• Journal of Life Science
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• v.20 no.1
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• pp.71-76
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• 2010

The purpose of this study was to investigate the changes of long term ECG response in a company with middle-aged male employees. Subjects were 60 men who were 40~55 years old. We enrolled 30 exercise group subjects into a 3-year exercise program. In measurement index, body composition was measured by % body fat and BMI. Exercise stress test analyses were measured using ST/HR slope and QRS vector. Statistical analysis was performed using analysis of repeated ANOVA. Results of this study were as follows: In ST/HR slope, the control group showed symptoms of ischemia after nine minutes of exercise. In the rest frontal axis of the QRS vector, the control group had a tendency towards right axis deviation. In the rest horizontal amplitude of the QRS vector, the control group had a tendency to show a significant decrease, but it was increased significantly in the exercise group. These findings suggest that inactive company workers was showed a decrease of exercise capacity, early diagnosis exercise-induced ST depression, and prolonged deviation of QRS vector, but that cardiac function could be elevated in active middle aged men through regular exercise program participation.

• The Korean Journal of Physiology
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• v.18 no.1
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• pp.51-65
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• 1984

In an effort to elucidate the effect of physical training on the electrocardiographic amplitudes, QRS vector, axis and QRS vector amplitude, electrocardiograms were recorded before and 1, 5 and 10 minutes after 3 minute rebounder exercise in 23 healthy male students aged between 18 and 21 years in two groups of athletes and non-athletes. ECG amplitudes were measured from lead I, $V_1$ and $V_5$ and axis and amplitudes of QRS vectors were measured from lead I and III in frontal plane, from lead $V_2$ and lead $V_6$ in horizontal plane. The results obtained are summarized as follows. ECG amplitudes: The R wave amplitude was $23.38{\pm}1.14\;mm$ in athletes which was higher than $17.91{\pm}2.00\;mm$ in non-athletes. After exercise, the difference in two groups remained significant throughout the recovery period. The S wave amplitude was increased significantly, and the T wave amplitude was decreased in both groups after exercise. The P wave amplitude was increased in both groups after exercise, and it was lower in athletes than in non-athletes. The PQ segment amplitude was zero in athletes but negative in non-athletes than in the resting state. The J point amplitude was positive in resting state and was negative after exercise in both groups. J+0.08 sec point amplitude was also lowered after exercise, and it was higher in athletes than in non-athletes. Therefore the whole ST segment was proved to be decreased after exercise. The summated amplitude of R in $V_5$ plus S in $V_1$ was $38.74{\pm}2.71\;mm$ in athletes which was higher than $32.82{\pm}2.90\;mm$ in non-athletes. After exercise, it was also significantly higher in athletes than in non-athletes. Axis of QRS vector: In frontal plane, axis of QRS vector was $62.7{\pm}7.36^{\circ}$ in athletes, it showed no significant difference between the two groups. In horizontal plane, axis of QRS vector was $-23.5{\pm}7.2^{\circ}$ in athletes which was significantly higher than $-38.8{\pm}8.2^{\circ}$ in non-athletes. After exercise, it was significantly higher than the resting state in both groups. Amplitude of QRS vector : In frontal plane, amplitude of QRS vector was $13.86{\pm}1.44\;mm$ in athletes which was significantly higher than $9.62{\pm}0.97\;mm$ in non-athletes. After exercise, it was also significantly higher in athletes than in non-athletes. In horizontal plane, amplitude of QRS vector was $19.82{\pm}2.10\;mm$ in athletes which was significantly higher than $16.90{\pm}1.39\;mm$ in non-athletes. After exercise, it was also significantly higher in athletes than in non-athletes. From the above, these results indicate that R wave amplitude in athletes was significantly higher than in non-athletes before and after exercise, and that the summated amplitude of R in $V_5$ plus S in $V_1$ in athletes was also $38.74{\pm}2.71\;mm$ suggesting a left ventricular hypertrophy We should note that the PQ segment and ST segment amplitude were higher in athletes than in non-athletes, and they were decreased with exercise in both groups. In particular, the fact that amplitudes of QRS vector in frontal plane or in horizontal plane were significantly greater in athletes than in non-athletes may be an index in evaluating athletes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

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. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• 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.

• The Korean Journal of Physiology
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• v.16 no.2
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• pp.129-136
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• 1982

We studied this experiment to compare the effects of exercise and other body conditions: i.e., Flack test, cold pressor test and bicycle ergometry on the electrocardiogram. We had sixty healthy college students who were thirty nine men and twenty one women. Their $mean{\pm}SD$ values of physical characteristics were as follows: age; $22.0{\pm}1.4$, weight; men $61.7{\pm}5.6\;kg$, women $46.2{\pm}7.47\;kg$. We observed the changes of P-Q and Q-T interval, R and T amplitude, mean QRS vector, S-T segment deviation, and P and T vector. The result obtained were summarized as follows: P vector was shifted rightward regardless of the type of stress. T vector was shifted var-in each stress but in the bicycle ergometry T vector was shifted leftward. Mean QRS vector was shifted rightward immediately after the bicycle ergometry. Percentage of the occurrence of the depression of S-T segment was 21.7% at the immediately after the submaximal bicycle ergometry in lead II. The elevation of S-T segment was often observed after the mild stresses. Increased amplitude of T wave in the cold pressor test and decreased amplitude of T wave in the bicycle ergometry were observed. In the bicycle ergometry and other stresses, the precise mechanism of S-T segment changes was unexplained but insufficient repolarization in base or apex of the left ventricle due to heart strain was indicated by so called S-T vector analysis.

• 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 the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.42-49
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• 2014

We propose an individual identification method using a single-lead electrocardiogram signal. In this paper, lead I ECG is measured from subjects in various physical and psychological states. We performed a noise reduction for lead I signal as a preprocessing stage and this signal is used to acquire the representative beat waveform for individuals by utilizing the ensemble average. From the P-QRS-T waves, features are extracted to identify individuals, 19 using the duration and amplitude information, and 16 from the QRS complex acquired by applying Pan-Tompkins algorithm to the ensemble averaged waveform. To analyze the effect of each feature and to improve efficiency while maintaining the performance, Relief-F algorithm is used to select features from the 35 features extracted. Some or all of these 35 features were used in the support vector machine (SVM) learning and tests. The classification accuracy using the entire feature set was 98.34%. Experimental results show that it is possible to identify a person by features extracted from limb lead I signal only.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.63-71
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• 2011

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.

• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.449-458
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• 1996

Most of the conventional electrocardiowaphs foil to detect signals other than P-QRS-T due to the limited SNR and bandwidth. High-resolution electrocardiography(HRECG) provides better SNR and wider bandwidth for the detection of micro-potentials with higher frequency components such as vontricular late potentials(LP). We have developed a HRECG using uncorrected XYZ lead for the detection of LPs. The overall gain of the amplifier is 4000 and the bandwidth is 0.5-300Hz without using 60Hz notch filter. Three 16-bit A/D converters sample X, Y, and Z signals simultaneously with a sampling frequency of 2000Hz. Sampled data are transmitted to a PC via a DMA-controlled, optically-coupled serial communication channel. In order to further reduce the noise, we implemented a signal averaging algorithm that averaged many instances of aligned beats. The beat alignment was carried out through the use of a template matching technique that finds a location maximizing cross-correlation with a given beat tem- plate. Beat alignment error was reduced to $\pm$0.25ms. FIR high-pass filter with cut-off frequency of 40Hz was applied to remove the low frequency components of the averaged X, Y, and Z signals. QRS onset and end point were determined from the vector magnitude of the sigrlaIL and some parameters needed to detect the existence of LP were estimated. The entire system was designed for the easy application of the future research topics including the optimal lead system, filter design, new parameter extraction, etc. In the developed HRECG, without signal averaging, the noise level was less than 5$\mu$V$_rms RTI$. With signal averaging of at least 100 beats, the noise level was reduced to 0.5$\mu$V$_rms RTI$, which is low enough to detect LPs. The developed HRECG will provide a new advanced functionality to interpretive ECG analyzers.