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Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram
Erdenebayar, Urtnasan,Kim, Yoon Ji,Park, Jong-Uk,Joo, Eun Yeon,Lee, Kyoung-Joung ELSEVIER SCIENTIFIC PUBLISHERS IRELAND LTD 2019 Computer Methods and Programs in Biomedicine Vol. No.
<P><B>Abstract</B></P> <P><B>Background and Objective</B></P> <P>This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal.</P> <P><B>Methods</B></P> <P>Six deep learning approaches were designed and implemented for automatic detection of SA events including deep neural network (DNN), one-dimensional (1D) convolutional neural networks (CNN), two-dimensional (2D) CNN, recurrent neural networks (RNN), long short-term memory, and gated-recurrent unit (GRU). Designed deep learning models were analyzed and compared in the performances. The ECG signal was pre-processed, normalized, and segmented into 10 s intervals. Subsequently, the signal was converted into a 2D form for analysis in the 2D CNN model. A dataset collected from 86 patients with SA was used. The training set comprised data from 69 of the patients, while the test set contained data from the remaining 17 patients.</P> <P><B>Results</B></P> <P>The accuracy of the best-performing model was 99.0%, and the 1D CNN and GRU models had 99.0% recall rates.</P> <P><B>Conclusions</B></P> <P>The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events, and they could distinguish between apnea and hypopnea events using an ECG signal. The deep learning approaches such as 1D CNN and GRU can be helpful tools to automatically detect SA in sleep apnea screening and related studies.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Deep learning approaches were designed to automatically detect sleep apnea (SA) from an electrocardiogram signal. </LI> <LI> Six deep learning approaches were designed and implemented including DNN, 1D CNN, 2D CNN, RNN, long short-term memory, and gated-recurrent unit models. </LI> <LI> The 1D CNN and GRU models were the best-performing of the accuracy was 99.0% and recall was 99.0%. </LI> <LI> The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events. </LI> </UL> </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.
Erdenebayar Baasanjav,Parthasarathi Bandyopadhyay,사이드구잔파,임수만,정상문 한국공업화학회 2021 Journal of Industrial and Engineering Chemistry Vol.99 No.-
Mixed nanostructured transition metal-based complex materials with hierarchical and porousarchitectures, built from interconnected nano-building blocks, are considered as high-performancepositive electrode materials in supercapacitors (SCs). Herein, zinc–nickel–iron phosphide (ZnNiFe–P) andZn–Ni–Fe–hydroxide precursors (ZnNiFe–OH) were combined in a 3D hierarchical and porous structure(ZnNiFe–(P/OH)) to improve their durability and electrochemical activity by incorporating a dual-ligandsynergistic modulation strategy. The 3D ZnNiFe–(P/OH) architectures, comprising perfectly alignednanosheet arrays (NSA), were successfully grown on Ni foam using a facile hydrothermal processfollowed by partial phosphorization. The dual-ligand ZnNiFe–(P/OH) electrode exhibited excellentspecific capacitance/areal capacitance (1708 F g 1/5.64 F cm 2 for 1 A g 1), high rate performance (62%upto 15 A g 1) and good cycle life. Moreover, the ZnNiFe–(P/OH) NSA positive electrode was coupled withan activated carbon negative electrode to design an asymmetric supercapacitor device. The devicedelivered an excellent capacitance of187 F g 1 at 0.8 A g 1, a superior energy density of58.4 W h kg 1at 600 W kg 1, and an excellent power density of 11250 W kg 1 at 34.4 W h kg 1 while maintaining goodcycling performance (88% after 5000 cycles).
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.
보청기용 범용 이어쉘을 위한 설계 파라미터에 관한 연구
에르덴바야르(Erdenebayar-Urtnasan),전유용(Yu-Yong Jeon),박규석(Gyu-Seok Park),송영록(Young-Rok Song),이상민(Sang-Min Lee) 대한전기학회 2011 전기학회논문지 Vol.60 No.5
In this study, main parameters: aperture, first bend and second bend which express a structure of ear canal are extracted in order to modeling and manufacture the ready-made ear shells of hearing aids. The proposed parameter extraction method consists of 2 important algorithms, aperture detection and feature detection. In the aperture detection algorithm, aperture of 3-D scanned virtual ear impression and parameters relating to ear shell of hearing aid are determined. The feature detection algorithm detects first bend, second bend, and related parameters. Through these two algorithms, parameters for aperture, first bend, and second bend are extracted to model the ready-made ear shell of hearing aid. The values of these extracted parameters from 36 people’s right ear impression are analyzed and measured statistically. As a result of the analysis, it has been found that it is possible to classify ready-made ear shell parameters by age and size. The ready-made ear shell parameters are classified 3-size for 20 years old and 2-size for 60 years olde. Using 3D rhino program, virtual ready-made ear shell is reconstructed by parameters of every type, and simulated to model it. A final product was produced by transferring simulation result with rapid prototyping system. The modeled ready-made ear shell is evaluated with the objective and subjective method. Objective method is the comparison volume ratio and overlapped volume ratio of ear impression from randomly chosen 18 people and ready-made ear shell. And subjective method is that the final product of ready-made ear shell is used by users and the satisfaction number drawn from well fitting and comfortable testing was evaluated. In the result of the evaluation, it has been found that volume ration is 70%, big and middle size ready-made ear shell products are possible, and the satisfaction number is high.
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.