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Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG
URTNASAN ERDENEBAYAR,박종욱,주은연,이경중 대한의학회 2020 Journal of Korean medical science Vol.35 No.47
Background: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. Methods: A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. Results: F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. Conclusion: The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.
Prediction Method of Periodic Limb Movements Based on Deep Learning Using ECG Signal
Urtnasan Erdenebayar,Jong-Uk Park,SooYong Lee,Eun-Yeon Joo,Kyoung-Joung Lee 한국지능시스템학회 2020 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.20 No.2
In this study, we demonstrated a novel method to predict a patient with periodic limb movements (PLMs) based on a deep learning model using an electrocardiogram (ECG) signal. A convolutional neural network (CNN) model was used to distinguish between the PLM and control subjects through morphological analysis of an ECG signal. The constructed CNN model consisted of convolutional, pooling, and fully connected layers. For this study, polysomnography (PSG) data that were measured from 14 subjects at the Samsung Medical Center were used. The subjects were divided into control group (4 males, 3 females) and PLM group (4 males, 3 females). To train and evaluate the CNN model, the ECG dataset was collected during the PSG study, and it was normalized and segmented at a duration of 10 s. The training and test sets consisted of 30,324 and 7,582 segments, respectively. The CNN model presented a prediction performance with an F1-score of 100.0% for the test sets. We obtained robust results that demonstrated the possibility of the automatic screening of PLM patients using the CNN model with an ECG signal.
URTNASAN ERDENEBAYAR,김형곤,박종욱,강동원,이경중 대한의학회 2019 Journal of Korean medical science Vol.34 No.7
Background: In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal. Methods: We designed a CNN model and optimized it by dropout and normalization. One- dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database. Results: The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%. Conclusion: The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.
Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit
Urtnasan Erdenebayar,Yeewoong Kim,Joung-Uk Park,SooYong Lee,Kyoung-Joung Lee 한국지능시스템학회 2020 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.20 No.3
A healthy sleep structure is clinically very important for overall health. The sleep structure can be represented by the percentage of different sleep stages during the total sleep time. In this study, we proposed a method for automatic classification of sleep stages from an electrocardiogram (ECG) signal using a gated-recurrent unit (GRU). The proposed method performed multiclass classification for three-class sleep stages such as awake, light, and deep sleep. A deep structured GRU was used in the proposed method, which is a common recurrent neural network. The proposed deep learning (SleepGRU) model consists of a 5-layer GRU and is optimized by batch-normalization, dropout, and Adam update rules. The ECG signal was recorded during nocturnal polysomnography from 112 subjects, and was normalized and segmented into units of 30-second duration. To train and evaluate the proposed method, the training set consisted of 80,316 segments from 89 subjects, and the test set used 20,079 segments from 23 subjects. We achieved good performances with an overall accuracy of 80.43% and F1-score of 80.07% for the test set. The proposed method can be an alternative and useful tool for sleep monitoring and sleep screening, which have previously been manually evaluated by a sleep technician or sleep expert.
Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor
URTNASAN ERDENEBAYAR,박종욱,정필수,이경중 대한의학회 2017 Journal of Korean medical science Vol.32 No.6
In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.
Urtnasan, Erdenebayar,Park, Jong-Uk,Lee, Kyoung-Joung IOP 2018 Physiological measurement Vol.39 No.6
<P> <I>Objective</I>: In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. <I>Approach</I>: In this study, automatic classification of three classes—normal, hypopnea, and apnea—based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45 096 events) and evaluated on a test dataset (11 274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. <I>Main results</I>: The proposed CNN model reaches a mean <img ALIGN='MIDDLE' ALT='' SRC='http://ej.iop.org/images/0967-3334/39/6/065003/pmeaaac7b7ieqn001.gif'/>-score of 93.0 for the training dataset and 87.0 for the test dataset. <I>Significance</I>: Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH.</P>
Optimal Classifier for Detection of Obstructive Sleep Apnea Using a Heartbeat Signal
Erdenebayar Urtnasan,Jong-Uk Park,SooYong Lee,Kyoung-Joung Lee 한국지능시스템학회 2017 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.17 No.2
This study is to find the optimum classifier that can be easy and robust diagnostic method of the obstructive sleep apnea (OSA) using a heartbeat signal. The heartbeat signal was acquired from the 92 patients with OSA. The dataset consists 98,060 epochs, from them the training sets contained 68,642 epochs from the 63 OSA patients and test sets contained 29,418 epochs from the 29 OSA patients, respectively. The heartbeat signal was analyzed in the time and frequency domain and six features were extracted (normal-to-normal [NN], standard deviation of mean NN [SDNN], root mean square of successive differences [rMSSD], low-frequency [LF], high-frequency [HF], and LF/HF ratio). All extracted features were used to train the following classifiers: linear discriminant analysis (LDA), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN) and support vector machine (SVM). The top three classifiers (SVM, DT, and LDA) showed the accuracy of 93.2%, 93.2%, and 93.2% for test sets, respectively. Then, the top three classifiers could be effective on sleep studies and OSA detections.