• Korean Journal of Oriental Medicine
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• v.10 no.1
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• pp.49-61
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• 2004

The Standardization of terms in The Pulse studies(脈學) is a need for development of learning. This study, for the correction of existing misused terms in The Pulse studies, we study on modernly and objectively the terms in The Pulse studies. By a focus of ${\ulcorner}$The Pulse Studies of Bin-Ho(瀕湖脈學)${\lrcorner}$, we studies on the new classification of pulse condition. The error of a existing technical books on Pulse studies begin that the classification of pulse condition is not establish a Standardization. For the correction of existing misused terms in The Pulse studies, we study on the pulse condition is expressed objectively a blood vessel that it is a subject of pulse condition. The expression of blood vessel contain a depth of blood vessel, a speed of pulsation, a curve of blood vessel, thickness of blood vessel, a diameter of blood vessel in expand and contract of blood vessel, a interval in expand and contract of blood vessel, a distinctness on a boundary of blood vessel, a speed of blood flow in blood vessel, a volume of blood flow in blood vessel, a condition of blood in blood vessel, a propelling power of blood vessel. These is standard of the new classification of pulse condition.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.555-558
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• 1999

In this paper, Ire present the result of feature points recognition and classification of radial pulse by the shape of pulse wave. The recognition algorithm use the method which runs in parallel with both the data of ECG and differential pulse simultaneously to recognize the feature points. Also we specified 3-time elements of pulse wave as main parameters for diagnosis and measured them by execution of algorithm. then we classify the shape of radial pulse by existence and position of feature points.

• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.328-334
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• 2010

Over the past few years, a considerable number of methods have been proposed and applied for the classification of photoplethysmography (PPG). Most of the previous studies, however, focused on the qualitative description of the pulse type according to specific disease and thus provided ambiguous criteria to interpreters. In order to screen out this problem, we present a quantitative method for the pulse type classification including the second derivative of photoplethysmography (SDPTG). In the PPG signal, we have classified the signal as 4 types using the position and the presence of the dicrotic wave. In addition, we have categorized the SDPTG signal as 7 types using the position and the presence of "c" and "d" wave and the sign of "c" wave. In order to check the efficacy of the proposed pulse type classification rule, we collected pulse signals from 155 subjects with different ages and sex. From the correlation analysis, Class 1(p<0.01) and Class 2(p<0.01) in the PPG signal are significantly correlated with ages. In a similar manner Class A(p<0.01), Class C(p<0.05), Class D(p<0.01), and Class F(p<0.01) in the SDPTG signal are considerably correlated with the ages. From these observations, and some earlier ones [4], [5], we can conclude that since the newly proposed method has objectivity and clarity in pulse type classification, this method can be used as an alternative of previous classification rules including similar age-related characteristics.

• Journal of Physiology & Pathology in Korean Medicine
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• v.22 no.6
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• pp.1347-1353
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• 2008

Pulse condition is the essential division for conducting pulse diagnosis which is one of the most fundamental and important diagnostics in traditional Korean/Chinese medicine. We studied the pulse condition referred to classics of traditional medicine for a full understanding in present time and come to a conclusion like below. The reference to pulse condition was concluded to 'twenty four pulse conditions' which is the fundamental conception generally accepted in present age since it had first mentioned in "Huangdi Neijing" and after it had passed through "Nanjing", "pulse pattern identification-chapter of normal pulse"of Zhang Zhongjing and reached "Maijing"of Wang Shuhe. Although medical partitioners had different views to some extent about pulse condition, there were no significant differences in the main theoretical frame. Even though there had been a diversity of opinions on the classification of pulse-condition between various medical practitioners, the method of Dae-dae and the method of systematic endeavored by Zhou Xueting and Zhou Xuehai who were medical scholars in the Ch'ing dynasty have been a criterion for the classification of pulse-condition up to date. We were able to recognize that the change of pulse condition caused by pathological situation should be compared to physiological pulse condition for detecting the deficiency and excess by researching the analyzing methods of pulse condition mentioned in the "Lingshu", and the book of Hua Shou and Zhou Xuehai). To sum up, first normal pulse which is the physiological pulse condition should be a standard for detecting physiological pulse condition. Secondly, Zhou Xueting insisted that relaxed pulse should be a standard pulse condition for detecting normal pulse.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.682-685
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• 2004

This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.263-264
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• 1998

In this paper, we present the result of feature points recognition and classification of radial pulse by the shape of pulse wave. And we analyze radial pulse in frequency domain. The recognition algorithm use the method which runs in parallel with both the data of ECG and differential pulse simultaneously to recognize the feature points. Also fie specified 3-time elements of pulse wave as main parameters for diagnosis and measured them by execution of algorithm, then we classify the shape of radial pulse by existence and position of feature points. lastly we execute frequency analysis on the feature points and get the power spectrum of radial pulse.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.158-165
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• 2018

Recently there is a great interest in the bi-static sonar. However, since the transmitter and the receiver operate on different platforms, it may be necessary to operate the system in a non-cooperative mode. In this situation, the detection and localization performance are limited. Therefore, it is necessary to classify the received pulse from the transmitter to overcome the performance limitation. In this paper, we proposed a robust automatic pulse classification method that can be applied to real systems. The proposed method eliminates the effects of noise and multipath propagation through post-processing and improves the pulse classification performance. We also verified the proposed method through the sea experimental data.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.29-38
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• 2020

In this research, we proposed the active pulse classification algorithm using multi-label convolutional neural networks for active sonar system. The proposed algorithm has the advantage of being able to acquire the information of the active pulse at a time, unlike the existing single label-based algorithm, which has several neural network structures, and also has an advantage of simplifying the learning process. In order to verify the proposed algorithm, the neural network was trained using sea experimental data. As a result of the analysis, it was confirmed that the proposed algorithm converged, and through the analysis of the confusion matrix, it was confirmed that it has excellent active pulse classification performance.

• Journal of IKEEE
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• v.22 no.3
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• pp.624-629
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• 2018

Classification of moving targets in Pulse Doppler Radar(PDR) for surveillance and reconnaissance purposes is generally carried out based on listening and training experience of Doppler audio signals by radar operator. In this paper, we proposed the automatic classification method to identify the class of moving target with Doppler audio signals using the Mel Frequency Cepstral Coefficients(MFCC) and the Hidden Markov Model(HMM) algorithm which are widely used in speech recognition and the classification performance was analyzed and verified by simulations.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.144-151
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• 2012

Chronic gastritis is the disease that is occuring in one in every 10 persons in Korea. In western medicine, endoscopy is needed to diagnose chronic gastritis, but it causes patients a pain and budget of expense. According to the TEM (Traditional Eastern Medicine), on the other hand, the 'Guan' position of the right wrist is related to a stomach. Thus we can diagnosis chronic gastritis by analyzing of pulse signal. However, pulse signal diagnosis is depended on oriental doctor's knowledge and experience. In this study, a systematic approach is proposed to analyze the computerized pulse signal. The pulse signals are firstly pre-processed, Gaussian model is adopted to fit the pulse signal, and then some related parameters are extracted from the model. Consequently, disease-sensitive parameters are selected by T-test and statistical difference. Finally, the selected parameters are entered into a Fuzzy C-Means (FCM) algorithm for classification. Classification results show that healthy persons and chronic gastritis patients are 95% and 87%, respectively.