• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.382-387
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• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.428-431
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• 1990

This paper describes a pattern classification algorithm of QRS-complexes using significant point detection for extracting features of signals. Significant point extraction was processed by zero-crossing method, and decision function based on relational spectrum was used for pattern classification of the QRS-complexes. The hierarchical AND/OR graph was obtained by decomposing the signal, and by use of this graph, QRS's patterns were classified. By using the proposed algorithm, the accuracy of pattern classification and the processing speed were improved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4443-4449
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• 2011

ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.

• 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%.

• 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.

• Journal of Sensor Science and Technology
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• v.16 no.5
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• pp.361-368
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• 2007

Small size real-time ECG signal analysis function by QRS-complex detection was put into sensor nodes. Wireless sensor nodes attached on the patient’s body transmit ECG data continuously in normal u-healthcare system. So there are heavy communication traffics between sensor nodes and gateways. New developed platform for real-time analysis of ECG signals on sensor node can be used as an advanced diagnosis and alarming system for healthcare. Sensor node does not need to transmit ECG data all the time in wireless sensor network and to server PC via gateway. When sensor node detects suspicion or abnormality in ECG, then the ECG data in the network was transmitted to the server PC for further powerful analysis. This system can reduce data packet overload and save some power in wireless sensor network. It can also increase the server performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.978-980
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• 2014

본 연구에서는 스트레스 상태 측정을 위한 심전도 신호 QRS 검출 알고리즘을 제안한다. 심전도 신호의 QRS 검출 과정은 4단계로 wavelet, moving average, squaring, threshold method로 구성된다. wavelet은 기저선 변동과 노이즈를 제거하고 moving average는 전체 신호를 부드럽게 하고 잔여 노이즈를 제거하며 squaring은 신호를 강조하는 역할을 한다. 마지막으로 threshold 기법을 이용해 검출간격을 설정하여 QRS를 검출하였다. 그 결과 Sensitivity는 99.54%, Positive Predictivity는 99.69%, Detection Error는 0.76%를 보였다. 또한, 피험자를 대상으로 게임을 이용해 스트레스 상태 변화에 대한 실험을 하였고, HRV 시간-주파수 파라미터를 분석함으로써 스트레스 상태 변화를 관찰할 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1728-1736
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• 2015

Several algorithms have been developed to classify arrhythmia which either rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. 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 extracting minimal feature. In this paper, we propose arrhythmia classification method using QRS Pattern of ECG signal according to personalized type. For this purpose, we detected R wave through the preprocessing method and define QRS pattern of ECG signal by QRS feature Also, we detect and modify by pattern classification, classified arrhythmia duplicated QRS pattern in realtime. Normal, PVC, PAC, LBBB, RBBB, Paced beat classification is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.98%, 97.22%, 95.14%, 91.47%, 94.85%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.175-176
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• 1998

The automated ECG diagnostic systems that are used in hospitals have low performance of P wave detection when faced with some diseases such as conduction block. So, the aim of this study is the improvement of detection performance in conduction block which is low in P wave detection. Median QRS-T segments were subtracted from the raw data and residual in the QRS-T regions was zeroed. After band pass filtering, we applied approximated length transformation to detect P wave.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2391-2398
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Most of the algorithms detecting PVC reported in literature is not always feasible due to the presence of noise and P wave making the detection difficult, and the process being time consuming and ineffective for real time analysis. To solve this problem, a new approach for the detection of PVC is presented based rhythm analysis and beat matching in this paper. For this purpose, the ECG signals are first processed by the usual preprocessing method and R wave was detected. The algorithm that decides beat type using the rhythm analysis of RR interval and beat matching of QRS width is developed. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate sensitivity of 99.74%, positive predictivity of 99.81% and sensitivity of 93.91%, positive predictivity of 96.48% accuracy respectively for R wave and PVC detection.