• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1289-1295
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1587-1595
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• 2016

P wave is cardiac parameters that represent the electrical and physiological characteristics, it is very important to diagnose atrial arrhythmia. However, It is very difficult to detect because of the small size compared to R wave and the various morphology. Several methods for detecting P wave has been proposed, such as frequency analysis and non-linear approach. However, in the case of conduction abnormality such as AV block or atrial arrhythmia, detection accuracy is at the lower level. We propose P wave detection algorithm through adaptive threshold and QRS peak variability. For this purpose, we detected Q, R, S wave from noise-free ECG signal through the preprocessing method. And then we classified three pattern of P wave by peak variability and detected adaptive window and threshold. The performance of P wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 92.60%.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.3-7
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• 2001

In active sonar system using CW signal, when the noise included reverberation has not the white characteristics, the CFAR detector estimates high threshold. Because of this reason it cannot detect targets and not resolve the closely spaced multiple targets. In order to solve these problems, we propose an adaptive reverberation rejection filter The proposed filter is composed of an adaptive filter and a fixed filter with its coefficients. To study the performance of the proposed adaptive reverberation rejection filter, various experiments have been performed under In moving active sonar environments. As a results, the proposed method has the improved performance than the previous methods.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.131-135
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• 2011

In this paper, we describe the design and implementation of wireless smart plug having AC power sensor and intelligent standby power control algorithm for energy sensor network. The adaptive standby power control algorithm has function to apply different threshold of standby power by using learning algorithm depending on electric equipments. As using the proposed algorithm, user convenience will be more better and power consumption can be more reduced. The implemented prototypes of wireless smart plug and wireless access point were tested to verify the required functions and performance. As a result, we confirmed practicality of wireless smart power sensor and satisfaction of given design specifications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.137-140
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• 2010

본 연구에서는 일상생활에서 보다 편리하게 건강모니터링을 수행하기 위해 신체에 착용 가능한 심전도 및 맥파 계측 시스템을 구현하고자 하였다. 이를 위하여 배터리로 구동 가능한 초소형의 심전도 및 맥파 측정 시스템을 구현하였으며, 계측된 생체신호의 무선전송을 위해 초저전력 무선 센서네트워크 기술을 적용한 무선 생체신호 전송시스템을 구현하였다. 무선으로 전송된 심전도 및 맥파 신호는 잡음 제거 및 심박동을 검출하기 위하여 전처리과정과 적응 가변형 문턱치를 적용하였으며, 검출된 심박동으로부터 동맥순환계의 긴장도 및 유순도의 변화를 반영하는 맥파전달시간(pulse transit time, PTT)을 계산하였다. 구현된 무선 맥파전달시간 계측시스템과 기존 상용시스템의 비교 평가를 수행함으로써 구현된 시스템의 유용성을 평가하고자 하였으며, 혈압 및 맥파전달시간의 동시계측을 통해 자세 변화에 따른 혈압의 변화 및 맥파전달시간의 변화양상을 관찰함으로써 혈압과 맥파전달시간의 관계를 추정하고자 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.148-151
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• 2010

심장관련 질환은 현대사회에서 업무 과중과 스트레스에 의해 발병 가능성이 높아지고 있으며, 일상생활 중 건강상태를 지속적으로 모니터링하여 심장질환 관련 응급상황에 대처하기위한 많은 연구들이 수행되고 있다. 본 연구에서는 가정 또는 사무실에서 무구속적인 방법으로 지속적인 심장 활동상태의 모니터링이 가능한 무구속 의자형 심탄도 계측 시스템을 구현하였다. 구현된 시스템에서 계측된 심탄도 신호로부터 건강모니터링을 위한 특징성분을 검출하기위해 웨이브렛 변환과 템플릿 매칭을 혼합한 신호처리방법을 제안하였다. 또한 적응 문턱치를 통해 심탄도 신호에서 심박동을 검출하였으며 심박동의 간격으로부터 HRV(heart rate variabillity)를 계산하였다. 구현된 시스템의 성능평가를 위하여 심전도와 동시에 심탄도를 측정하였으며, 두 신호로부터 심박동 검출 성능을 비교하여 구현된 무구속 의자형 심탄도 계측 시스템의 유용성 및 무자각 건강모니터링의 가능성을 확인하였다. 또한 스트레스에 따른 HRV의 변화를 관찰하기 위하여 피실험자로부터 인위적으로 숨을 참고 강제호기를 통해 흉강내압을 증가시켜 인위적인 육체적 스트레스를 가하는 발살바를 유도하였으며, HRV의 시간 및 주파수 영역에서 도출되는 파라미터들을 평가하여 심탄도 모니터링을 통해 안정 상태와 스트레스 상태의 판별 및 무구속 건강모니터링의 가능성을 평가하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2223-2230
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• 2011

ECG is used to diagnose heart diseases such as myocardial ischemia, arrhythmia and myocardial infarction. Particularly, myocardial ischemia causes the shape change of the ST segment, this change is transient and may occur without symptoms. So it is important to detect the transient change of ST segment through long term monitoring. ST segment classification algorithm for making diagnosis myocardial ischemia is presented in this paper. The first step in the ST segment shape classification process is to detect R wave point and feature points based adaptive threshold and window. And then, the suggested algorithm detects the ST level change, To classify the ST segment shape, the suggested algorithm uses the slope values of the four points between the S and T wave. The ECG data in the European ST-T database were used to verify the performance of the developed algorithm. The best correct rate was 99.40% and the worst correct rate was 68.48%.