221 research outputs found
A Systematic Review of Detecting Sleep Apnea Using Deep Learning
Sleep apnea is a sleep related disorder that significantly affects the population.
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert
technician is needed to score. Numerous researchers have proposed and implemented automatic
scoring processes to address these issues, based on fewer sensors and automatic classification
algorithms. Deep learning is gaining higher interest due to database availability, newly developed
techniques, the possibility of producing machine created features and higher computing power that
allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep
apnea research has currently gained significant interest in deep learning. The goal of this work is to
analyze the published research in the last decade, providing an answer to the research questions such
as how to implement the different deep networks, what kind of pre-processing or feature extraction is
needed, and the advantages and disadvantages of different kinds of networks. The employed signals,
sensors, databases and implementation challenges were also considered. A systematic search was
conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were
selected by considering the inclusion and exclusion criteria, using the preferred reporting items for
systematic reviews and meta-analyses (PRISMA) approach.info:eu-repo/semantics/publishedVersio
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
Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach
Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal
breathing. The severity of OSA can lead to many symptoms such as sudden cardiac
death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It
records many signals from the patient's body for at least one whole night and
calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or
hypopnea incidences per hour. This value is then used to classify patients into
OSA severity levels. However, it has many disadvantages and limitations.
Consequently, we proposed a novel methodology of OSA severity classification
using a Deep Learning approach. We focused on the classification between normal
subjects (AHI 30). The 15-second raw
ECG records with apnea or hypopnea events were used with a series of deep
learning models. The main advantages of our proposed method include easier data
acquisition, instantaneous OSA severity detection, and effective feature
extraction without domain knowledge from expertise. To evaluate our proposed
method, 545 subjects of which 364 were normal and 181 were severe OSA patients
obtained from the MrOS sleep study (Visit 1) database were used with the k-fold
cross-validation technique. The accuracy of 79.45\% for OSA severity
classification with sensitivity, specificity, and F-score was achieved. This is
significantly higher than the results from the SVM classifier with RR Intervals
and ECG derived respiration (EDR) signal feature extraction. The promising
result shows that this proposed method is a good start for the detection of OSA
severity from a single channel ECG which can be obtained from wearable devices
at home and can also be applied to near real-time alerting systems such as
before SCD occurs
A review of automated sleep disorder detection
Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand
Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals
The regulation of the autonomic nervous system changes with the sleep stages
causing variations in the physiological variables. We exploit these changes
with the aim of classifying the sleep stages in awake or asleep using pulse
oximeter signals. We applied a recurrent neural network to heart rate and
peripheral oxygen saturation signals to classify the sleep stage every 30
seconds. The network architecture consists of two stacked layers of
bidirectional gated recurrent units (GRUs) and a softmax layer to classify the
output. In this paper, we used 5000 patients from the Sleep Heart Health Study
dataset. 2500 patients were used to train the network, and two subsets of 1250
were used to validate and test the trained models. In the test stage, the best
result obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity,
92.05% precision, and 84.68% negative predictive value. Further, the Cohen's
Kappa coefficient was 0.74 and the average absolute error percentage to the
actual sleep time was 8.9%. The performance of the proposed network is
comparable with the state-of-the-art algorithms when they use much more
informative signals (except those with EEG).Comment: 12 pages, 4 figures, 2 table
A Review of Deep Learning Methods for Photoplethysmography Data
Photoplethysmography (PPG) is a highly promising device due to its advantages
in portability, user-friendly operation, and non-invasive capabilities to
measure a wide range of physiological information. Recent advancements in deep
learning have demonstrated remarkable outcomes by leveraging PPG signals for
tasks related to personal health management and other multifaceted
applications. In this review, we systematically reviewed papers that applied
deep learning models to process PPG data between January 1st of 2017 and July
31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed
from three key perspectives: tasks, models, and data. We finally extracted 193
papers where different deep learning frameworks were used to process PPG
signals. Based on the tasks addressed in these papers, we categorized them into
two major groups: medical-related, and non-medical-related. The medical-related
tasks were further divided into seven subgroups, including blood pressure
analysis, cardiovascular monitoring and diagnosis, sleep health, mental health,
respiratory monitoring and analysis, blood glucose analysis, as well as others.
The non-medical-related tasks were divided into four subgroups, which encompass
signal processing, biometric identification, electrocardiogram reconstruction,
and human activity recognition. In conclusion, significant progress has been
made in the field of using deep learning methods to process PPG data recently.
This allows for a more thorough exploration and utilization of the information
contained in PPG signals. However, challenges remain, such as limited quantity
and quality of publicly available databases, a lack of effective validation in
real-world scenarios, and concerns about the interpretability, scalability, and
complexity of deep learning models. Moreover, there are still emerging research
areas that require further investigation
- …