Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obstructive Sleep Apnea (OSA) detection from the Electrocardiogram (ECG) signals obtained through the body sensors. Our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models—two Convolutional Neural Network (CNN) based models, and a combination of CNN and Long Short-Term Memory (LSTM) models, which were previously proposed in the OSA detection domain. We have chosen four ensemble techniques—majority voting, sum rule and Choquet integral based fuzzy fusion and trainable ensemble using Multi-Layer Perceptron (MLP) for our case study. All the experiments are conducted on the benchmark PhysioNet Apnea-ECG Database. Finally, we have achieved highest OSA detection accuracy of 85.58% using the MLP based ensemble approach. Our best result is also able to surpass many of state-of-the-art methods.

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Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2019.2951346 ◽

2019 ◽

pp. 1-1 ◽

Cited By ~ 5

Author(s):

Henri Korkalainen ◽

Timo Leppanen ◽

Juhani Aakko ◽

Sami Nikkonen ◽

Samu Kainulainen ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Deep Learning ◽

Sleep Apnea ◽

Clinical Population ◽

Sleep Staging ◽

Obstructive Sleep

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414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽

10.1093/sleep/zsab072.413 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A164-A164

Author(s):

Pahnwat Taweesedt ◽

JungYoon Kim ◽

Jaehyun Park ◽

Jangwoon Park ◽

Munish Sharma ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Deep Neural Networks ◽

Support Vector ◽

Learning Models ◽

Obstructive Sleep ◽

Screening Questionnaires ◽

Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

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Anomaly Detection Using Deep Neural Network for IoT Architecture

Applied Sciences ◽

10.3390/app11157050 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7050

Author(s):

Zeeshan Ahmad ◽

Adnan Shahid Khan ◽

Kashif Nisar ◽

Iram Haider ◽

Rosilah Hassan ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Anomaly Detection ◽

Deep Neural Network ◽

Detection System ◽

Traffic Monitoring ◽

Detection Accuracy ◽

Learning Models ◽

Prime Concern ◽

Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

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Cardiac arrhythmias and conduction disturbances in the obstructive sleep apnea syndrome

In a good rythm ◽

10.5604/01.3001.0012.0392 ◽

2018 ◽

Vol 2 (47) ◽

pp. 10-15

Author(s):

Danuta Łoboda ◽

Karolina Simionescu ◽

Anna Szajerska-Kurasiewicz ◽

Dorota Lasyk ◽

Grzegorz Jarosiński ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Cardiac Arrhythmias ◽

Sleep Apnea Syndrome ◽

The Body ◽

Apnea Hypopnea Index ◽

Premature Ventricular Contractions ◽

Mortality And Morbidity ◽

Conduction Disturbances ◽

Obstructive Sleep

Cardiac arrhythmias during sleep are reported in almost half of the population suffering from obstructive sleep apnea (OSA). The most common are bradyarrhythmias and atrial fibrillation whereas premature ventricular contractions and nonsustained ventricular tachycardia are less frequent. The risk of arrhythmia is proportional to the body mass index (BMI), number of respiratory events per hour of sleep described with apnea/hypopnea index (AHI) and the level of oxygen desaturation during these episodes. Continuous positive airway pressure (CPAP) treatment in OSA reduces the incidence of cardiac arrhythmias therefore reduce mortality and morbidity from cardiovascular disease.

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The efficacy of a titrated tongue stabilizing device on obstructive sleep apnea: a quasi-experimental study

Journal of Clinical Sleep Medicine ◽

10.5664/jcsm.9260 ◽

2021 ◽

Author(s):

Waled M. Alshhrani ◽

Mona M. Hamoda ◽

Kentaro Okuno ◽

Yuuya Kohzuka ◽

John A. Fleetham ◽

...

Keyword(s):

Experimental Study ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Obstructive Sleep ◽

Quasi Experimental

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COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

Applied Sciences ◽

10.3390/app112311423 ◽

2021 ◽

Vol 11 (23) ◽

pp. 11423

Author(s):

Chandrakanta Mahanty ◽

Raghvendra Kumar ◽

Panagiotis G. Asteris ◽

Amir H. Gandomi

Keyword(s):

Transfer Learning ◽

Ensemble Methods ◽

Cost Effective ◽

Majority Voting ◽

Learning Models ◽

Medical Practitioners ◽

Healthcare Facilities ◽

Ensemble Techniques ◽

Effective Manner ◽

Detection Strategies

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

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Automated sleep apnea detection from instantaneous heart rate using deep learning models

10.1183/23120541.sleepandbreathing-2021.78 ◽

2021 ◽

Author(s):

Z Siting ◽

Y Jie Chen ◽

K Kishan ◽

A Patanaik

Keyword(s):

Heart Rate ◽

Deep Learning ◽

Sleep Apnea ◽

Learning Models ◽

Instantaneous Heart Rate ◽

Apnea Detection

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407 Explanatory analysis of polysomnography for the identification of sleep apnea hypopnea events using deep learning neural network

SLEEP ◽

10.1093/sleep/zsab072.406 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A161-A162

Author(s):

Soonhyun Yook ◽

Chaitanya Gupte ◽

Zhixian Han ◽

Eun Yeon Joo ◽

Hea Ree Park ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Deep Learning ◽

Sleep Apnea ◽

Learning Algorithms ◽

Sleep Apnea Syndrome ◽

Learning Approaches ◽

Explanatory Analysis ◽

Technology Advancement ◽

Obstructive Sleep ◽

Deep Learning Neural Network

Abstract Introduction Using deep learning algorithms, we investigated univariate and multivariate effects of four polysomnography features including heart rate (HR), electrocardiogram (ECG), oxygen saturation (SpO2) and nasal air flow (NAF) on the identification of sleep apnea and hypopnea events. This explanatory analysis that may clarify the sensitivity and specificity of those features to SAs and SHs have not been probed. Methods We studied 804 polysomonography samples from 704 patients with obstructive sleep apnea and 100 controls. The input data were converted into scalograms as 4-channel 2D images to train Xception networks. For training, 77,638 patches were sampled from the original 6-hour sleep data with 30-second time width. A 10% of these patches were segregated as the test-set. With each feature sets, we tested the following classifications: 1) normal vs apnea vs hypopnea; 2) normal vs. apnea+hypopnea; 3) normal vs. apnea; and 4) normal vs. hypopnea. Results SpO2 classified normal vs. apnea most accurately (98%), followed by NAF (85%), ECG (77%), and HR (63%). SpO2 also showed the highest accuracy in classifying normal vs. hypopnea (87%), and normal vs. apnea+hypopnea (96%) and three groups (82%). When the combination of four features were used, the classification accuracies were generally improved compared to use of SpO2 only (normal vs. apnea 99%; vs. hypopnea 89%; vs. apnea+hypopnea: 94%; three groups: 86%). Conclusion Deep learning with SpO2 or NAF feature most accurately classified apneas from normal sleep events, suggesting these features’ characterization of sleep apnea events. Oxygen desaturation, which is a typical pattern of hypopnea, was only the feature showing reliable accuracy in classifying hypopnea vs. normal. Nevertheless, combination of four polysomnography features could improve the identification of sleep apnea and hypopnea. Furthermore, classifying normal vs. apnea+hypopnea was more accurate than separately classifying three groups, suggesting deep learning approaches as the primary screen tool. Since the classification accuracy of using SpO2 was higher than any other features, developing a portable equipment measuring SpO2 and running deep learning algorithms has the potential for inexpensive, accurate diagnostics of obstructive sleep apnea syndrome. Support (if any) This study was supported by USC STEVENS CENTER FOR INNOVATION TECHNOLOGY ADVANCEMENT GRANTS (TAG), BrightFocus Foundation Award (A2019052S).

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