• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.378-382
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• 2006

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The two most important coefficients are selected by the non-overlap area distribution measurement method to minimize the classification rules that show PVC classification rate of 99.90%. By Presenting locations of the extracted two coefficients based on the R wave location, it is shown that PVC can be detected using only information of the two portions.

• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.125-132
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• 2007

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM), NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The eight most important coefficients of d3 and d4 are selected by the non-overlap area distribution measurement method. The selected 8 coefficients are used for 3 data sets showing reliable accuracy rates 99,80%, 99,21%, and 98.78%, respectively, which means the selected input features are less dependent to the data sets. The ECG signal segments and fuzzy membership functions of the 8 coefficients enable input features to interpret explicitly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1947-1954
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• 2013

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose arrhythmia detection based on binary coding using QRS feature varibility. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.245-254
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• 2013

In oriental medicine, it is possible to classify and treat many diseases using the pulse wave detection system. Other problems may arise. As it is a very subjective way to analyze the pulse wave. One problem of the conventional pulse wave detection system is that the arterial pulse sensor is not located correctly at the radial artery. Threrefore measurement results can differ depending on the measurement position and the measurement procedure. This is mostly due to it's sensitivity to high reproducibility. In order to solve this problem this paper proposes an algorithm to analyze the weak pulse wave symptom and strong pulse wave symptom. It uses the portable pulse wave detection system which includes a Hall Sensor. As a final result, it analyzed the weak pulse wave symptom and strong pulse wave symptom by the SPSS statistics technique. It proves that N time (notch point time) and S Amp (rise waveform size) mean values are significantly different in 95% confidence interval.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.222-227
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• 1997

To diagnose cardiac arrhythmia owing to reentry mechanism, cardiac conduction system was modeled by modified Hidden Markov modeled by evaluated. First, simulation of transient conduction states and output waves were made with initially assumed parametric values of cardiac muscle repolariztion time, conduction velocity and its automaticity. The output was a series of onset time and the name of the wave. Parameters determined the rate of beating, lengths of wave intervals, rate of abnormal beats, and the like. Several parameter sets were found to simulate normal sinus rhythm, supraventricular /ventricular tachycardia, atrial /vetricular extrasystole, etc. Then, utilizing the estimation theorems of Hidden Markov Model, the best conduction path was estimated given the previous output. With this modified estimation method, close matching between the simulated conduction path and the estimated one was confirmed.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.105-112
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• 2012

Most methods for detecting PVC and APC require the measurement of accurate QRS complex, P wave and T wave. In this study, we propose new algorithm for detecting PVC and APC without using complex parameter and algorithms. Proposed algorithm have wide applicability to abnormal waveform by personal distinction and difference as well as all sorts of normal waveform on ECG. To achieve this, we separate ECG signal into each unit patterns and made a standard unit pattern by just using unit patterns which have normal R-R internal. After that, we detect PVC and APC by using similarity analysis for pattern matching between standard unit pattern and each unit patterns.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.962-967
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• 2014

In this paper, through a digital potentiometer and exponentially weighted moving average filter, pulse and PPG waveform measurable device was fabricated in radial artery. If this device is not proper about signal size in analog part, MCU can judge easily by adjusted amplification through digital potentiometer, using exponentially weighted moving average filter is able to filter out more clear value of ADC. I presumed pulse rate as value of measuring time between point of maximum contraction from sensing signal in radial artery of wrist. Therefore, this means can measure stable pulse rate and PPG waveform, finger as well as radial artery, whether signal size of each person is different finger as well as radial artery.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.495-503
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• 2000

스트레스 심전계에서 발생되는 근잡음을 제거하기 위하여 wavelet interpolation filter(WIF)를 설계하였다. WIF는 크게 웨이브렛 변환부와 보간법 적용부로 구성되어 있다. 웨이브렛 변환부는 Haar 웨이브렛을 이용하였으며 심전도 저주파 영역과 고주파 영역으로 분할하는 과정이다. 보간법 적용부에서는 분할되어진 신호 중 A3을 선택하여 신호의 재생 성능을 향상시키기 위하여 보간법을 적용하였다. WIF의 성능을 평가하기 위해서 신호대 잡음비, 재생신호 자승오차 및 표준편차의 파라미터를 이용하였다. 본 실험에서는 MIT/BIH 부정맥 데이터베이스, European ST-T 데이터베이스 및 삼각파형을 이용하여 성능 파라미터를 측정하였다. 결과적으로 WIF는 성능 파라미터에서 기존에 많이 사용되고 있는 평균값 필터, 중간값 필터 및 hard thresholding 방법에 비해 우수함을 알 수 있었다.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.505-512
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• 2000

기계식 인공심장판막을 통한 혈액의 유동과 이 유동에 관련된 판첨의 거동특성을 수치해석기법을 이용하여 연구하였다. 혈액은 맥동류, 층류, 비압축성 유동으로 가정하였으며 유체-고체의 상호작용을 고려하기 위하여 혈액의 유동방정식과 고체의 운동방정식이 동시에 계산되었다. 심실과 대동맥에서의 압력파형을 경계조건으로 사용하였다. 연구의 결과로서 혈액유동과 판첨의 거동이 예측되었으며, 판막을 통한 3개의 제트가 발견되었으며 vortex가 판첨의 끝단에서 발생하여 하부로 흘러가는 것이 관찰되었다. 판첨의 닫힘 거동은 열림 거동에 비하여 2배정도 빠르게 진행되었으며 sinus에서 2개의 큰 vortex가 관찰되었다. 유체-고체 상호작용을 고려하는 본 연구방법은 향후 판막의 연구와 개발에 매우 유용할 것으로 판단된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1826-1829
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• 2015

본 연구는 측정된 심전도와 광용적맥파의 박동특징점을 무결하게 검출하기 위한 프로그램 개발에 관한 것이다. 개발된 프로그램은 생체신호 계측기에서 측정된 생체신호 데이터를 자동 및 수동으로 분석하여 박동특징점의 시간 및 값 정보를 추출 및 저장하는 프로그램으로, 측정된 파형 및 검출된 특징점 위치, 인접한 특징점간 시간 간격을 시각적으로 전달할 수 있는 GUI(Graphic User Interface)를 포함한다. 개발된 프로그램은 기존 연구에서 제시된 심박동, 맥파의 박동 특징점 거출 알고리즘을 사용하여 최초 검출을 수행하고, GUI와 연동되는 컨텍스트 메뉴를 통해 오검출 또는 미검출 정보를 효율적으로 수정할 수 있도록 함으로 비전문가에 의한 쉽고 효율적인 사용이 가능하다.