Internet of Things and Nature-Inspired Intelligent Techniques for the Future of Biomedical Engineering

Along with the rise of artificial intelligence (AI), there are many different research fields gaining importance. Because of the growing amount of data and needs for immediate access to information for dealing with the problems, different types of research fields take place within the scientific community. Internet of things (IoT) is one of them, and it enables devices to communicate with each other in order to form a general network of physical, working devices. The objective of this chapter in this manner is to provide a general discussion of using nature-inspired techniques of AI to form the future of biomedical engineering over IoT. Because it is often thought that the medical services of the future will be based on autonomous machines supported with AI and IoT, discussing such a topic by considering biomedical engineering applications will be good for the related literature.

Performance of Negative Selection Algorithms in Patient Detection and Classification

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.

Intelligent Biomedical Engineering Operations by Cloud Computing Technologies

Cloud computing is an effective way of using hardware- and software-oriented resources at optimum levels. Thanks to this technology, it is possible to share large amounts of resources effectively and accurately among target users. Because it is a rapidly growing technology, one cannot deny that it has remarkable relations with alternative research fields having great potential and application scope. It is clear that artificial intelligence is one of these fields. As associated with both these research fields, the purpose of this chapter is to examine artificial-intelligence-based biomedical engineering works supported/connected with cloud computing. Because it has a vital importance with applications regarding the medical/health problems, biomedical engineering needs support from the most recent technologies and research fields in this manner. So, the chapter provides a view over the intersection of these three research fields as trying to improve awareness among interested readers.

Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.

Intelligent Techniques Inspired by Nature and Used in Biomedical Engineering

Managing medical information and knowledge is becoming an increasing problem for healthcare professionals. Medical science that contains ever-increasing amounts of knowledge, such as the medical history of a patient, medical data about diseases, diagnosis and treatment methods, should be necessarily a science of information. The real problem faced by patients and healthcare providers is finding and using relevant knowledge at the right time. In this context, in the middle of 1950s, intelligent computer systems, called clinical decision support systems (CDSS), were introduced as a new concept. CDSS is defined as an active intelligent system that can help medical experts to make decisions by taking specific recommendations. Also, it provides decisions based on resolving patient-specific information and related medical truths. The objective of this chapter is to focus on these systems and explain relations with the field of artificial intelligence methods, approaches, or techniques in this manner.

Determining Headache Diseases With Genetic Algorithm

Currently, medical diagnosis has a strong relation with the artificial-intelligence-oriented approaches. Because it is practical to employ intelligent mechanisms over some input data-expert knowledge and design effective solution ways, even the biomedical engineering field is interested in taking support from artificial intelligence. If applications in this manner are taken into consideration, we can see that medical diagnoses have a big percentage. In the sense of the explanations, the objective of this chapter is to use genetic algorithm (GA) for diagnosing headache diseases. As a popular and essential technique benefiting from evolutionary mechanisms, GA can deal with many different types of real-world problems. So, it has been chosen as the solution way/algorithm over the headache disease detection problem, which shapes the research framework of the study. The chapter content gives information about the performed diagnosis application and the results.

Towards an Intelligent Biomedical Engineering With Nature-Inspired Artificial Intelligence Techniques

Artificial intelligence has a remarkable effect on many different fields with its flexible and comprehensive solution approaches to solve real-world problems. In this context, the field of biomedical engineering has also been affected by employment of different artificial intelligence-based techniques. This chapter aims to give a theoretical discussion on using nature-inspired artificial intelligent techniques for obtaining intelligent applications within biomedical engineering. As it is known, techniques within the field of artificial intelligence are inspired from nature. So, it is a good approach to focus on nature-inspired techniques for discussing intelligent biomedical engineering research works. Readers will have a chance to understand some ways of using artificial intelligence for achieving better results in biomedical engineering and the related developments associated with this field.

Deep Learning and Biomedical Engineering

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

Artificial Intelligence Applications on Classification of Heart Sounds

The aim of this chapter is to classify normal and extra systole heart sounds using artificial intelligence methods. Initially, both heart sounds have been passed from Butterworth, Chebyshev, Elliptic digital filter in specific frequency values to remove noise. Afterwards, features of heart sounds have been obtained for classification. For this process, wavelet transform and Mel-frequency cepstral coefficients (MFCC) methods have been applied. Training and test data have been created for classifier by taking means and standard deviation of gained feature. Support vector machine (SVM) and artificial neural network (ANN) methods have been used for classification of these heart sounds. Using wavelet and MFCC features, classification success of SVM has been obtained as 93.33% and 100%, respectively. Using wavelet and MFCC features, classification success of ANN has been obtained as 83.33% and 90%, respectively.

A General Medical Diagnosis System Formed by Artificial Neural Networks and Swarm Intelligence Techniques

One of the most popular applications of artificial intelligence within the medical field is developing medical diagnosis systems. Because artificial-intelligence-based techniques are able to use pre-data and instant data flow for making predictions, it is an easy task to design intelligent systems that can give advice to people or perform diagnosis-based decision making. So, it has been an important research interest to design and develop intelligent systems, which are able to make diagnoses for medical purposes. In this sense, the objective of this chapter is to introduce a general medical diagnosis system that can be used for detecting diseases. In detail, the system employs artificial neural networks and swarm-intelligence-based techniques to form a general framework of intelligent diagnosis. The chapter briefly focuses on the infrastructure of the system and discusses its diagnosis potential.