• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.200-207
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• 2014

Arrhythmia electrocardiogram signal contains a specific unusual heartbeat with abnormal morphology. Because unusual heartbeat is useful for diagnosis and classification of various diseases, such as arrhythmia, detection of unusual heartbeat from the arrhythmic ECG signal is very important. Amplitude and kurtosis at R-peak point and RR interval are characteristics of ECG signal on R-wave. In this paper, we provide a method for detecting unusual heartbeat based on these. Through the value of the attribute deviates more from the average value if unusual heartbeat is more certainly, the proposed method detects unusual heartbeat in order using the mean and standard deviation. From 15 ECG signals of MIT-BIH arrhythmia database which has R-wave distortion, we compare the result of conventional method which uses the fixed threshold value and the result of proposed method. Throughout the experiment, the sensitivity is significantly increased to 97% from 50% using the proposed method.

• 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.37 no.8
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• pp.644-651
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• 2004

Background: Refractory atrial arrhythmias in patients late after the Fontan operation result in significant morbidity and mortality. We reviewed our experience with arrhythmia surgery in patients who had Fontan operation. Material and Method: Between July 1986 and December 2003, 275 early survivors after Fontan operation were reviewed. Fourteen patients underwent. arrhythmia surgery at reoperation after Fontan operation, and mean age at reoperation was 16.8$\pm$7.1 (range: 4.5 ∼ 30.6) years. Mechanisms of arrhythmia included atrial flutter in 8 patients, and atrial fibrillation in 2. Arrhythmia surgery has evolved from isthmus cryoablation in 12 patients to right-sided maze in 2 patients. Thirty-two patients. underwent prophylactic isthmus cryoablation concomitantly at initial Fontan operation. Result: Postoperative arrhythmias occurred in 68 patients (24.7%) among 275. There was no early and late mortality after the arrhythmia surgery. After redo Fontan operation, all patients maintained normal sinus rhythm. Atrial flutter recurred in 3 patients who had sinus conversion with medication and 7 required permanent pacemakers with a mean follow-up of 26.5$\pm$29.1 (range: 2 ∼ 73) months. All patients have improved to NYHA class I or II. After prophylactic cryoablation at initial Fontan operation, 29 patients (90.6%) had sinus rhythm, 1 patient had junctional tachycardia, 1 patient had sinus nodal dysfunction, and 1 patient had AV block with a mean follow-up of 51.3$\pm$19.8 (range: 4∼80) months. Conclusion: Redo Fontan operation, and concomitant arrhythmia surgery reduced atrial arrhythmias and improved NYHA functional classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.192-200
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• 2015

Several algorithms have been developed to classify arrhythmia which 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 classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.405-413
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• 2012

Low misclassification performance is significant with high classification accuracy for a reliable diagnosis of ECG signals, and diagnosing abnormal state as normal state can especially raises a deadly problem to a person in ECG test. In this paper, we propose detection and classification method of abnormal rhythm by rule-based rhythm classification reflecting clinical criteria for disease. Rule-based classification classifies rhythm types using rule-base for feature of rhythm section, and rule-base deduces decision results corresponding to professional materials of clinical and internal fields. Experimental results for the MIT-BIH arrhythmia database show that the applicability of proposed method is confirmed to classify rhythm types for normal sinus, paced, and various abnormal rhythms, especially without misclassification in detection aspect of abnormal rhythm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.705-711
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Journal of Chest Surgery
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• v.19 no.4
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• pp.606-617
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• 1986

During the period from November 1981 through June 1986, 18 cases of coronary arterial bypass graft were performed at Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital. They consisted of 13 males and 5 females with the mean age of 49 [range: 28-69 years]. History of myocardial infarction was noted in 50% of the patients and cardiomegaly on chest PA in 2 patients with preserved LV function. On resting EKG, except the evidences of old myocardial infarction, the findings of LVH were noted in 7 cases, acute myocardial infarction in 2, diffuse myocardial ischemia in 1, and significant ventricular arrhythmia in 2 cases. The angina by type of presentation is stable in 3 patients, unstable in 15 patients with resting, postinfarction and progressive angina as the criteria of unstability. The patterns of involvement of significant disease were single vessel involvement [5 cases] double vessel involvement [8 cases], and triple vessel involvement [5 cases] including 5 cases of left main coronary arterial diseases. The pattern of coronary arterial disease in individual patient was one or more stenosis of the proximal left coronary arterial system with or without right coronary involvement, in every case. We performed 9 cases of double bypass and 9 cases of triple bypass with great saphenous vein using single anastomosis technique except in 4 cases, One of the 4 cases is our first case, sequential anastomosis between LAD and diagonal was performed due to shortage of the prepared vein graft. In the other 3 cases, our latest experience, we adopted the left internal mammary artery for the left anterior descending coronary revascularization. The distribution of sites of distal anastomosis revealed more striking predilection to LAD, showing our attention on the significance of the revascularization of LAD system. The ischemic time was 35 minutes per graft and mean number of grafts per patient was 2.5. Of the 18 patients, 13 [77.2%] had complete revascularization, and incomplete in 5 cases with the causes of incompleteness as presented. The early results of operation were as followed: surgical death in 2 [11%], perioperative infarction 2 [11%], need of inotropic support 5 [28%], arrhythmia 2 [11%], wound problem, bleeding, and emotional dysfunction. The actuarial anginal free survival during the period of 6 months through 2 years was 85.2% with excellent symptomatic control according to the angina classification of Canadian Cardiovascular Society.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.453-456
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• 2005

일반적으로 심전도는 심장계통의 질환을 판단할 때 사용된다. 이러한 심장질환의 이상 유무를 자동으로 진단하기 위해서는 QRS파형 검출을 필요로 하며, 이를 위하여 웨이블렛변환 방법이나 템플릿매칭, 룰 베이스 방법 등 여러 가지 방법들이 쓰이고 있으나, 심전도 신호가 표준화된 형태를 갖지 않는 경우는 검출 능력에 많은 한계를 갖고 있다. 본 논문은 파형의 베이스라인(baseline)을 기준으로 진폭 값에 절대치을 취하는 방법으로 파형의 R피크값을 검출하는 알고리즘을 제안한다. 결과를 검증하기 위해 MIT-BIH 데이타베이스에서 제공하는 데이터와 R피크값을 본 논문의 알고리즘으로 추출된 R피크값과 비교한 결과 96.7%의 검출률을 보였다.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.325-327
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• 2004

ECG limb lead II signal widely used to diagnosis heart diseases and it is essential to detect ECG events (onsets, offsets and peaks of the QRS complex P wave and T wave) and extract them from ECG signal for heart diseases diagnoses. However, it is very difficult to develop standardized feature extraction formulas since ECG signals are varying on patients and disease types. In this paper, simple feature extraction method from normal and abnormal types of ECG signals is proposed. As a signal features, heart rate, PR interval, QRS interval, QT interval, interval between S wave and baseline, and T wave types are extracted. To show the validity of proposed method, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Sinus Bradycardia, and Sinus Tachycardia data from MIT-BIH arrhythmia database are used for feature extraction and the extraction results showed higher extraction capability compare to conventional formula based extraction method.