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계층구조적 분류모델을 이용한 심전도에서의 비정상 비트 검출
이도훈,조백환,박관수,송수화,이종실,지영준,김인영,김선일,Lee, Do-Hoon,Cho, Baek-Hwan,Park, Kwan-Soo,Song, Soo-Hwa,Lee, Jong-Shill,Chee, Young-Joon,Kim, In-Young,Kim, Sun-Il 대한의용생체공학회 2008 의공학회지 Vol.29 No.6
The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don't consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.
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자극 유형에 따른 중국 내 한국어 학습자의 한국어 어두 파열음의 음향적 특성 연구
장봉춘,주문현,뢰정,송수화,주롱 한중인문학회 2021 한중인문학연구 Vol.71 No.-
본 연구는 중국 내 한국어 학습자에 의해 산출된 한국어 어두 파열음의 음향적 특성을 분 석하였다. 이를 위해 산출실험을 실행하였으며 실험 환경은 두 가지로 하였다. 하나는 주어진 자료를 보고 읽는 것이고, 다른 하나는 주어진 실험 자극을 듣고 따라 읽는 것이다. 나아가 두 실험에서 산출된 어두 파열음이 어떠한 차이를 갖는지 VOT와 후행 모음의 F0를 통해 관 찰하였다. 실험 결과는 다음과 같다. 보고읽기 실험에서 산출된 VOT는 평음-경음-격음의 삼 중 대립을 잘 나타내고, 후행 모음의 F0도 평음-경음/격음의 대립을 잘 보였다. 그러나 따라 읽기 실험에서 산출된 VOT와 후행 모음 F0는 평음과 격음 사이에서 통계적인 차이가 관찰 되지 않았다. 이는 한국어 평음의 지각적 특성이 한국어 산출에 오류를 유발하기 쉽다는 것을 의미하며, 또한 조음 방법을 중심으로 한 발음 교육의 중요성이 부각되기도 한다. The purpose of this study is to analyze the acoustic characteristics of Korean word-initial stops produced by students majoring in Korean in China. To address the goal, pronunciation assessments were performed, and the assessments consisted of two parts. One was to look at and read the material given, and the other was to listen to and repeat the experiment stimulation given. Moreover, how the word-initial stops produced at the two experiments differed was observed with the VOT and following vowel F0. The results of the experiments are as follows. The VOT produced at the experiment of looking at and reading the material given, the three-way (lenis-fortis-aspirated) phoneme distinction was well observed, and the following vowel F0, too, revealed lenis-fortis/aspirated phoneme distinction well. In the VOT produced at the experiment of listening and repeating, difference between the lenis and aspirated phonemes was not observed. This implies that the auditory perceptual features of Korean word-initial lenis phonemes do not influence Korean pronunciation negatively, and also, it stresses the importance of pronunciation education focused on the manner of articulation.
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고차통계와 Hermite 모델을 이용한 계층적 심전도 비트 분류
박관수,이종실,지영준,김인영,김선일,조백환,이도훈,송수화 대한의료정보학회 2009 Healthcare Informatics Research Vol.15 No.1
Objective: The heartbeat classification of the electrocardiogram is important in cardiac disease diagnosis. For detecting QRS complex, conventional detection algorithm have been designed to detect P, QRS, T wave, first. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. Furthermore the conventional multiclass classification method may have skewed results to the majority class, because of unbalanced data distribution. Methods: The Hermite model of the higher order statistics is good characterization methods for recognizing morphological QRS complex. We applied three morphological feature extraction methods for detecting QRS complex: higher-order statistics, Hermite basis functions and Hermite model of the higher order statistics. Hierarchical scheme tackle the unbalanced data distribution problem. We also employed a hierarchical classification method using support vector machines. Results: We compared classification methods with feature extraction methods. As a result, our mean values of sensitivity for hierarchical classification method (75.47%, 76.16% and 81.21%) give better performance than the conventional multiclass classification method (46.16%). In addition, the Hermite model of the higher order statistics gave the best results compared to the higher order statistics and the Hermite basis functions in the hierarchical classification method. Conclusion: This research suggests that the Hermite model of the higher order statistics is feasible for heartbeat feature extraction. The hierarchical classification is also feasible for heartbeat classification tasks that have the unbalanced data distribution.
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