scholarly journals Estimating daytime sleepiness with previous night electroencephalography, electrooculography, and electromyography spectrograms in patients with suspected sleep apnea using a convolutional neural network

SLEEP ◽  
2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Sami Nikkonen ◽  
Henri Korkalainen ◽  
Samu Kainulainen ◽  
Sami Myllymaa ◽  
Akseli Leino ◽  
...  
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Daytime Sleepiness ◽  
Sleep Latency ◽  
Standard Test ◽  
Common Symptom ◽  
Previous Night ◽  
Obstructive Sleep ◽  
Sleep Questionnaires

Abstract A common symptom of obstructive sleep apnea (OSA) is excessive daytime sleepiness (EDS). The gold standard test for EDS is the multiple sleep latency test (MSLT). However, due to its high cost, MSLT is not routinely conducted for OSA patients and EDS is instead evaluated using sleep questionnaires. This is problematic however, since sleep questionnaires are subjective and correlate poorly with the MSLT. Therefore, new objective tools are needed for reliable evaluation of EDS. The aim of this study was to test our hypothesis that EDS can be estimated with neural network analysis of previous night polysomnographic signals. We trained a convolutional neural network (CNN) classifier using electroencephalography, electrooculography, and chin electromyography signals from 2,014 patients with suspected OSA. The CNN was trained to classify the patients into four sleepiness categories based on their mean sleep latency (MSL); severe (MSL < 5min), moderate (5 ≤ MSL < 10), mild (10 ≤ MSL < 15), and normal (MSL ≥ 15). The CNN classified patients to the four sleepiness categories with an overall accuracy of 60.6% and Cohen’s kappa value of 0.464. In two-group classification scheme with sleepy (MSL < 10 min) and non-sleepy (MSL ≥ 10) patients, the CNN achieved an accuracy of 77.2%, with sensitivity of 76.5%, and specificity of 77.9%. Our results show that previous night’s polysomnographic signals can be used for objective estimation of EDS with at least moderate accuracy. Since the diagnosis of OSA is currently confirmed by polysomnography, the classifier could be used simultaneously to get an objective estimate of the daytime sleepiness with minimal extra workload.

scholarly journals Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

2021 ◽  
Author(s):  
Satoru Tsuiki ◽  
Takuya Nagaoka ◽  
Tatsuya Fukuda ◽  
Yuki Sakamoto ◽  
Fernanda R. Almeida ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Apnea Hypopnea Index ◽  
Cephalometric Analysis ◽  
Obstructive Sleep ◽  
Main Region

Abstract Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. Results The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. Conclusions A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

scholarly journals Patients’ symptoms as a reason for consulting a doctor and obtaining a diagnosis of obstructive sleep apnea

2017 ◽  
Vol 145 (3-4) ◽  
pp. 136-140
Author(s):  
Dimitar Karkinski ◽  
Dejan Dokic
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Medical History ◽  
Disease Severity ◽  
Daytime Sleepiness ◽  
Apnea Hypopnea Index ◽  
Common Symptom ◽  
Self Awareness ◽  
Obstructive Sleep ◽  
To Come

Introduction/Objective. Obstructive sleep apnea (OSA) is characterized by a number of symptoms of which the patient is sometimes not aware. The aim of this study was to determine the symptoms due to which patients came to our sleep department, and to examine to which extent patients? self-awareness plays a role in diagnosing OSA. Methods. The study included 388 patients who came to the Sleep Department of the Clinic of Pulmonology, Skopje, Macedonia, from 2012 to 2016, with suspicion of OSA. Medical history was taken from all patients and polysomnography was performed in order to diagnose OSA. All patients with symptoms of OSA and Apnea?Hypopnea Index score of over 5 were diagnosed with OSA. Results. We identified a list of 23 symptoms that lead patients to visit a doctor. The most common symptom was snoring, which occurs in 86% of patients. It is followed by a feeling of under-sleeping with 68% and witnessed apnea with 63%. A total of 258 patients were diagnosed with OSA. The most important primary symptoms that led OSA-positive patients to our clinic were snoring, witnessed apnea, and daytime sleepiness. The percentage of snoring was decreasing with disease severity. Percentage of witnessed apnea and daytime sleepiness were increasing with disease severity. Self-awareness of symptoms led a majority of the patients to come to the Sleep Department. Conclusion. Patients who have symptoms such as snoring, witnessed apnea, and daytime sleepiness are likely to suffer from OSA. Most of the patients are aware of their symptoms and seek help from a doctor.

scholarly journals Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129586-129599 ◽  
Author(s):  
Sheikh Shanawaz Mostafa ◽  
Fabio Mendonca ◽  
Antonio G. Ravelo-Garcia ◽  
Gabriel Julia-Serda ◽  
Fernando Morgado-Dias
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Hyperparameter Optimization ◽  
Multi Objective ◽  
Obstructive Sleep ◽  
Apnea Detection

A convolutional neural network architecture to enhance oximetry ability to diagnose pediatric obstructive sleep apnea

2021 ◽  
pp. 1-1
Author(s):  
Fernando Vaquerizo-Villar ◽  
Daniel Alvarez ◽  
Leila Kheirandish-Gozal ◽  
Gonzalo Cesar Gutierrez-Tobal ◽  
Veronica Barroso-Garcia ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Obstructive Sleep

scholarly journals A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250618
Author(s):  
S. M. Isuru Niroshana ◽  
Xin Zhu ◽  
Keijiro Nakamura ◽  
Wenxi Chen
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Expert Knowledge ◽  
Dense Layer ◽  
Time Frequency ◽  
Average Accuracy ◽  
Obstructive Sleep ◽  
Fused Image

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for diagnosing OSA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of OSA diagnosis in public health sectors. Therefore, electrocardiogram (ECG)-based methods for OSA detection have been proposed to automate the polysomnography procedure and reduce its discomfort. So far, most of the proposed approaches rely on feature engineering, which calls for advanced expert knowledge and experience. This paper proposes a novel fused-image-based technique that detects OSA using only a single-lead ECG signal. In the proposed approach, a convolutional neural network extracts features automatically from images created with one-minute ECG segments. The proposed network comprises 37 layers, including four residual blocks, a dense layer, a dropout layer, and a soft-max layer. In this study, three time–frequency representations, namely the scalogram, the spectrogram, and the Wigner–Ville distribution, were used to investigate the effectiveness of the fused-image-based approach. We found that blending scalogram and spectrogram images further improved the system’s discriminative characteristics. Seventy ECG recordings from the PhysioNet Apnea-ECG database were used to train and evaluate the proposed model using 10-fold cross validation. The results of this study demonstrated that the proposed classifier can perform OSA detection with an average accuracy, recall, and specificity of 92.4%, 92.3%, and 92.6%, respectively, for the fused spectral images.

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