• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1420-1428
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• 2017

It is difficult to guarantee the reliability of the algorithm due to the difference of the sampling frequency among the various ECG databases used for the R wave detection in case of applying to different environments. In this study, we propose an optimal threshold setting method for R wave detection according to the sampling frequency of ECG signals. For this purpose, preprocessing process was performed using moving average and the squaring function based the derivative. The optimal value for the peak threshold was then detected according to the sampling frequency by changing the threshold value according to the variation of the signal and the previously detected peak value. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. When the optimal values of the differential section, window size, and threshold coefficient for the MIT-BIH sampling frequency of 360 Hz were 7, 8, and 6.6, respectively, the R wave detection rate was 99.758%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.705-711
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.

• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.93-101
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• 2013

The accurate detection of R-wave is important for other feature extraction in ECG, and R-wave has a lot of medical information about heart. Numerous R-wave detection algorithms have been studied on the ECG signal shape analysis, but it was difficult to find accurate R-wave when the shape of R-wave is similar to the shape of P-wave. This paper presents an R-wave detection method based on the refractory period that is the period of depolarization and repolarization of the cell membrane after excitation. And we also use the shape of kurtosis in the refractory period. The proposed method is validated using the ECG records of the MIT-BIH arrhythmia database. Experimental results show that the proposed method significantly outperforms other method in case of 105 and 108 record that have R-wave similar to P-wave, as well as other records.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.11 no.2
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• pp.29-33
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• 2012

ECG widely used in cardiac function test is a graph that is recorded by measuring the electrical impulses occurred in the heart. Normal ECG has the form of similar sections that are repeated, and each section has the information occurred in a heart beat. Thus, In order to make the correct diagnosis, correct grasp of the sections and formed analysis must be done. In this research, a system that detects the sections of ECG is proposed. The system is based on ECG stored in the form of files. The ECG can easily have a noise caused by an outside factor. The noise of ECG is easily caused by external factors. Through a band-pass filter, it can be removed. and then, to get this ECG without a noise, interval detection algorithm using R-peak is applied. The clean, intuitive interface will help the above functions to be used without any difficulties.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.453-456
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• 2005

일반적으로 심전도는 심장계통의 질환을 판단할 때 사용된다. 이러한 심장질환의 이상 유무를 자동으로 진단하기 위해서는 QRS파형 검출을 필요로 하며, 이를 위하여 웨이블렛변환 방법이나 템플릿매칭, 룰 베이스 방법 등 여러 가지 방법들이 쓰이고 있으나, 심전도 신호가 표준화된 형태를 갖지 않는 경우는 검출 능력에 많은 한계를 갖고 있다. 본 논문은 파형의 베이스라인(baseline)을 기준으로 진폭 값에 절대치을 취하는 방법으로 파형의 R피크값을 검출하는 알고리즘을 제안한다. 결과를 검증하기 위해 MIT-BIH 데이타베이스에서 제공하는 데이터와 R피크값을 본 논문의 알고리즘으로 추출된 R피크값과 비교한 결과 96.7%의 검출률을 보였다.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.325-327
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• 2004

ECG limb lead II signal widely used to diagnosis heart diseases and it is essential to detect ECG events (onsets, offsets and peaks of the QRS complex P wave and T wave) and extract them from ECG signal for heart diseases diagnoses. However, it is very difficult to develop standardized feature extraction formulas since ECG signals are varying on patients and disease types. In this paper, simple feature extraction method from normal and abnormal types of ECG signals is proposed. As a signal features, heart rate, PR interval, QRS interval, QT interval, interval between S wave and baseline, and T wave types are extracted. To show the validity of proposed method, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Sinus Bradycardia, and Sinus Tachycardia data from MIT-BIH arrhythmia database are used for feature extraction and the extraction results showed higher extraction capability compare to conventional formula based extraction method.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.472-473
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• 2008

심실조기수축(PVC: Premature Ventricular Contraction)은 성인에게서 가장 흔하게 발생되는 심장 부정맥 증상 중의 하나이다. 심실조기수축 부정맥이 자주 발현되는 사람의 경우 관상 동맥질환, 고혈압 등의 심혈관계 질환이 진행되고 있을 가능성이 많고, 심실빈맥이나 심실세동으로 전이되는 경우 심정지 등을 유발하여 사망에 이르기 때문에 지속적으로 관찰이 필요한 증상이다. 따라서 본 연구에서는 R-R 간격 정보를 이용하여 심실조기수축 부정맥 증상을 실시간으로 검출할 수 있는 신호처리 알고리즘을 구현하고자 하였다.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.474-475
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• 2008

심실 빈맥은 심실에서 비정상적인 전기자극 발생으로 인하여 심박수가 분당 $100{\sim}120$회를 초과하는 부정맥 증상을 일컫는다. 심실 빈맥이 발생하는 경우 심박출량이 감소하고, 폐에는 미처 나가지 못한 피가 고이는 경우도 발생하여 심부전증이 나타나거나 심실 세동으로 발전하여 사망에 이를 수 있는, 매우 위험한 부정맥 중의 하나이므로 심실 빈맥 검출은 매우 중요한 사안이다. 따라서 본 연구에서는 R-R 간격 정보를 이용하여 심실 빈맥 부정맥 신호를 실시간으로 검출할 수 있는 신호처리 알고리즘을 구현하고자 하였다.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1276-1279
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• 1987

Cardiac arrhythmias are associated with electrical Instability and, hence, with abnormal mechanical activity of the heart in many cases, arrhythmias can be treated with drugs or electric shock to control and/or stop them. Hence fast arrhythmia detection is very important. In this paper RR interval, QRS width, and morphology are used for diagnosis and QRS complex is detected by hardware system. hence diagnosing time is shorten. Moreover doctors or nurses who have little knowledge of computer manipulation can get the Information of Patient's ECG by showing characteristics of abnormal waveform and by mapping graphs of RR interval .vs. QRS width and RR interval .vs. morphology on screen.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.287-288
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• 2011

본 연구에서는 마이크로프로세서 및 스마트폰 기반의 초소형, 저전력 심전도 계측 시스템에 적용하기 위한 심박동 검출 및 매트릭스 추정 기법을 이용한 실시간 이상 심전도 판별 알고리즘을 구현하였다. MIT-BIH 표준 데이터베이스를 이용하여 실시간 심전도 분석 알고리즘의 성능 평가를 수행한 결과 이상 심전도가 포함되어 있는 7개 레코드에서 심박동 검출 성공률은 99.63%, 매트릭스 추정기법을 이용한 이상 심전도 검출은 92.46%로 우수한 성능을 나타내었다.