• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.125-132
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• 2007

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM), NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The eight most important coefficients of d3 and d4 are selected by the non-overlap area distribution measurement method. The selected 8 coefficients are used for 3 data sets showing reliable accuracy rates 99,80%, 99,21%, and 98.78%, respectively, which means the selected input features are less dependent to the data sets. The ECG signal segments and fuzzy membership functions of the 8 coefficients enable input features to interpret explicitly.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.7-14
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• 2021

With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.

• Journal of Chest Surgery
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• v.38 no.2 s.247
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• pp.110-115
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• 2005

Background: The aims of this paper were to review the mid term clinical results and to analyze the preoperative risk factors of isolated aortic valve replacement (AVR). Material and Method: Between January 1992 and February 2003, 80 patients underwent isolated AVR. 58 were male and 22 were female patients, raging from 12 to 75 years of age (mean :$46.8{\pm}13.0$ years). 74 patients except one early death and 5 follow-up loss were contacted by OPD or by telephone. The mean duration of follow-up was $44.2{\pm}29.7$ months and the total cumulative period was 272.8 patient-year. Result: The complications in hospital occurred in 35 cases : 12 wound problems (11 superficial, 1 deep), 11 arrhythmias (9 temporary, 2 persistent), 3 low cardiac output, and so forth. The late deaths were 4 cases : the heart-related deaths were 2 cases ($0.7\%$ patient-year). Conclusion: The risk factors that influenced the early mortality and morbidity were older age (> 60 years)(p=0.04), poor preoperative NYHA functional class (> 3) (p=0.048), high preoperative serum creatinin level (> 1.2 mg/100 ml)(p=0.031), long operation time (aortic clamping time>90 min)(p=0.042). The same factors influenced the late mortality and morbidity. Freedom from valve-related complication was $86.4{\pm}5.3\%,$ actuarial survival rate were $96.8{\pm}2.3\%$ at 3 years and $90.8{\pm}4.6\%$ at 10 years.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.341-348
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• 2011

ECG(Electrocardiogram), a field of Bio-signal, is generally experimented with classification algorithms most of which are SVM(Support Vector Machine), MLP(Multilayer Perceptron). But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm. The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed. As a result, modified RF Classifier showed better consequences than SVM classifier, MLP classifier and other researches' results in accuracy category. The Band-pass filter is used to extract R-R interval in pre-processing stage. However, the Wavelet transform, median filter, and finite impulse response filter in addition to Band-pass filter are often used in experiment of ECG. After this study, selection of the filters efficiently deleting the baseline wandering in pre-processing stage and study of the methods correctly extracting the R-R interval are needed.

• 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.

• Journal of Chest Surgery
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• v.29 no.8
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• pp.875-882
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• 1996

Multlvalvular heart surgery was performed In 78 cases, in the Department of Thoracic & Cardiovascu far Surgery of Chonbuk national University Hospital from november 1983 to March 1994. There Where 31 men and 47 women. whose ranged from 14 to 63 years. The causes of the valvular lesions were 57 rheumatic origin, 18 degenerative, 1 previous endocarditls, 1 prosthetic valve mal-function. There were 25 double valve replacement with or wit out tricuspid valve repair, i M VR and aortic valve repair, 18 MVR and tricuspid valve repair, 1 MVR and aortic and tricuspid valve repair, 10 AVR and mi- tral valve repair, 1 AVR and tricuspid valve repair, 8 mitral aortic valve repair, 13 mitral and tricuspid valve repair. They were improved mean New York Heart Association functional cldss, from 2.72% 121 Early deaths were 5 cases(6.4%). The cause of death wet'e low cArdiac output syndrome. veritricular tachycardia, massive bleeding and cerebral thromboembolism. All the survivors belonged to New York Heart Association functional class I or ll at discharge. The patients who had had valve replacement operation were medicated with warfarin to maintain the level of 30∼ 50% of normal prothrombin time. During follow-up(93.6%, mean 49.9 months), 2 late deaths were developed. One was due to intracranial hemorrhage and the other congestive heart failure. The pre-operative New York Heart Association Functional class IV was statistically sig ificant operat- ive risk factors(p< 0.05).

• Journal of Chest Surgery
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• v.39 no.5 s.262
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• pp.366-375
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• 2006

Background: Hypomagnesemia is a common complication after cardiac surgery with cardiopulmonary bypass. The purpose of this study was to assess the clinical beneficial effect of administration of magnesium sulfate in cardiac surgery. Material and Method: Thirty five patients scheduled for elective cardiac surgery were randomly assigned to magnesium group (n=20) which received magnesium sulfate in priming solution (1 g) and cardioplegic solution (1 g) or control group (n=15) which did not receive it. Arterial blood samples were drawn for measuring $Mg^{++}$ and electrolytes contents, blood gas analysis, CBC, total protein, albumin, blood urea nitrogen (BUN), creatinine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, tumor necrosis factor-${\alpha}$ $(TNF-{\alpha})$, interleukin-6 (IL-6), interleukin-10 (IL-10), creatine phosphokinase (CpK), creatine kinase-MB (CK-MB), lactate dehydrogenase(LDH), troponin-1 (TNI), prothrombin time (PT) and activated pratial thromboplastin time level (aPTT). Venous blood samples were drawn before and after the operation for measuring activated clotting time level (ACT). Result: $Mg^{++}$ levels in magensium group were higher than those of control group at intraoperative and post-operative periods (p<0.05). dysrhythmias were lower in magnesium group (8 cases out of 17 patients, 46.4%) than in control group (10 cases out of 10, 100%, p=0.050). Conclusion: These results showed that administration of low dose magnesium sulfate during cardiac surgery prevented hypomagnesemia and lowered incidence of dysrhythmia.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2551-2561
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• 2014

Several algorithms have been developed to detect AFIB(Atrial Fibrillation) which either rely on the linear and frequency analysis. But they are more complex than time time domain algorithm and difficult to get the consistent rule of irregular RR interval rhythm. In this study, we propose algorithm for optimal value detection of irregular RR interval for AFIB classification based on linear analysis. For this purpose, we detected R wave, RR interval, from noise-free ECG signal through the preprocessing process and subtractive operation method. Also, we set scope for segment length and detected optimal value and then classified AFIB in realtime through liniar analysis such as absolute deviation and absolute difference. The performance of proposed algorithm for AFIB classification is evaluated by using MIT-BIH arrhythmia and AFIB database. The optimal value indicate ${\alpha}=0.75$, ${\beta}=1.4$, ${\gamma}=300ms$ in AFIB classification.

• Journal of Chest Surgery
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• v.39 no.9 s.266
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• pp.681-691
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• 2006

Background: The aim of this study was to investigate the mid-term outcomes of our modifications to the maze procedure using cryoablation for treating atrial fibrillation associated with rheumatic mitral valve disease. Material and Method: Between March 2000 and February 2004, 177 consecutive patients underwent the modified maze procedure with the use of cryoablation concomitant with mitral valve surgery for atrial fibrillation associated with rheumatic mitral valve disease, and were divided into three groups: (1) modified Cox-maze III (CM group, n=88): (2) modified Kosakai-maze (KM group, n=63): and (3) left atrial maze procedure (LA group, n=26). The postoperative and follow-up results were analyzed and compared between the groups. Result: There were three hospital deaths (1.7%) and no significant differences in the incidence of postoperative complications between the three groups. The operative time, such as the cardiopulmonary bypass and aortic crossclamp time, were significantly longer in the CM group than in the KM and LA groups, respectively (p<0.0001). The mean follow-up was $22.4{\pm}15.1$ months ($1\sim52.6$ months) for all patients. One late death developed in the CM group (0.0%). At last follow-up, 139 patients exhibited sinus rhythm (79.9%), which was also regained in 67 patients (77.9%) in the CM group, 50 (80.7%) in the KM group and 22 (84.6%) in the LA group (p=0.743). The actuarial freedom from stroke at 4 years was $84.5{\pm}9.4%$ in the CM group, $95.0{\pm}4.9%$ in the KM group, and $92.9{\pm}6.9%$ in the LA group (p=0.916). Conclusion: The modified maze procedure using cryoablation is safe and effective in treating chronic atrial fibrillation associated with rheumatic mitral valve disease.