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Time-Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Birth
Ryu, Jiwoo,Park, Cheolsoo The Institute of Electronics and Information Engin 2015 IEIE Transactions on Smart Processing & Computing Vol.4 No.2
In this paper, a novel method for the classification of term and preterm birth is proposed based on time-frequency analysis of electrohysterogram (EHG) using multivariate empirical mode decomposition (MEMD). EHG is a promising study for preterm birth prediction, because it is low-cost and accurate compared to other preterm birth prediction methods, such as tocodynamometry (TOCO). Previous studies on preterm birth prediction applied prefilterings based on Fourier analysis of an EHG, followed by feature extraction and classification, even though Fourier analysis is suboptimal to biomedical signals, such as EHG, because of its nonlinearity and nonstationarity. Therefore, the proposed method applies prefiltering based on MEMD instead of Fourier-based prefilters before extracting the sample entropy feature and classifying the term and preterm birth groups. For the evaluation, the Physionet term-preterm EHG database was used where the proposed method and Fourier prefiltering-based method were adopted for comparative study. The result showed that the area under curve (AUC) of the receiver operating characteristic (ROC) was increased by 0.0351 when MEMD was used instead of the Fourier-based prefilter.
Flexible and Printed PPG Sensors for Estimation of Drowsiness
Ryu, Gi-Seong,You, Jiwoo,Kostianovskii, Vladislav,Lee, Eung-Bin,Kim, Youngjoo,Park, Cheolsoo,Noh, Yong-Young Institute of Electrical and Electronics Engineers 2018 IEEE transactions on electron devices Vol.65 No.7
<P>We report printed flexible optoelectronic sensors composed of red organic light-emitting diodes (OLEDs) and organic photodiodes (OPDs) for detection of various biological signals in a photoplethysmograph (PPG) device. Fabricated flexible OLEDs achieved maximum luminance >1000 cd/m<SUP>2</SUP> at 9 V, with peak at 640 nm. Maximum flexible OPD photosensitivity for the poly(3-hexylthiophene-2, 5-diyl) and phenyl-C61-butyric acid methyl ester (PCBM) heterojunction is <TEX>$ {2} \times {10}^{{2}}$</TEX> at 0 V and 1.76 at −1 V, irradiated with 1.2 mW/cm<SUP>2</SUP> at 660 nm. The diketopyrrolopyrrole thieno [3,2-b]thiophene blended with PCBM OPDs with poly (3,4-ethylenedioxythiophene):polystyrene sulfonate anode showed photosensitivity = 84 at −1 V bias to almost <TEX>${6} \times {10}^{{4}}$</TEX> at 0 V accompanied by low dark current ( <TEX>${9.5} \times {10}^{-{8}}$</TEX> A/cm<SUP>2</SUP> at −1 V). PPG signals were successfully detected using the developed flexible PPG sensor and the conventional driving circuit. Human studies were conducted to evaluate the flexible PPG sensor performance in practical applications. Subject drowsiness was estimated from heart rate variability, extracted from the PPG signals, using machine learning algorithms. The flexible PPG sensor achieved 79.2% accuracy and 72.1% area under the receiver (AUC) to predict drowsiness (60-s window), which are meaningful results compared with conventional PPG sensors (83.3% accuracy and 69% AUC). Drowsiness estimation experiments using two PPG signals showed that the flexible PPG sensor achieved similar or better performance compared to conventional PPG sensors.</P>
Low-complexity generalized residual prediction for SHVC
Kim, Kyeonghye,Jiwoo, Ryu,Donggyu, Sim The Institute of Electronics and Information Engin 2013 IEIE Transactions on Smart Processing & Computing Vol.2 No.6
This paper proposes a simplified generalized residual prediction (GRP) that reduces the computational complexity of spatial scalability in scalable high efficiency video coding (SHVC). GRP is a coding tool to improve the inter prediction by adding a residual signal to the inter predictor. The residual signal was created by carrying out motion compensation (MC) of both the enhancement layer (EL) and up-sampled reference layer (RL) with the motion vector (MV) of the EL. In the MC process, interpolation of the EL and the up-sampled RL are required when the MV of the EL has sub-pel accuracy. Because the up-sampled RL has few high frequency components, interpolation of the up-sampled RL does not give significantly new information. Therefore, the proposed method reduces the computational complexity of the GRP by skipping the interpolation of the up-sampled RL. The experiment on SHVC software (SHM-2.0) showed that the proposed method reduces the decoding time by 10 % compared to conventional GRP. The BD-rate loss of the proposed method was as low as 1.0% on the top of SHM-2.0.
Kim, Youngjoo,Ryu, Jiwoo,Kim, Ko Keun,Took, Clive C.,Mandic, Danilo P.,Park, Cheolsoo Hindawi Publishing Corporation 2016 Computational intelligence and neuroscience Vol.2016 No.-
<P>Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.</P>
Endan Li,Jiwoo Choi,Hye-Ri Sim,Jiyeon Kim,Jae Hyun Jun,Jangbeen Kyung,Nina Ha,Keun Ho Ryu,Seung Soo Chung,Hyun Sook Kim,Semi Kim,Sungsu Lee,Wongi Seol,송지환 생화학분자생물학회 2023 BMB Reports Vol.56 No.3
Huntington’s disease (HD) is a neurodegenerative disorder, ofwhich pathogenesis is caused by a polyglutamine expansion inthe amino-terminus of huntingtin gene that resulted in the aggregationof mutant HTT proteins. HD is characterized by progressivemotor dysfunction, cognitive impairment and neuropsychiatricdisturbances. Histone deacetylase 6 (HDAC6), amicrotubule-associated deacetylase, has been shown to inducetransport- and release-defect phenotypes in HD models, whilsttreatment with HDAC6 inhibitors ameliorates the phenotypiceffects of HD by increasing the levels of α-tubulin acetylation,as well as decreasing the accumulation of mutant huntingtin(mHTT) aggregates, suggesting HDAC6 inhibitor as a HD therapeutics. In this study, we employed in vitro neural stem cell(NSC) model and in vivo YAC128 transgenic (TG) mouse modelof HD to test the effect of a novel HDAC6 selective inhibitor,CKD-504, developed by Chong Kun Dang (CKD PharmaceuticalCorp., Korea). We found that treatment of CKD-504 increasedtubulin acetylation, microtubule stabilization, axonaltransport, and the decrease of mutant huntingtin protein invitro. From in vivo study, we observed CKD-504 improved thepathology of Huntington’s disease: alleviated behavioral deficits,increased axonal transport and number of neurons, restoredsynaptic function in corticostriatal (CS) circuit, reduced mHTTaccumulation, inflammation and tau hyperphosphorylation inYAC128 TG mouse model. These novel results highlight CKD-504as a potential therapeutic strategy in HD.