Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Automatic Sleep EEG Pattern Detection

Analýza mozkové aktivity je jednou z klícových vyšetrovacích metod v moderní spánkové medicíne a výzkumu.nalysis of recorded brain activity is one of the main investigation methods in modern sleep medicine and research

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

A systematic review on artifact removal and classification techniques for enhanced MEG-based BCI systems

Neurological disease victims may be completely paralyzed and unable to move, but they may still be able to think. Their brain activity is the only means by which they can interact with their environment. Brain-Computer Interface (BCI) research attempts to create tools that support subjects with disabilities. Furthermore, BCI research has expanded rapidly over the past few decades as a result of the interest in creating a new kind of human-to-machine communication. As magnetoencephalography (MEG) has superior spatial and temporal resolution than other approaches, it is being utilized to measure brain activity non-invasively. The recorded signal includes signals related to brain activity as well as noise and artifacts from numerous sources. MEG can have a low signal-to-noise ratio because the magnetic fields generated by cortical activity are small compared to other artifacts and noise. By using the right techniques for noise and artifact detection and removal, the signal-to-noise ratio can be increased. This article analyses various methods for removing artifacts as well as classification strategies. Additionally, this offers a study of the influence of Deep Learning models on the BCI system. Furthermore, the various challenges in collecting and analyzing MEG signals as well as possible study fields in MEG-based BCI are examined

Real-Time, Hardware Efficient Ocular Artifact Removal From Single Channel EEG data Using a Hybrid Algebraic and Wavelet Algorithm

Electroencephalography (EEG) is a promising technique to record brain activities in natural settings. EEG signal usually gets contaminated by Ocular Artifacts (OA), removal of which is critical for the feature extraction and classification. With the increasing interest in wearable technologies, single channel EEG systems are becoming more prevalent that often require real-time signal processing for immediate feedback. In this context, a new hybrid algorithm to detect OA and subsequently remove OA from single channel streaming EEG data is proposed here. The algorithm first detects the OA zones using Algebraic approach, and then removes artifact from the detected OA zones using Discrete Wavelet Transform (DWT) decomposition method. De-noising technique is applied only to the OA zone that minimizes interference to neural information outside of OA zone. The microcontroller hardware implemented hybrid OA removal algorithm demonstrated real-time execution with sufficient accuracy in both OA detection and removal. The performance evaluation was carried out qualitatively and quantitatively for 0.5 sec epoch in overlapping manner using time-frequency analysis, mean square coherence, Correlation Coefficient (CC) and Mutual Information statistics. Matlab implementation resulted in average CC of 0.3242 and average MI of 1.0042, while microcontroller implementation resulted in average CC of 0.4033 for all blinks. Successful implementation of OA removal from single channel real-time EEG data using the proposed algorithm shows promise for real-time feedabck applications of wearable EEG devices