Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network

This paper reports on ongoing research, which aims to prove that features of Obstructed Sleep Apnoea (OSA) can be automatically identified from single-lead electrocardiogram (ECG) signals using a One-Dimensional Convolutional Neural Network (1DCNN) model. The 1DCNN is also compared against other machine learning (ML) classifier models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC). The 1DCNN architecture consists of 4 major parts, a Convolutional Layer, a Flattened Dense Layer, a Max Pooling Layer and a Fully Connected Multilayer Perceptron (MLP), with 1 Hidden Layer and a SoftMax output. The model repeatedly learns how to better extract prominent features from one-dimensional data and map it to the MLP for increased prediction. Training and validation are achieved using pre-processed time-series ECG signals captured from 35 ECG recordings. Using our unique windowing strategy, the data is shaped into 5 datasets of different window sizes. A total of 15 models (5 for each group, 1DCNNs, RFCs, SVMs) were evaluated using various metrics, with each being run over numerous experiments. Results show the 1DCNN-500 model delivered the greatest degree of accuracy and rapidity in comparison to the best producing RFC and SVM classifiers. 1DCNN-500 (Sensitivity 0.9743, Specificity 0.9708, Accuracy 0.9699); RFC-500 (Sensitivity/Recall (0) 0.90 / (1) 0.94, Precision (0) 0.94 / (1) 0.90, Accuracy 0.91); SVM-500 (Sensitivity (0) 0.94 / (1) 0.50, Precision (0) 0.65 / (1) 0.90, Accuracy 0.72). The model presents a novel approach that could provide support mechanisms in clinical practice to promptly diagnose patients suffering from OSA

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Wearable Wireless Devices

No abstract available

Sleep Medicine and the Evolution of Contemporary Sleep Pharmacotherapy

Sleep is a fundamental physiological feature experienced by all known mammalian, and most non-mammalian, species. Underscoring its importance is the wide array of neural and cellular processes that have evolved to govern when and how it occurs, its duration, sequence of phases, and the influence it exerts on numerous other brain functions. This book takes up the growing prevalence of sleep disorders affecting these processes and the panorama of pharmaceutical tools that have evolved for their medical care. Its wide-ranging discussion promises not only recent updates on their clinical management but a contemporary window into sleep’s cross-cutting relevance for the many neurological dysfunctions now known to associate with sleep disturbances