Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications.
This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert’s knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications.
The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.
Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field.
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xiii | |
| Abbreviations |
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xvii | |
| Preface |
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xxiii | |
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1 Design of Medical Expert Systems Using Machine Learning Techniques |
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1 | (30) |
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2 From Design Issues to Validation: Machine Learning in Biomedical Engineering |
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31 | (20) |
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3 Biomedical Engineering and Informatics Using Artificial Intelligence |
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51 | (22) |
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4 Hybrid Genetic Algorithms for Biomedical Applications |
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73 | (26) |
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5 Healthcare Applications of the Biomedical AI System |
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99 | (26) |
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6 Applications of Artificial Intelligence in Biomedical Engineering |
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125 | (22) |
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7 Biomedical Imaging Techniques Using AI Systems |
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147 | (26) |
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8 Analysis of Heart Disease Prediction Using Machine Learning Techniques |
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173 | (22) |
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9 A Review on Patient Monitoring and Diagnosis Assistance by Artificial Intelligence Tools |
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195 | (22) |
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10 Semantic Annotation of Healthcare Data |
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217 | (16) |
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11 Drug Side Effect Frequency Mining over a Large Twitter Dataset using Apache Spark |
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233 | (28) |
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12 Deep Learning in Brain Segmentation |
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261 | (28) |
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13 Security and Privacy Issues in Biomedical AI Systems and Potential Solutions |
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289 | (22) |
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14 LiMoS---Live Patient Monitoring System |
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311 | (20) |
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15 Real-Time Detection of Facial Expressions Using k-NN, SVM, Ensemble classifier and Convolution Neural Networks |
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331 | (22) |
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16 Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence |
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353 | (18) |
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17 Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques |
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371 | (30) |
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Manivanna Boopathi Arumugam |
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18 Deployment of Supervised Machine Learning and Deep Learning Algorithms in Biomedical Text Classification |
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401 | (22) |
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19 Energy Efficient Optimum Cluster Head Estimation for Body Area Networks |
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423 | (24) |
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20 Segmentation and Classification of Tumour Regions from Brain Magnetic Resonance Images by Neural Network-Based Technique |
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447 | (22) |
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21 A Hypothetical Study in Biomedical Based Artificial Intelligence Systems using Machine Language (ML) Rudiments |
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469 | (24) |
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22 Neural Source Connectivity Estimation using particle filter and Granger causality methods |
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493 | (16) |
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Santhosh Kumar Veeramalla |
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23 Exploration of Lymph Node-Negative Breast Cancers by Support Vector Machines, Naive Bayes, and Decision Trees: A Comparative Study |
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509 | (16) |
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| Index |
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525 | |
Saravanan Krishnan, PhD, is a Senior Assistant Professor in the Department of Computer Science & Engineering at Anna University, Regional Campus, Tirunelveli, Tamilnadu, India. He has 14 years of experience in academics and the IT industry and has published papers in 14 international conferences and 24 international journals. He has also written six book chapters and has edited three books with international publishers. He has done four research projects and two consultancy projects with the total worth of Rs.70 Lakhs. He is an active researcher and academician. Also, he is reviewer for many reputed journals published by Elsevier, Springer, IEEE, etc. He also received an outstanding reviewer certificate from Elsevier, Inc. He is a Mentor of Change for Atal Tinkering Lab of NITI Aayog. He has professional membership with several professional organizations. He has previously worked at Cognizant Technology Solutions, Pvt Ltd. as software associate. He earned his PhD in 2015 and completed his ME (Software Engineering) in 2007.
Ramesh Kesavan, PhD, is currently an Assistant Professor in the Department of Computer Applications, Anna University Regional Campus, Tirunelveli, India. His area of research includes cloud computing, big data analytics, data mining, and machine learning. He earned his PhD degree in Computer Science from Anna University, Chennai, India.
B. Surendiran, PhD, is an Associate Dean (Academic) and Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology, Puducherry, Karaikal, India. His research interests include medical imaging, machine learning, dimensionality reduction, and intrusion detection. He has published over 20 papers in international journals and has several conference publications to his credit. He is an active reviewer for various SCI and Scopus journals. He earned his PhD at the National Institute of Technology, Tiruchirappalli, India.
G. S. Mahalakshmi, PhD, is currently working as Associate Professor in the Computer Science and Engineering Department at College of Engineering, Anna University, Guindy, Chennai, India. She has vast research experience and has published 180 papers in reputed journals and international conferences. She is also deputy director for the Centre for Entrepreneurship Development, Anna University. She is an active reviewer for various SCI, Scopus journals. Her research interest includes machine learning, artificial intelligence, text mining, and natural language processing.
