• Journal of Sensor Science and Technology
• /
• v.11 no.4
• /
• pp.230-238
• /
• 2002

In this paper, we designed a fuzzy system using the wavelet coefficients to detection the PVCs effectively and to increase the accuracy of decision of the arrhythmia. In the proposed Fuzzy system, the QRS complex of ECG signal is divided into 6th level frequence bands by wavelet transform using Haar wavelet. The MIT/BIH database for the source of input signal is used in order to evaluate the performance of the proposed system. From the simulation results, the decision of membership functions for PVCs and heart rates by using Fuzzy rules, we detected the abnormal values effectively by application of leaned from neural network and we also found results in classification ratio of 95% the decision of arrhythmia.

• Journal of Korean Critical Care Nursing
• /
• v.7 no.2
• /
• pp.14-23
• /
• 2014

Purpose: The purpose of this study was to provide basic information to develop appropriate nursing guidelines for cardiac telemetry. Methods: This retrospective research was conducted to identify the current usage of cardiac telemetry and considered 1,000 patients hospitalized for telemetry. The collected data were analyzed using IBM (SPSS Statistics for Windows 21.0). Results: Four-hundred and ninety-two patients (49.2%) were diagnosed with arrhythmia and 209 (20.9%) with heart failure. Electrocardiogram (ECG) rhythm changes were detected via telemetry in 464 cases. Major arrhythmias were ventricular tachycardia (183, 39.4%) and bradycardia (99, 21.3%). Interventions after detecting arrhythmia were cardioversion (16, 3.4%), defibrillation (1, 0.2%), and cardiopulmonary cerebral resuscitation (5, 1.1%); other patients were treated conservatively with close observation (381, 82.1%). Conclusion: There was appropriate intervention in 46.4% of the considered cases with the detection of ECG changes before patient notification, which implied that cardiac telemetry could be considered for application to patients with hemodynamic instability. Clear standards and guidelines are required to determine who requires telemetry and when to end the telemetry monitoring.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.42-47
• /
• 2001

Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the polynomial approximation partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.2 no.2
• /
• pp.15-21
• /
• 2009

This paper proposes a novel algorithm to assess the abnormal heart beats such as PVC (Premature Ventricular Contraction) and its subsequent RUNs. Our Arrhythmic detection scheme is based on only the R-R Interval features extracted from ECG waveforms and MIT-BIH arrhythmia database is evaluated to validate the efficiency of our algorithm in terms of sensitivity, specificity, FPR(%) and FNR(%).

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

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.4 no.1
• /
• pp.17-22
• /
• 2011

In this study, a P wave emphasizing and detection algorithm from ECG signal was proposed to read arrhythmia. The algorithm uses two slope tracing waveform, the descending slope tracing wave and the ascending slope tracing wave, developed for efficient determination of slope inverting points and sudden slope changing points. The algorithm generates the slope tracing waveform which trace the original ECG wave, and subtracts one tracing wave from the other to detect P and T waves. The algorithm has been applied to MIT/BIH database in order to verify its efficacy and validity in practical applications.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.7 no.3
• /
• pp.126-131
• /
• 2014

In this study, QRS complex is detected by Wavelet Transform and it can be worked in 32bit fixed point operation thought optimization. First, ECG signal is passed though band pass filter. Second, it is transformed using one-band combined wavelet function from 3-band wavelet function. Third, it is passed though moving window integral. Finally, QRS complex is detected by decision rule. The proposed algorithm is evaluated using MIT-BIH arrhythmia database. Its all of process make progress 32-bit fixed-point operation and it makes table that high complexity operations like trigonometrical function. The detection algorithm evaluate through computer simulation.

• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.137-142
• /
• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Journal of Biomedical Engineering Research
• /
• v.21 no.2
• /
• pp.165-174
• /
• 2000

본 논문에서는 M 채널 필터 뱅크를 이용하여 심전도 자동 진단 시스템에서 매우 중요한 파라미터로 사용되는 QRS complex 검출을 실시하였다. 제안된 알고리즘에서는 심전도 신호를 M개의 균일한 주파수 대역으로 분할(decomposition)하고, 분할된 서브밴드(subband) 신호들 중에서 QRS complex의 에너지 분포가 가장 많이 존재하는 5∼25Hz 영역의 서브밴드 신호들을 선택하여 feature를 계산함으로써 QRS complex 검출을 실시하였다. 제안된 알고리즘의 성능 비교를 위하여 MIT-BIH arrhythmia database를 사용하였으며, sensitivity는 99.82%, positive predictivity는 99.82, 평균 검출율은 99.67%로 기존의 알고리즘에 비해 높은 검출 성능을 나타내었다. 또한 polyphase representation을 이용하여 M 채널 필터 뱅크를 구현한 결과 연산 시간이 단추되어 실시간 검출이 가능함을 확인하였다.

• Journal of Chest Surgery
• /
• v.47 no.3
• /
• pp.294-297
• /
• 2014

Cardiac complications such as arrhythmia and heart failure are common in Graves disease. Early detection and proper treatment of hyperthyroidism are important because cardiac complications are reported to be reversible if the thyroid function is normalized by medical treatment. We report here a case of cardiac complication of Graves disease that was too late to reverse with medical treatment and required surgical treatment.