6.8 Acquiring/processing patient's clinical data
194(1)
10.7 Conclusion and future work
194(5)
References
195(4)
11 Artificial intelligence: practical primer for clinical research in cardiovascular disease
199(12)
Shivendra Dubey
Chetan Gupta
Kalpana Rai
11.1 Artificial intelligence
199(2)
11.2 Traditional statistics versus AI
201(1)
11.3 Representative algorithms of AI
202(2)
11.4 Machine power along with big data
204(2)
11.4.1 Image identification
204(1)
11.4.2 Structured data
205(1)
11.4.3 Unstructured data
205(1)
11.4.4 Medical images
205(1)
11.5 Challenges to implementation
206(1)
11.6 Conclusion and future work
207(4)
References
207(4)
12 Deep data analysis for COVID-19 outbreak
211(26)
S.O. Owoeye
O.J. Odeyemi
F.O. Durodola
K.I. Adenuga
12.1 Introduction to deep data analysis
212(4)
12.1.1 Data visualization
212(1)
12.1.2 Descriptive statistics
212(1)
12.1.3 Predictive modelling
213(1)
12.1.4 Machine learning
213(2)
12.1.5 Data reduction
215(1)
12.1.6 Multivariate analysis
216(1)
12.1.7 Regression analysis
216(1)
12.1.8 Data wrangling
216(1)
12.2 Deep data analysis for COVID-19
216(7)
12.2.1 Artificial neural networks
217(1)
12.2.2 Deep neural networks
218(1)
12.2.3 Generative adversarial networks
219(1)
12.2.4 Deep belief networks
220(1)
12.2.5 Convolutional neural network
220(1)
12.2.6 Recurrent neural network (RNN) - long short-term memory
220(1)
12.2.7 Modular neural network
221(1)
12.2.8 Sequence-to-sequence models
222(1)
12.3 CNN architectures
223(3)
12.3.1 LeNet
223(1)
12.3.2 AlexNet
223(2)
12.3.3 VGGNet 16
225(1)
12.3.4 Google Net/Inception
225(1)
12.3.5 ResNets
225(1)
12.4 Building the neural network
226(2)
12.4.1 Dataset
226(1)
12.4.2 Data pre-processing
226(2)
12.4.3 Train-test split
228(1)
12.4.4 Data augmentation
228(1)
12.5 Neural network architecture
228(3)
12.6 Other parameters used to configure the neural network
231(1)
12.7 Model summary
231(1)
12.8 Metrics used for evaluation
231(1)
12.9 Results and evaluation
232(2)
12.10 Conclusion and future scope
234(3)
References
234(3)
13 Healthcare system using deep learning
237(20)
J.B. Shajilin Loret
P.C. Sherimon
13.1 Introduction
238(1)
13.2 History of healthcare deep learning
239(1)
13.3 Deep learning benefits
239(1)
13.4 Components of deep learning
239(12)
13.4.1 Generative adversarial networks
240(1)
13.4.2 Multilayer perceptron
241(2)
13.4.3 Radial basis network
243(3)
13.4.4 Recurrent neural networks
246(2)
13.4.5 Convolutional neural networks
248(3)
13.5 The role of deep learning in healthcare in the future
251(1)
13.6 Deep learning applications in healthcare
252(2)
13.6.1 Drug discovery
253(1)
13.6.2 Medical imaging
253(1)
13.6.3 Insurance fraud
253(1)
13.6.4 Alzheimer's disease
253(1)
13.6.5 Genome
254(1)
13.7 Conclusion and future work
254(3)
References
254(3)
14 Intelligent classification of ECG signals using machine learning techniques
257(16)
Kuldeep Singh Kaswan
Anupam Baliyan
Jagjit Singh Dhatterwal
Vishal Jain
Jyotir Moy Chatterjee
14.1 Introduction
258(1)
14.2 Heart-generated ECG signal
258(4)
14.3 Filtering parameters least-mean-square algorithm
262(2)
14.3.1 Updated filter coefficient in normalized least-mean-square (NLMS) algorithm
263(1)
14.3.2 Improved performance LMS (DENLMS) algorithm delaying normalization inaccuracy
263(1)
14.3.3 LMS is variant of sign data least-mean-square (SDLMS) algorithm
263(1)
14.4 Retrieve and classify ECG signals utilizing ML-based techniques
264(1)
14.5 Artificial neural network (ANN)-based ECG signals
265(1)
14.6 Classification of ECG signals based fuzzy logic (FL)
266(1)
14.7 Fourier transform wavelet transforms
267(1)
14.8 Combination of machine learning and statistical algorithms
268(1)
14.9 Conclusion and future work
269(4)
References
269(4)
15 A survey and taxonomy on mutual interference mitigation techniques in wireless body area networks
273(16)
Neethu Suman
P.C. Neelakantan
15.1 Introduction
273(2)
15.2 Interference issues in WBAN
275(1)
15.3 Mutual interference mitigation schemes
276(8)
15.3.1 MAC approach
276(4)
15.3.2 Transmission power control
280(1)
15.3.3 Adaptive spectrum allocation
281(2)
15.3.4 Cooperative scheduling for interference mitigation
283(1)
15.4 Conclusion and future scope
284(5)
References
284(5)
16 Predicting COVID cases using machine learning, android, and firebase cloud storage
289(22)
Ritesh Kumar Sinha
Sukant Kishoro Bisoy
Saurabh Kumar
Sai Prasad Sarangi
Utku Kose
16.1 Introduction
290(1)
16.2 Literature survey
291(2)
16.3 Implementation and methodology
293(2)
16.4 Machine learning models
295(4)
16.4.1 Linear regression
295(1)
16.4.2 Support vector machine
296(1)
16.4.3 Random forest
297(2)
16.4.4 Decision tree
299(1)
16.5 Introduction to android app
299(1)
16.6 Result analysis
299(8)
16.6.1 Odisha analysis
304(2)
16.6.2 Delhi analysis
306(1)
16.6.3 Maharashtra analysis
307(1)
16.7 Conclusion and future work
307(4)
References
308(3)
17 Technological advancement with artificial intelligence in healthcare
311(22)
Manas Kumar Yogi
Jyotsna Garikipati
Jyotir Moy Chatterjee
17.1 Introduction
311(4)
17.1.1 Steps to build a machine learning model
313(1)
17.1.2 Machine learning terminology
313(1)
17.1.3 ML algorithms
314(1)
17.2 Literature review
315(1)
17.2.1 Applications of machine learning in healthcare
315(1)
17.3 Disease identification and diagnosis
316(4)
17.3.1 Heart disease
317(1)
17.3.2 Diabetes
317(1)
17.3.3 Liver disease
317(1)
17.3.4 Dengue disease
318(1)
17.3.5 Hepatitis disease
319(1)
17.4 Drug discovery and manufacturing
320(3)
17.5 Electronic health records
323(2)
17.6 Disease prediction using machine learning
325(1)
17.7 Fairness
325(1)
17.7.1 Fairness in the dataset
326(1)
17.7.2 Fairness in model or algorithm
326(1)
17.7.3 Fairness in the metrics/results
326(1)
17.8 Data analytics role in healthcare
326(1)
17.8.1 Predictive modeling
326(1)
17.8.2 Reduction in healthcare costs
327(1)
17.8.3 Empowering advanced chronic disease prevention
327(1)
17.9 Deep learning applications in healthcare
327(2)
17.9.1 Drug discovery
327(1)
17.9.2 Challenges faced by deep learning applications in healthcare
328(1)
17.10 Conclusion and future scope
329(4)
References
329(4)
18 Changing dynamics on the Internet of Medical Things: challenges and opportunities
333(12)
Imtiaz Ali Brohi
Najma Imtiaz All
Pardeep Kumar
18.1 Introduction
333(2)
18.2 The applications of Internet of Things
335(1)
18.3 Healthcare and Internet of Things
336(1)
18.4 Security in Internet of Medical Things
336(1)
18.5 Privacy in Internet of Medical Things
337(2)
18.6 Perception of trust and risk in IoMT
339(1)
18.7 Conclusion and future scope
340(5)
References
340(5)
19 Internet of Drones (IOD) in medical transport application
345(10)
G. Prasad
J. Kavya
J. Sahana
19.1 Introduction to unmanned aerial vehicle
345(1)
19.2 Internet of Things in Industry 5.0
346(2)
19.3 Applications in medical transport
348(1)
19.4 Methodology and approach
349(1)
19.5 Conclusion and future
350(5)
Acknowledgment
351(1)
References
352(3)
20 Blockchain-based Internet of Things (IoT) for healthcare systems: COVID-19 perspective
355(16)
Anand Sharma
S.R. Biradar
H.K.D. Sarma
N.P. Rana
20.1 Introduction
356(3)
20.2 IoT in healthcare system
359(1)
20.3 COVID-19 outbreak
360(1)
20.4 Blockchain
361(3)
20.5 Blockchain-based IoT for healthcare systems
364(3)
20.6 Advantages of proposed system
367(2)
20.7 Conclusion and future scope
369(2)
References
369(2)
21 Artificial intelligence-based diseases detection and diagnosis in healthcare
371(12)
Said El Kqfhali
Iman El Mir
21.1 Introduction
371(2)
21.2 Overview of diseases detection and diagnosis techniques
373(1)
21.3 Supervised learning models
373(4)
21.3.1 Deep learning models
373(2)
21.3.2 Neural networks models
375(1)
21.3.3 Regression models
376(1)
21.3.4 Traditional classification models
377(1)
21.3.5 Probabilistic models
377(1)
21.4 Unsupervised learning models
377(2)
21.4.1 Clustering models
378(1)
21.4.2 One-class classification models
378(1)
21.4.3 Dimensionality reduction models
379(1)
21.5 Reinforcement learning models
379(1)
21.6 Summary of some applications for disease diagnosis in healthcare
380(1)
21.7 Some open research problems
381(2)
21.8 Conclusions
383(1)
References 383(6)
Index 389
Vishal Jain is an associate professor at the Department of Computer Science and Engineering, Sharda University, India. He was awarded Young Active Member Award (2012-13) from the Computer Society of India, Best Faculty Award (2017), and Best Researcher Award (2019) from BVICAM, New Delhi. He has published over 70 peer-reviewed papers and 10 books. His research areas include information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, and sensor networks.



Jyotir Moy Chatterjee is an assistant professor in the Information Technology Department at Lord Buddha Education Foundation (LBEF), Nepal. He has been a member of the organizing committee of various international conferences (IEEE, Springer, Elsevier) and serves as a reviewer for numerous international journals. His research interests include the Internet of Things, machine learning, and deep learning. He has published 22 research papers, 3 international conference papers, 28 books, and 16 chapters.



Pardeep Kumar is a professor and the head of the Software Engineering Department, QUEST University, Nawabshah, Pakistan. He is also the director for the Office of Research, Innovation and Commercialization (ORIC). His research interests include wireless communication, wireless sensor networks, distributed and embedded systems and IoT technologies. Dr. Kumar has been an author/editor of 4 books, several book chapters, and more than 50 research publications.



Utku Kose is an associate professor at the Department of Computer Engineering of Süleyman Demirel University, Turkey. He has published more than 200 journal papers, conference presentations, keynote speeches and book chapters. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science.
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