• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.107-116
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• 2009

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2534-2536
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• 2004

본 논문은 유전알고리즘을 이용하여 부정맥 진단의 최적화된 입력을 구성하는 방법을 제시한다. 심전도 신호의 특징을 추출하기 위해 웨이블릿 변환이 널리 사용되고 있지만, 추출된 특징들의 선택과 최적화의 문제에 대해서는 명쾌한 해결책을 제시하지 못하고 있다. 심전도 신호는 연속 웨이블릿 변환을 이용해 5레벨로 분해되었으며, 각 서브밴드에서 추출된 계수들은 부정맥 진단을 위한 특징으로 쓰이게 된다. 웨이블릿 변환을 통해 추출된 특징들(feature)은 유전자 알고리즘과 중회귀 분석을 동하여 부정맥 진단을 위한 최적화된 특징조합이 결정되었다. 본 연구를 통해 특정레벨의 어떤 계수가 부정맥 진단에 크게 영향을 미치는지 판단할 수 있었으며 입력의 차원감소는 연산시간의 축소를 가져왔고 분류정확도를 향상시켜 분류기의 성능을 증대시켰다.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.393-395
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• 2009

심장의 활성 근육의 움직임에 의하여 발생되는 전기적 변화량을 나타내는 심전도는 부정맥 또는 허혈성 심장질환을 진단하는데 널리 활용되고 있다. 특히 심실빈맥(Ventricular Tachycardia) 또는 심실세동(Ventricular Fibrillation)과 같이 치명적인 심장리듬이 발생하기 이전에, 심실조기수축(Ventricular Premature Contraction)을 검출하여 생명을 위협할 수 있는 부정맥을 조기에 진단할 수 있는 연구들이 일부 진행되고 있다. 이에 따라서 본 연구에서는 심전도 신호의 R-R 간격 정보와 R-peak 정보의 진위성을 판단하여 PVC 부정맥 패턴뿐만 아니라 PVC 파형이 연속적으로 진행되는 PVC-RUNs을 효율적으로 검출할 수 있는 부정맥 진단 알고리즘을 제안하고자 하였다.

• Annual Conference of KIPS
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• 2024.10a
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• pp.527-528
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• 2024

부정맥은 심각한 합병증을 초래할 수 있는 심장 질환으로, 조기 진단이 중요하다. 본 연구는 부정맥 진단의 자동화를 위해 Wavelet 변환과 합성곱 신경망(CNN)을 결합한 새로운 접근 방법을 제안한다. MIT-BIH Arrhythmia Database 와 P-Wave Annotations 를 사용하여 ECG 신호에서 QRS complex 와 P-wave 를 동시에 검출하는 전처리 방법을 개발하였다. Wavelet 변환 기반 전처리와 다양한 ECG 특징 추출 기법 결합한 1 차원 CNN 모델을 적용한 결과, 93%의 전체 정확도와 평균 0.9906 의 AUC 점수를 달성하였으며, 특히 심실 부정맥에 대해 96.8%의 높은 재현율을 보였다. 이는 현재 임상에서 사용되는 많은 자동화된 ECG 분석 시스템들의 miss reading 확률(10-15%)보다 낮은 7%의 miss reading 확률을 나타낸다. 본 연구는 ECG 데이터의 효율적인 해석과 부정맥의 조기 진단 가능성을 입증하였으며, 임상 현장에서의 적용 가능성을 제시한다. 향후 연구에서는 다양한 데이터셋 검증과 실시간 처리 능력 평가를 통해 실제 임상 환경에서의 적용성을 높일 계획이다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.131-139
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• 2013

In this paper, we look into previous research in relation to each processing step for ECG diagnosis and propose detection and classification method of arrhythmia using rhythm features of ECG signal. Rhythm features for distribution of rhythm and heartbeat such as identity, regularity, etc. are extracted in feature extraction, and rhythm type is classified using rule-base constructed in advance for features of rhythm section in rhythm classification. Experimental results for all of rhythm types in the MIT-BIH arrhythmia database show detection performance of 100% for arrhythmia with only normal rhythm rule and applicability of classification for rhythm types with arrhythmia rhythm rules.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.16-22
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• 2010

Electrocardiogram(ECG) analysis and arrhythmia recognition are critical for diagnosis and treatment of ill patients. Cardiac arrhythmia is a condition in which heart beat may be irregular and presents a serious threat to the patient recovering from ventricular tachycardia (VT) and ventricular fibrillation (VF). Other arrhythmias like atrial premature contraction (APC), Premature ventricular contraction (PVC) and superventricular tachycardia (SVT) are important in diagnosing the heart diseases. This paper presented new method to classify various arrhythmias contrary to other techniques which are limited to only two or three arrhythmias. ECG is decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Burg algorithm was performed on IMFs to obtain AR coefficients which can reduce the dimension of feature vector and utilized as Multi-class SVM inputs which is basically extended from binary SVM. We chose optimal parameters for SVM classifier, applied to arrhythmias classification and achieved the accuracies of detecting NSR, APC, PVC, SVT, VT and VP were 96.8% to 99.5%. The results showed that EMD was useful for the preprocessing and feature extraction and multi-class SVM for classification of cardiac arrhythmias, with high usefulness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.37-43
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• 2020

Recently, many researches have been actively to diagnose symptoms of heart disease using ECG signal, which is an electrical signal measuring heart status. In particular, the electrocardiogram signal can be used to monitor and diagnose arrhythmias that indicates an abnormal heart status. In this paper, we proposed 1-D convolutional neural network for arrhythmias classification systems. The proposed model consists of deep 11 layers which can learn to extract features and classify 5 types of arrhythmias. The simulation results over MIT-BIH arrhythmia database show that the learned neural network has more than 99% classification accuracy. It is analyzed that the more the number of convolutional kernels the network has, the more detailed characteristics of ECG signal resulted in better performance. Moreover, we implemented a practical application based on the proposed one to classify arrythmias in real-time.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.45-50
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• 2021

With the recent development of IT technology, research and interest in various bio-signal measuring devices are increasing. But studies related to ECG(electrocardiogram), which is one of the most representative bio-signals, particularly arrhythmic signal detection, are incomplete. Since arrhythmia has various causes and has a poor prognosis after onset, preventive treatment through early diagnosis is best. However, the 24-hour Holter electrocardiogram, a tool for diagnosing arrhythmia, has disadvantages in the limitation of use time, difficulty in analyzing motion artifact due to daily life, and the user's real-time alarm function in danger. In this study, an ECG and pulse monitoring device capable of continuous measurement for a long time, a real-time monitoring app, and software for analysis were developed, and the trend of the measured values was confirmed. In future studies, research on derivation of quantitative results of ECG signal measurement analysis is required, and further research on the development of an arrhythmic signal detection algorithm based on this is required.

• Journal of Chest Surgery
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• v.31 no.11
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• pp.1056-1062
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• 1998

Background: We prospectively investigated types, incidences, and risk factors for arrhythmias after open heart surgery in adults. Materials and methods: From June 1994 to May 1995, we performed 302 cases of adult cardiac surgery at our department. This study group consisted of 150 men and 152 women, with a mean age of 43.9±28.0(range 16 to 75)years. We included all the patients irrespective of their operative types or disease entities. Results: The overall incidence of arrhythmias after open heart surgery in adults was 58.3%. The incidence of postoperative arrhythmias for redo-valvular heart surgery was 77.8%, and those for simple valvular procedure, coronary artery bypass surgery, aortic surgery, and congenital heart disease were 70.8%, 45.3%, 40.0%, and 29.5%, respectively. Eight out of twelve risk factors showed statistical significance for the development of postoperative arrhythmias. They were preoperative history of arrhythmias, antiarrhythmic drug medication, previous cardiac surgery, larger left ventricular end-diastolic, end-systolic dimension, left atrial dimension on preoperative echocardiogram, longer cardiopulmonary bypass time and aortic cross clamping time. Univariated analyses for age and types of cardioplegic solution did not show statistical significance. Conclusions: Prospective study on postoperative arrhythmias occurrence, treatment and prevention of is warrauted to draw more clear conclusion.

• The Korean Chronic Disease News
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• no.167
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• pp.10-10
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• 1994