10 research outputs found
Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data
Sleep apnea is a common respiratory disorder characterized by breathing
pauses during the night. Consequences of untreated sleep apnea can be severe.
Still, many people remain undiagnosed due to shortages of hospital beds and
trained sleep technicians. To assist in the diagnosis process, automated
detection methods are being developed. Recent works have demonstrated that deep
learning models can extract useful information from raw respiratory data and
that such models can be used as a robust sleep apnea detector. However, trained
sleep technicians take into account multiple sensor signals when annotating
sleep recordings instead of relying on a single respiratory estimate. To
improve the predictive performance and reliability of the models, early and
late sensor fusion methods are explored in this work. In addition, a novel late
sensor fusion method is proposed which uses backward shortcut connections to
improve the learning of the first stages of the models. The performance of
these fusion methods is analyzed using CNN as well as LSTM deep learning
base-models. The results demonstrate a significant and consistent improvement
in predictive performance over the single sensor methods and over the other
explored sensor fusion methods, by using the proposed sensor fusion method with
backward shortcut connections.Comment: Paper presented at ML4H (Machine Learning for Health) workshop at
NeurIPS 2019. https://ml4health.github.io/2019
Машинне навчання під час діагностування і моніторингу сонного апное
This paper contains a review and analysis of applications of modern ma-chine learning approaches to solve sleep apnea severity level detection by localization of apnea episodes and prediction of the subsequent apnea episodes. We demonstrate that signals provided by cheap wearable devices can be used to solve typical tasks of sleep apnea detection. We review major publicly available datasets that can be used for training respective deep learning models, and we analyze the usage options of these datasets. In particular, we prove that deep learning could improve the accuracy of sleep apnea classification, sleep apnea localization, and sleep apnea prediction, especially using more complex models with multimodal data from several sensors
Intelligent Biosignal Analysis Methods
This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others
Secondary Analysis of Electronic Health Records
Health Informatics; Ethics; Data Mining and Knowledge Discovery; Statistics for Life Sciences, Medicine, Health Science
High Frequency Physiological Data Quality Modelling in the Intensive Care Unit
Intensive care medicine is a resource intense environment in which technical and clinical decision making relies on rapidly assimilating a huge amount of categorical and timeseries physiologic data. These signals are being presented at variable frequencies and of variable quality. Intensive care clinicians rely on high frequency measurements of the patient's physiologic state to assess critical illness and the response to therapies. Physiological waveforms have the potential to reveal details about the patient state in very fine resolution, and can assist, augment, or even automate decision making in intensive care. However, these high frequency time-series physiologic signals pose many challenges for modelling. These signals contain noise, artefacts, and systematic timing errors, all of which can impact the quality and accuracy of models being developed and the reproducibility of results. In this context, the central theme of this thesis is to model the process of data collection in an intensive care environment from a statistical, metrological, and biosignals engineering perspective with the aim of identifying, quantifying, and, where possible, correcting errors introduced by the data collection systems. Three different aspects of physiological measurement were explored in detail, namely measurement of blood oxygenation, measurement of blood pressure, and measurement of time. A literature review of sources of errors and uncertainty in timing systems used in intensive care units was undertaken. A signal alignment algorithm was developed and applied to approximately 34,000 patient-hours of simultaneously collected electroencephalography and physiological waveforms collected at the bedside using two different medical devices
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words