• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.7-11
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• 2007

The aim of this paper is to design and implement a new ECG signal monitoring and analysis method for the home care of elderly persons or patients, using wireless sensor network (WSN) technology. The wireless technology for home-care purpose gives new possibilities for monitoring of vital parameter with wearable biomedical sensors and will give the patient freedom to be mobile and still be under continuously monitoring. Developed platform for portable real-time analysis of ECG signals can be used as an advanced diagnosis and alarming system. The ECG features are used to detect life-threatening arrhythmias, with an emphasis on the software for analyzing the P-wave, QRS complex, and T-wave in ECG signals at server after receiving data from base station. Based on abnormal ECG activity, the server transfer diagnostic results and alarm conditions to a doctor's PDA. Doctor can diagnose the patients who have survived from arrhythmia diseases.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.213-216
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• 1996

In this research we would like to develop an exercise ECG signal analysis algorithm using the wavelet transform, which is possible to analyze the time and the frequency simultaneously. Wavelet transform has an advantage of dividing the nonstationary signals into the high frequency and low frequency band successively. Thus, it can separates the unnecessary noises from the frequency band of QRS complex and then using the selected frequency band we could detect the QRS complex and ST segment.

• 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.

• Journal of the Korean Institute of Oriental Medical Informatics
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• v.14 no.1
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• pp.81-106
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• 2008

The ECG which used for this paper, is analysis result from alogrisms of arrythmia, and we have studied that how we could certain Cold(寒 )type or Heat(熱) type and that Deficiency(虛) type or Excess(實)type of organs from various special diseases, and we obtained like these results. 1. we depend on our examination about Pulse(脈) because we can't discriminate arrythmia using EKG analysis instruments. 2. We obtained that Cold(寒) type diseases had wave that prolonged above normal baseline and ST wave which had downward aptitude. 3. We obtained that Heat(熱) type diseases had the fibrillation which had shortend wave that compare to normal and had downward aptitude or negative aptitude. 4. We obtained that Respiratory system (肺) diseases had wave that is within normal or is short of normal range and had much fluctuation in potential difference or trans on P wave. 5. The character of EKG which presented about diseases of gastric systems is prolonged above narmal range of wave, and the EKG had represented mixed wave with Heat(熱) type when accompany inflammatory in gastric system. 6. The wave of Blood Stasis(瘀血) type had upward aptitude of QRS wave, and the wave of anemia or blood loss type(少血 ) had downward aptitude of QRS wave, the wave which had both Cold(寒) and Heat (熱) represented mixed waves. 7. The Knotted Pulse(結脈) and Intermittent Pulse(代脈) is corresponded with sinus brady cardia, and the Swift Pulsle(疾脈) is corresponded with fibrillation. 8. We pay attention to the relations of formations about pressures pulse from formations of EMD( electromechanical dissociation ). From these results, we will have to study about EKG which using in tests of change of Parkinsons disease.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.313-316
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• 2009

A novel approach for electrocardiogram (ECG) analysis within a functional sensor node has been developed and evaluated. The main aim is to reduce data collision, traffic over loads and power consumption in healthcare applications of wireless sensor networks (WSN). The sensor node attached on the patient's bodysurface around the heart can perform ECG analysis based on a QRS detection algorithm to detect abnormal condition of the patient. Data transfer is activated only after detected abnormality in the ECG. This system can reduce packet loss during transmission by reducing traffic overload. In addition, it saves power supply energy leading to more reliable, cheap and user-friendly operation in the WSN based ubiquitous health monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• The Korean Journal of Physiology
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• v.19 no.1
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• pp.61-72
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• 1985

In order to compare the cardiac function of various groups of athletes, the resting electrocardiographic time intervals, amplitudes and vectors were analyzed in high school athletes of throwing(n=7), jumping(n=11), short track(n=8), long track(n=14), boxing(n=7), volleyball(n=8) and baseball(n=9), and nonathletic control students(n= 19). All athletic groups showed a significantly longer R-R interval(0.96-1.09 sec) than the controls (0.78 sec). Therefore, the heart rate was significantly slower in atheletes than in the control, but was not different among the different athletic groups. R-R interval is the sum of intervals of P-R, 0-T and T-P: P-R and Q-T intervals showed no difference among the control and athletic groups, but T-P interval in the jump, short track, long track and boxing groups was significantly higher than the control. R-B interval showed a significant correlation with T-P or Q-T intervals but no correlation with P-R or QRS complex. Comparing the amplitude of electrocardiographic waves, the athletic groups showed a lower trend in P wave than the controls. T wave in lead $V_5\;(Tv_5)$ was similar in the athletic and control groups. The long track group showed a significantly higher waves of $Rv_5$, $Sv_1$, and the sum of $Rv_5$ and $Sv_1$ than not only the controls but also the other athletic group. The angles of P, QRS, and T vector in the frontal and horizontal planes were not different among the control and all the athletic groups. Each athletic group stowed a lower trend in amplitude of P vector in the frontal plane, but in horizontal plane, throwing, jump, short track and baseball groups showed a significantly lower than the controls. The amplitude of QRS and T vector was similar in the athletic and control groups, but only the baseball group showed a significantly higher QRS vector in the frontal plane. In taken together, all the athletic groups showed a slower heart rate than the controls, mainly because of elongated T-P interval. Comparing the electrocardiographic waves and vector, the athletic groups showed lower amplitudes of P wave and P vector than the controls. Values of $Rv_5$ and $Sv_1$ strongly suggest that only the long distance runners among the various athletic groups developed a left ventricular hypertrophy.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.633-636
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• 1986

We have developed an automated ECG interpretation technique by micro-computer for rapid analysis of ECG data in the clinical hospital. This system is consisted of 16 bit microcomputer including bandpass-filter, comparator, A/D converter in order to obtain the QRS width, R-R interval.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1034-1042
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• 2016

The detection of P-waves and T-wave in the electrocardiogram signal analysis is an important issue. But the accuracy of the boundary detection algorithm is an insufficient level in the change of slow transition in the signal compared to the QRS complex. This study proposes an algorithm to detect P-wave and T-wave sequentially after determining local baseline using QRS complex. First, we detected the peak points based on local baseline and determined the onset and offset through the calculation of the area of the section. After modifying the baseline using detected waveform, we detected the other waveform in the same way and separated the P-wave and the T-wave based on the location. We used the PhysioNet QT database to evaluate the performances of the algorithm, and calculate the mean and the standard deviations. The experiment results show that standard deviations are under the tolerances accepted by expert physicians, and outperform the results obtained by the other algorithms.