• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.129-135
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• 2004

In the home health management system, we often face the situation to handle biological signals that are frequently measured from normal subjects. In such a case, it is necessary to decide whether the signal at a certain moment is normal or abnormal. Since ECC is one of the most frequently measured biological signals, we describe algorithms that detect QRS-complex and decide whether it is normal or abnormal. The developed QRS detection algorithm is a simplified version of the conventional algorithm providing enough performance for the proposed application. The developed classification algorithm that detects abnormal from mostly normal beats is based on QRS width, R-R interval and QRS shape parameter using Karhunen-Loeve transformation. The simplified QRS detector correctly detected about 99% of all beats in the MTT/BIH ECG database. The classification algorithm correctly classified about 96% of beats as normal or abnormal. The QRS detection and classification algorithm described in this paper could be used in home health management system.

• Journal of Biomedical Engineering Research
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• v.21 no.2
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• pp.165-174
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• 2000

본 논문에서는 M 채널 필터 뱅크를 이용하여 심전도 자동 진단 시스템에서 매우 중요한 파라미터로 사용되는 QRS complex 검출을 실시하였다. 제안된 알고리즘에서는 심전도 신호를 M개의 균일한 주파수 대역으로 분할(decomposition)하고, 분할된 서브밴드(subband) 신호들 중에서 QRS complex의 에너지 분포가 가장 많이 존재하는 5∼25Hz 영역의 서브밴드 신호들을 선택하여 feature를 계산함으로써 QRS complex 검출을 실시하였다. 제안된 알고리즘의 성능 비교를 위하여 MIT-BIH arrhythmia database를 사용하였으며, sensitivity는 99.82%, positive predictivity는 99.82, 평균 검출율은 99.67%로 기존의 알고리즘에 비해 높은 검출 성능을 나타내었다. 또한 polyphase representation을 이용하여 M 채널 필터 뱅크를 구현한 결과 연산 시간이 단추되어 실시간 검출이 가능함을 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.3
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• pp.40-47
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• 2010

In this study, we investigated the performance of real-time QRS complex detection algorithm in physio-module for echocardiography. The performance of QRS detection module in echocardiography was evaluated according to international standard, EC-13 and we compared with commercialized physio-module with QRS complex detection. In this study, we can get performance of QRS complex detection, pacer pulse detection, Tall t-wave rejection and arrhythmia detection within EC-13's criteria and we can get improved QRS trigger delay time and baseline wondering rejection times in compared with commercialized physio-module.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.483-486
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• 2000

심전도에서 QRS complex와 R-wave의 검출은 부정맥 진단, 심전도의 특성점 검출 기준, heart rate variability(HRV) 측정에 있어서 중요하나, 시시각각 변화하는 생리적 변화와 여러 가지 노이즈로 인해 검출이 쉽지 않다 제안된 알고리듬에서는 wavelet filter banks를 이용하여 대칭적 enhanced 신호와 noise 와 같은 very high frequency 성분이 제거된 ECG에 근사화 된 approximated 신호를 얻는다. Enhanced 신호로부터 QRS complex의 위치를 검출하고, 검출된 위치의 주변에서 대칭적 wavelet의 특성이 반영된 dominant한 peak의 위치정보, 즉 R wave의 후보점을 얻는다. 이 위치 정보를 이용하여 enhanced 신호에서 각 peak에서의 크기, approxi-mated 신호에서 각 peak 주변에서의 기울기 변화, 기울기 부호 등을 고려하여 R-wave의 위치를 원래의 ECG 신호에서 얻는다. MIT/BIH database에 적용한 결과 99.6%의 QRS complex검출률과 92.9%의 R-wave 검출률을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1531-1539
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• 2014

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons's physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.945-952
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• 2013

The R wave of QRS complex is the most prominent feature in ECG because of its specific shape; therefore it is taken as a reference in ECG feature extraction. But R wave detection suffers from the fact that frequency bands of the noise/other components such as P/T waves overlap with that of QRS complex. ECG signal processing must consider efficiency for hardware and software resources available in processing for miniaturization and low power. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, efficient QRS detection based on SOM(Subtractive Operation Method) is presented in this paper. For this purpose, we detected R wave through the preprocessing method using morphological filter, empirical threshold, and subtractive signal. Also, we applied dynamic backward searching method for efficient detection. The performance of R wave detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41% in R wave detection.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.49-56
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• 1995

This paper is a study on the design of adptive filter for QRS complex detection. We propose a simple adaptive algorithm to increase capability of noise cancelation in QRS complex detection with two stage adaptive filter. At the first stage, background noise is removed and at the next stage, only spectrum of QRS complex components is passed. Two adaptive filters can afford to keep track of the changes of both noise and QRS complex. Each adaptive filter consists of prediction error filter and FIR filter. The impulse response of FIR filter uses coefficients of prediction error filter. The detection rates for 105 and 108 of MIT/BIH data base were 99.3% and 97.4% respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.450-460
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• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.3
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• pp.126-131
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• 2014

In this study, QRS complex is detected by Wavelet Transform and it can be worked in 32bit fixed point operation thought optimization. First, ECG signal is passed though band pass filter. Second, it is transformed using one-band combined wavelet function from 3-band wavelet function. Third, it is passed though moving window integral. Finally, QRS complex is detected by decision rule. The proposed algorithm is evaluated using MIT-BIH arrhythmia database. Its all of process make progress 32-bit fixed-point operation and it makes table that high complexity operations like trigonometrical function. The detection algorithm evaluate through computer simulation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.45-50
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• 2010

In this study, Discrete Wavelet Transform(DWT), which can detect more correct QRS complex, approximated through impulse response for reducing complexity to suit real-time system during exercise. Also, rhythm information, which is Arrythmia, Bradycardia and Tachycardia, is provided through continuously monitoring QRS complex. Proposed algorithm is evaluated by computer simulation of ECG signal that is measured during exercise.