• Journal of Satellite, Information and Communications
• /
• v.9 no.1
• /
• pp.85-89
• /
• 2014

Electrocardiogram (ECG) is a diagnostic test which records the electrical activity of the heart, shows abnormal rhythms and detects heart muscle damages. With this ECG signal, medical centers diagnose patients' heart disease symptoms. A normal resting heart rate for adults rages from 60 to 100 beats a minute. An irregular heartbeat is called "arrhythmia", and arrhythmia is also called "cardiac dysrhythmia". In an arrhythmia, the heartbeat maybe too slow(slower than 60beats), too rapid(faster than 100beats), too irregular, etc. Among these symptoms of arrhythmia, if the heart beat is slower than the normal range, the symptom is called "bradycardia", and if it is faster than the range, it is called "tachycardia" In this letters, we proposed the detection algorithm of cardiac arrhythmia in ECG signal using R-R interval through the detection of R-peak.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.9
• /
• pp.43-51
• /
• 2013

Classification result by comprehensive analysis of rhythm section and heartbeat unit makes a reliable diagnosis of heart disease possible. In this paper, based on feature-points of ECG signals, rhythm analysis for constant section and heartbeat unit is conducted using rule-based classification and SVM-based classification respectively. Rhythm types are classified using a rule base deduced from clinical materials for features of rhythm section in rule-based classification, and monotonic rhythm or major abnormality heartbeats are classified using multiple SVMs trained previously for features of heartbeat unit in SVM-based classification. Experimental results for the MIT-BIH arrhythmia database show classification ratios of 68.52% by rule-based method alone and 87.04% by fusion method of rule-based and SVM-based for 11 rhythm types. The proposed fusion method is improved by about 19% through misclassification improvement for monotonic and arrangement rhythms by SVM-based method.

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

• Annual Conference of KIPS
• /
• 2017.04a
• /
• pp.669-672
• /
• 2017

최근 한국인의 주요 사망원인 중 하나로 부정맥이 부각되고 있다. 심방조기수축(PAC:Premature Atrial Contraction)은 심방이 동방결절의 명령이 있기 전에 수축해 버리는 것이다. 심방조기수축은 일시적으로 유발하였다 사라지곤 할 수 있기 때문에 심한 증상이 없다면 생명에 위협을 가하진 않지만 반대의 경우에는 위험할 수 있다. 따라서 비정상적인 심장 박동이 발생하면 이를 검출하여 조기에 부정맥을 진단할 수 있는 방법이 필요하다. 이를 위해 대상의 ECG 신호로부터 QRS패턴에 해당하는 특징들을 추출하였고 특징들을 이용하여 심방조기수축 파형을 분류한다. 오류 역전파 기반으로 특징들을 훈련하며 가중치와 바이어스값을 구한뒤 이를 이용하여 정상파형과 심방조기수축 파형을 분류한다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.10
• /
• pp.2223-2230
• /
• 2011

ECG is used to diagnose heart diseases such as myocardial ischemia, arrhythmia and myocardial infarction. Particularly, myocardial ischemia causes the shape change of the ST segment, this change is transient and may occur without symptoms. So it is important to detect the transient change of ST segment through long term monitoring. ST segment classification algorithm for making diagnosis myocardial ischemia is presented in this paper. The first step in the ST segment shape classification process is to detect R wave point and feature points based adaptive threshold and window. And then, the suggested algorithm detects the ST level change, To classify the ST segment shape, the suggested algorithm uses the slope values of the four points between the S and T wave. The ECG data in the European ST-T database were used to verify the performance of the developed algorithm. The best correct rate was 99.40% and the worst correct rate was 68.48%.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.4
• /
• pp.731-739
• /
• 2015

In arrhythmia ECG signal, abnormal beat that has various abnormal shape depending on the generation site and conduction disorders is included and it is very important to diagnose heart disease such as arrhythmia. In this paper, we propose a PVC abnormal beat detection algorithm associated with ventricular disease. The PVC abnormal beat is characterized by distortion of the QRS complex occurs among the components of the ECG signal. Therefore it is possible to detect PVC abnormal beat according to the degree of distortion of the QRS complex. First, quantify the distortion of the QRS complex by using the potential of the R-peak, kurtosis and period. By using the mean and standard deviation, PVC abnormal beat is detected depending on the degree of distortion from the normal beat. The proposed algorithm can detect the average over 98% of the AAMI-V class type abnormal beat associated with ventricular disease in MIT-BIH arrhythmia database.

• Journal of Yeungnam Medical Science
• /
• v.17 no.1
• /
• pp.49-54
• /
• 2000

Background: Echocardiography is rapidly establishing itself as the primary diagnostic technique for investigation of children with heart diseases, and referrals are increasing to the pediatric cardiology clinic for investigation. However, because there is a lack of analyzed data on the patients referred to the pediatric cardiology clinics, we have proceeded to compare and analyze their characteristics to provide basic data base. Methods: From Oct. 1, 1998 to Jul. 10, 1999, total 443 cases referred to the pediatric cardiology clinic of Yeungnam University Hospital were studied retrospectively by medical records, chest X-ray, EKG and echocardiography, etc. Results: The results were as follows. 1. The proportion of male was 61.0%(261 cases) and that of female was 39.0%(67 cases). The ratio of male to female was 1.6:1. The proportion infants less than 1 year-old was 62.6% (26R cases) of all patients. 2. Cardiac murmur was present in 248 cases(57.9%), which was the most common case of referral ed to the pediatric cardiology clinic. The impression at referral was more congenital heart disease(70.6%) than acquired heart disease(17.8%) and arrhythmia01.6%). 3. The final diagnosis was as follows : congenital heart disease was present in 212 cases(49.5%), acquired heart disease, 59 cases(13.9%); arrhythmia. 13 cases(3.0%); normal heart. 144 cases(33.6%). Conclusion: Among the patients referred to pediatric cardiology clinic, 33.6%(144 cases) had normal hearts and why these patients were referred may be possibly due to more dependence on echocardiography than on auscultation instruction. Therefore, clinical and auscultatory skill should be emphasized to minimize dependence on expensive echocardiography for evaluation of pediatric heart disease.

• Journal of Biomedical Engineering Research
• /
• v.12 no.1
• /
• pp.63-70
• /
• 1991

Design of Arrhythmia Automatic Diagnostic System Using Decision Table We have developed an arrhythmia automatic diagnostic system using decision table which is based on the criteria of Minnesota code. This system is divided into two Parts. One is wave detection algorithm using significant point extraction method, the other is arrhythmia diag- nostic algorthm. The proposed system allows physicians to diagnose more accurately by pro- viding the objective information about a lot of computer -processed ECG data.

• Journal of Biomedical Engineering Research
• /
• v.10 no.1
• /
• pp.17-24
• /
• 1989

This papaer presents a new algorithm for the P-wave detection in the ECG signal. Digital differentiation method (7-point derivative) is used for detecting P-waves exactly. This algorithm can detect various parameters of PR, PP, RR interval, which are important to diagnosis AV blocks and WPW syndrome. Especially, this algorithm can detect P-waves very efficiently not only in well-preprocessed waves but in pccr waves with noise and artifact. And it enables to develope more reliable automatic diagnosis algorithm.