Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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2017 - 20194

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Author: search.filters.author.Sert, MustafaSubject: search.filters.subject.Electrocardiogram (ECG)

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    Classification of Obstructive Sleep Apnea using Multimodal and Sigma-based Feature Representation
    (2019) Memis, Gokhan; Sert, Mustafa
    Obstructive sleep apnea (OSA) is a sleep disorder characterized by a decrease in blood oxygen saturation and waking up after a long time. Diagnosis can be made by following a full night with a polysomnogram device, so there is a need for computer-based methods for the diagnosis of OSA. In this study, a method based on feature selection is proposed for OSA classification using oxygen saturation and electrocardiogram signals. Standard deviation (sigma) based features have been created to increase accuracy and reduce computational complexity. To evaluate the effectiveness, comparisons were made with selected machine learning algorithms. The achievements of the obtained features were compared with Naive Bayes (NB), k-nearest neighborhood (kNN) and Support Vector Machine (SVM) classifiers. The tests performed on the PhysioNet dataset consisting of real clinical samples show that the use of sigma-based features result an average performance increase of 1.98% in all test scenarios.
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    The Effectiveness of Feature Selection Methods on Physical Activity Recognition
    (2018) Memis, Gokhan; Sert, Mustafa
    For the definition of physical activity monitoring with long activity times can be costly and there is a need for efficient computer based algorithms. Smartphone sensors such as accelerometer, magnetometer, and gyroscope for physical activity recognition are used in many researches. In this study, we propose a multi-modal approach to classify the different physical activities at the feature level by fusing electrocardiography (ECG), accelerometer, magnetometer, and gyroscope signals. We use Support Vector Machine (SVM), nearest neighbors, Naive Bayes, Random Tree and Bagging RepTree classifiers as learning algorithms and provide comprehensive empirical results on fusion strategy. Our experimental results on real clinical examples from the MHealth dataset show that the proposed feature-level fusion approach gives an average accuracy of 98.40% using SVM with the highest value in all scenarios. We also observe that when we use the SVM classifier with the gyroscope signal, which we take the highest value as a single modal, it gives an average accuracy of 96.27%. We achieve a significant improvement in comparision with existing studies.
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    Multimodal Classification of Obstructive Sleep Apnea using Feature Level Fusion
    (2017) Memis, Gokhan; Sert, Mustafa; 0000-0002-7056-4245; AAB-8673-2019
    Obstructive sleep apnea (OSA) is a sleep disorder with long-term consequences. Long-term effects include sleep related issues and cardiovascular diseases. OSA is often diagnosed with an overnight sleep test called a polysomnogram. Monitoring can be costly with long wait times for diagnosis and computer-based efficient algorithms are needed. Here, we employ a multi-modal approach that performs feature-level fusion of two physiological signals, namely electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) for efficient OSA classification. We design Naive Bayes (NB), k-nearest neighbor (kNN), and Support Vector Machine (SVM) classifiers as the learning algorithms and present extensive empirical information regarding the utilized fusion strategy. Compared with other existing methods either considering single modality of signals or perform tests on subjects that have same severity of sleep apnea (i.e., high degree of apnea, low degree of apnea, or without apnea), we also define a test scenario that employs different subjects that have different sleep apnea severity to show the effectiveness of our approach. Our experimental results on real clinical examples from PhysioNet database show that, the proposed multimodal approach using feature-level fusion approach gives best classification rates when using SVM with an average accuracy of 96.64% for all test scenarios, i.e., within Subject with Same Severity (99.49%), between subjects with same sleep apnea severity (95.35%), and between subjects with distinct sleep apnea severity (95.07%).
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    Leveraging Multimodal and Feature Selection Approaches to Improve Sleep Apnea Classification Performance
    (2017) Memis, Gokhan; Sert, Mustafa; Yazici, Adnan; 0000-0002-7056-4245; AAB-8673-2019
    Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust features from both biological signals and design three classifiers, namely Naive Bayes (NB), k-nearest neighbors (kNN), and Support Vector Machine (DVM) to test these features. Our experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and Relieff feature selection based method improves the average classification accuracy by 4.67% on all test scenarios.