Tension Type Headache

Development in the field of technology is growing with a fast pace, mainly the IoT (internet of things). It is an interface between machine-to-machine, machine-to-human, machine-to-infrastructure as well as machine-to-environment. Stress, especially TTH (tension type headache), is a serious problem in today's world. Now every person in this world is facing headache and stress-related problems in daily life. To measure the stress level, the authors have introduced the concept of EEG, EMG, and GSR biofeedback. In case of TTH, human is in a state in which one experiences pain like a physical weight or a tight band around one's head. TTH is different from migraine as it can be affected due to everyday activities. The most common type of primitive headache is tension type headache (TTH). The focus of the research work was to compare the impression of EMG-, GSR-, and EEG-integrated biofeedback on stress due to headache and quality of life of the subjects under consideration.

Evolutionary Intelligence-Based Feature Descriptor Selection for Efficient Identification of Anti-Cancer Peptides

A novel evolutionary-based feature selection model for ACPs identification that will explore the relationships hidden across the various feature descriptors is explored in this chapter. In this model, the authors amalgamate the nine feature descriptors from the three groups of peptide feature descriptors including amino acid composition (three descriptors), grouped amino acid composition and composition/transition/distribution (three descriptors). The proposed model integrates these features to unfold the hidden association between the diverse features in peptide classification. However, the inclusion of irrelevant, redundant, and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. Hence, evolutionary-based feature selection is utilized in the model that involves a combination of search and feature utility estimation by ReliefF score. Through extensive experiments on benchmark dataset, it is demonstrated that the proposed model achieves improved performance.

A Hybrid Approach for 3D Lung Segmentation in CT Images Using Active Contour and Morphological Operation

Lung segmentation is the initial step for detection and diagnosis for lung-related abnormalities and disease. In CAD system for lung cancer, this step traces the boundary for the pulmonary region from thorax in CT images. It decreases the overhead for a further step in CAD system by reducing the space for finding the ROIs. The major issue and challenging task for the segmentation is the inclusion of juxtapleural nodules in the segmented lungs. The chapter attempts 3D lung segmentation of CT images using active contour and morphological operations. The major steps in the proposed approach contain: preprocessing through various techniques, Otsu's thresholding for the binarizing the image; morphological operations are applied for elimination of undesired region and, finally, active contour for the segmentation of the lungs in 3D. For experiment, 10 subjects are taken from the public dataset of LIDC-IDRI. The proposed method achieved accuracies 0.979 Jaccard's similarity index value, 0.989 Dice similarity coefficient, and 0.073 volume overlap error when compared to ground truth.

Epileptic Seizure Detection From EEG Signals Using Bagged Ensemble Approach

Seizures are caused by irregular electrical pulses in the brain. Epileptic seizure detection on EEG signals is a long process, which is done manually by epileptologists. The aim of the study is automatically detecting the seizures of the brain, given the electroencephalogram signals by feature extraction and processing through different machine learning algorithms. Machines can be trained to do this type of observation and predict the output with high accuracy. In this chapter, the classification study of individual and ensemble classifier is performed for epileptic seizure detection. The proposed method consists of two phases: extraction of data from EEG signals and development of an individual and ensemble models. Bagging ensemble is developed to achieve better results. The development of the ensemble using various classification algorithms contributes towards increasing the diversity of the ensemble. An extensive comparative study with existing benchmark algorithm is performed for epileptic seizure detection.

A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset

The role of ECG analysis in the diagnosis of cardio-vascular ailments has been significant in recent times. Although effective, the present computational algorithms lack accuracy, and no technique till date is capable of predicting the onset of a CVD condition with precision. In this chapter, the authors attempt to formulate a novel mapping technique based on feature extraction using fractional Fourier transform (FrFT) and map generation using self-organizing maps (SOM). FrFT feature extraction from the ECG data has been performed in a manner reminiscent of short time Fourier transform (STFT). Results show capability to generate maps from the isolated ECG wavetrains with better prediction capability to ascertain the onset of CVDs, which is not possible using conventional algorithms. Promising results provide the ability to visualize the data in a time evolution manner with the help of maps and histograms to predict onset of different CVD conditions and the ability to generate the required output with unsupervised training helping in greater generalization than previous reported techniques.

Heart Sound Data Acquisition and Preprocessing Techniques

Heart sound or phonocardiogram (PCG) signal quantifies the information about the mechanical activity of the heart, and the medical practitioners use the stethoscope to listen to this sound. The PCG signal can be used for clinical applications such as detection of various valvular diseases and non-clinical applications such as biometric system, stress and emotion detection, etc. The PCG signal acquisition and preprocessing are important tasks for the diagnosis of heart valve-related disorders and other applications. The heart sound preprocessing techniques include denoising of PCG signal, segmentation of first and second heart sound (S1, S2) and other heart sound components from the PCG signal, feature extraction from the segmented heart sound components, followed by classification. This chapter reviews the state-of-the-art approaches for heart sound acquisition and pre-processing techniques and also provides the information that is commonly used by the researchers for the validation of their PCG signal processing algorithms.

Automated Seizure Classification Using Deep Neural Network Based on Autoencoder

A neurological abnormality in the brain that manifests as a seizure is the prime risk of epilepsy. The earlier and accurate detection of the epileptic seizure is the foremost task for the diagnosis of epilepsy. In this chapter, a nonlinear deep neural network is used for seizure classification. The proposed network is based on the autoencoder that significantly explores the non-linear dynamics of the electroencephalogram (EEG) signals. It involves the traditional deep neural domain expertise to extract the features from the raw data in order to fit a deep neural network-based learning model and predicts the class of the unknown seizures. The EEG signals are subjected to an autoencoder-based neural network that unintendedly extracts the significant attributes that are applied to the softmax classifier. The achieved classification accuracy is up to 100% on different publicly available Bonn University database classes. The proposed algorithm is suitable for real-time implementation.

Exhibiting App and Analysis for Biofeedback-Based Mental Health Analyzer

Many apps and analyzers based on machine learning have been designed already to help and cure the stress issue, which is increasing rapidly. The project is based on an experimental research work that the authors have performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In their research work, the correctness and accuracy have been studied and compared for two biofeedback devices, electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes—audio, visual, and audio-visual—with the help of data set of tension type headache (TTH) patients. The authors have realized by their research work that these days people have a lot of stress in their lives so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In their project, the authors have a website that contains a closed set of questionnaires, which have some weight associated with each question.

Three Channel Wavelet Filter Banks With Minimal Time Frequency Spread for Classification of Seizure-Free and Seizure EEG Signals

The authors compute the classification accuracy of minimal time-frequency spread wavelet filter bank with three channels in discriminating seizure-free and seizure electroencephalogram (EEG) signals. Wavelet filter bank with three channels generates two wavelet functions and one scaling function at the first level of wavelet decomposition. A time-frequency localized filter bank can be generated by minimizing the time spread and frequency spread of any one or all the functions simultaneously. The minimal time-frequency spread wavelet filter bank with three channels of regularity order, one designed with several different time-frequency optimality criteria and length six, are chosen, and the effect of each optimality criterion on the discrimination of seizure-free and seizure EEG signals is computed. The classification accuracy for five different optimality criteria are computed. Time-frequency localized three-band filter bank of length six classifies, the seizure-free and seizure EEG signals of Bonn University EEG database, with 98.25% of accuracy.

Design of Healthcare Assistant Using EEG Signals for Stress

Stress is a matter of concern for every age group. In this chapter, the author has focused on the age group 17-24 (five males and five females), who have just completed senior secondary and entered into an undergraduate course, who are vulnerable to cope up with the situation of transition. The focus of the chapter is on the design of the study protocol to identify stress levels by using induced stress tests like Stroop color test and arithmetic three-level (easy, medium, and hard) test. Data is divided into bands delta theta alpha beta and gamma by using four levels of discrete wavelet transform, and features extracted are Hjorth parameters activity, mobility, and complexity from the combination of channels and band levels. The accuracy achieved for stress and no-stress for male vs. male, female vs. female, and male vs. female is in the range 93-98% for the combination of channels at the frontal position and at band delta theta alpha beta and gamma whereas less involvement has been observed for other lobes parietal, temporal, and occipital.