scholarly journals Computer Aided Detection of Obstructive Sleep Apnea from EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 17-24
Author(s):  
Saheed Ademola Bello ◽  
Umar Alqasemi
Keyword(s):  
Sleep Apnea ◽  
Kernel Functions ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Obstructive Sleep ◽  
Computer Aided ◽  
The Time Domain

Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat sleep apnea leads to fatal complications in both psychological and physiological being of human. Electroencephalogram (EEG) performs an important task in probing for sleep apnea through identifying and recording the brain’s activities while sleeping. In this study, computer aided detection of sleep apnea from EEG signals is developed to optimize and increase the prompt recognition and diagnosis of sleep apnea in patients. The time domain, wavelets, and frequency domain of the EEG signals were computed, and features were extracted from these domains. These features are inputted into two machine learning algorithms: Support Vector Machine and K-Nearest Neighbors of different kernel functions and orders. Evaluation metrics such as specificity, accuracy, and sensitivity are computed and analyzed for the classifiers. The KNN classifier outperforms the SVM in classifying apnea from non-apnea events in patients. The KNN order 3 shows the highest performance sensitivity of 85.92%, specificity of 80% and accuracy of 82.69%.

scholarly journals Classification of sleep apnea based on EEG sub-band signal characteristics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoyun Zhao ◽  
Xiaohong Wang ◽  
Tianshun Yang ◽  
Siyu Ji ◽  
Huiquan Wang ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Automatic Classification ◽  
Support Vector ◽  
Composition Analysis ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Normal Breathing ◽  
Obstructive Sleep

AbstractSleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

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

SLEEP ◽  
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):

scholarly journals Classification of sleep apnea using EMD-based features and PSO-trained neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sajjad Afrakhteh ◽  
Ahmad Ayatollahi ◽  
Fatemeh Soltani
Keyword(s):  
Neural Networks ◽  
Sleep Apnea ◽  
Back Propagation ◽  
Pso Algorithm ◽  
Bp Algorithm ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Obstructive Sleep

Abstract In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN’s performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN’s accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

BREAST CANCER DETECTION USING RSFS-BASED FEATURE SELECTION ALGORITHMS IN THERMAL IMAGES

2021 ◽  
pp. 2150020
Author(s):  
Nazila Darabi ◽  
Abdalhossein Rezai ◽  
Seyedeh Shahrbanoo Falahieh Hamidpour
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cancer Detection ◽  
Vital Role ◽  
Support Vector ◽  
Computer Aided Detection ◽  
K Nearest Neighbors ◽  
Cad System ◽  
Common Cancer ◽  
Selection Algorithms

Breast cancer is a common cancer in female. Accurate and early detection of breast cancer can play a vital role in treatment. This paper presents and evaluates a thermogram based Computer-Aided Detection (CAD) system for the detection of breast cancer. In this CAD system, the Random Subset Feature Selection (RSFS) algorithm and hybrid of minimum Redundancy Maximum Relevance (mRMR) algorithm and Genetic Algorithm (GA) with RSFS algorithm are utilized for feature selection. In addition, the Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithms are utilized as classifier algorithm. The proposed CAD system is verified using MATLAB 2017 and a dataset that is composed of breast images from 78 patients. The implementation results demonstrate that using RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 85.36% and 75%, and sensitivity of 94.11% and 79.31%, respectively. In addition, using hybrid GA and RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 83.87% and 69.56%, and sensitivity of 96% and 81.81%, respectively, and using hybrid mRMR and RSFS algorithms for feature selection and kNN and SVM algorithms as classifier have accuracy of 77.41% and 73.07%, and sensitivity of 98% and 72.72%, respectively.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

Prediction of Sleep Apnea Using EEG Signals and Machine Learning Algorithms

2021 ◽  
Author(s):  
Aysu Onargan ◽  
Busra Gavcar ◽  
Gulizar Caliskan ◽  
Aydin Akan
Keyword(s):  
Machine Learning ◽  
Sleep Apnea ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Eeg Signals
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