Sleep Apnea Screening by Autoregressive Models From a Single ECG Lead

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Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.info:eu-repo/semantics/publishedVersio

Spectral Heart Rate Variability analysis using the heart timing signal for the screening of the Sleep Apnea–Hypopnea Syndrome

The final publication is available http://dx.doi.org/10.1016/j.compbiomed.2016.01.023[Abstract] Some approaches have been published in the past using Heart Rate Variability (HRV) spectral features for the screening of Sleep Apnea–Hypopnea Syndrome (SAHS) patients. However there is a big variability among these methods regarding the selection of the source signal and the specific spectral components relevant to the analysis. In this study we investigate the use of the Heart Timing (HT) as the source signal in comparison to the classical approaches of Heart Rate (HR) and Heart Period (HP). This signal has the theoretical advantage of being optimal under the Integral Pulse Frequency Modulation (IPFM) model assumption. Only spectral bands defined as standard for the study of HRV are considered, and for each method the so-called LF/HF and VLFn features are derived. A comparative statistical analysis between the different resulting methods is performed, and subject classification is investigated by means of ROC analysis and a Naïve-Bayes classifier. The standard Apnea-ECG database is used for validation purposes. Our results show statistical differences between SAHS patients and controls for all the derived features. In the subject classification task the best performance in the testing set was obtained using the LF/HF ratio derived from the HR signal (Area under ROC curve=0.88). Only slight differences are obtained due to the effect of changing the source signal. The impact of using the HT signal in this domain is therefore limited, and has not shown relevant differences with respect to the use of the classical approaches of HR or HP.Xunta de Galicia; CN2011/007Ministerio de Economía y Competitividad; TIN2013-40686-PXunta de Galicia; CN2012/21

A Framework for Evaluation and Identication of Time Series Models for Multi-Step Ahead Prediction of Physiological Signals

Significant interest exists in the potential to use continuous physiological monitoring to prevent respiratory complications and death, especially in the postoperative period. Smart alarm-threshold based systems are currently used with hospitalized patients. Despite clinical observations and research studies to support benefit from these systems, several concerns remain. For example, a small difference in a threshold may significantly increase the alarm rate. A significant increase in alarm related adverse outcomes has been reported by health care oversight organizations. Also, it has been recently shown that the signaled alarms are indeed late detections for clinical instability leading to a delayed recognition and less successful clinical intervention. This dissertation advances the state of art by moving from just monitoring towards prediction of physiological variables. Moving in this direction introduces research challenges in many aspects. Although existing literature describes many metrics for characterizing the prediction performance of time series models, these metrics may not be relevant for physiological signals. In these signals, clinicians are often concerned about specific regions of clinical interest. This dissertation develops and implements different types of metrics that can characterize the performance in predicting clinically relevant regions in physiological signals. In the era of massive data, biomedical devices are able to collect a large number of synchronized physiological signals recording a significant time history of a patient's physiological state. Directionality between physiological signals and which ones can be used to improve the ability to predict the other ones is an important research question. This dissertation uses a dynamic systems perspective to address this question. Metrics are also defined to characterize the improvement achieved by incorporating additional data into the prediction model of a physiological signal of interest. Although a rich literature exists on time series prediction models, these models traditionally consider the (absolute or square) error between the predicted and actual time series as an objective for optimization. This dissertation proposes two modeling frameworks for predicting clinical regions of interest in physiological signals. The physiological definition of the clinically relevant regions is incorporated in the model development and used to optimize models with respect to predictions of these regions.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116666/1/elmoaqet_1.pd

Uncovering the structure of clinical EEG signals with self-supervised learning

Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels. Approach. We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically, we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection. We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches. Main results. Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects. Significance. We demonstrate the benefit of SSL approaches on EEG data. Our results suggest that self-supervision may pave the way to a wider use of deep learning models on EEG data.Peer reviewe

Sleep stage and obstructive apneaic epoch classification using single-lead ECG

<p>Abstract</p> <p>Background</p> <p>Polysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal.</p> <p>Methods</p> <p>For this purpose, PSG recordings (ECG included) were obtained during the night's sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed.</p> <p>Results</p> <p>QDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM.</p> <p>Conclusion</p> <p>This study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.</p

An interactive, real-time, high precision and portable monitoring system of obstructive sleep apnea

Obstructive sleep apnea (OSA) is the most common type of sleep apnea which is defined as the suspension of breathing. OSA is generally caused by complete or partial obstruction of airway during sleep, making the breathing pattern irregular and abnormal for prolonged periods of time. Apnea can contribute to a variety of life threatening medical conditions, and can be deadly if left untreated. Nowadays, out of 18 to 50 million people in the US, most cases remain undiagnosed due to the cost, cumbersome and resource limitations of overnight polysomnography (PSG) at sleep labs. Currently PSG relies on a doctor's experience. In order to improve the medical service efficiency, reduce diagnosis time and ensure a more accurate diagnosis, a quantitative and objective method is needed. In this dissertation, an innovative method in characterizing bio-signals for detecting epochs of sleep apnea with high accuracy is presented. Three data channels that are related to breath defect; respiratory sound, ECG and SpO2 are investigated, in order to extract physiological indicators that characterize sleep apnea. An automated method was used to analyze the respiratory sound to find pauses in breathing. Furthermore, the automated method analyzed ECG to find irregular heartbeats and SpO 2 to find rises and drops. The system consists of three main parts which are signal segmentation, features extraction and features classification. Feature extractions process is based on statistical measures. Features classification process is learned through Support Vector Machines (SVMs) and Neural Network (NN) classifiers. Moreover, a preprocessing technique is carried out to distinguish the R-wave from the other waves of the ECG signal. The approach presented in this dissertation was tested using downloaded polysomnographic ECG and SpO2 data from the Physionet database. In addition, to identifying sleep apnea using the acoustic signal of respiration; the characterization of breathing sound was carried by Voice Activity Detection (VAD) algorithm. VAD was used to measure the energy of the acoustic respiratory signal during breath and silence segments. From the experimental results for the three signals, it was concluded that the precision of classifying sleep apnea has an accuracy of 97%. This result offers a clinical reference value for identifying OSA instead of expensive PSG visual scoring method which is commonly used to asses sleep apnea, and could reduce diagnostic time and improve medical service efficiency

Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.

INTRODUCTION: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. METHODS: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. RESULTS: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%