• 센서학회지
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• 제29권3호
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• pp.162-171
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• 2020

The incidences of cardiovascular diseases are rapidly increasing worldwide. The electrocardiogram (ECG) is a test to detect and monitor heart issues via electric signals in the heart. Presently, detecting heart disease in real time is not only possible but also easy using the myDAQ data acquisition device and LabVIEW. Hence, this paper proposes a system that can acquire ECG signals in real time, as well as detect heart abnormalities, and through light-emitting diodes (LEDs) it can simultaneously reveal whether a particular waveform is in range or otherwise. The main hardware components used in the system are the myDAQ device, Vernier adapter, and ECG sensor, which are connected to ECG monitoring electrodes for data acquisition from the human body, while further processing is accomplished using the LabVIEW software. In the Results section, the proposed system is compared with some other studies based on the features detected. This system is tested on 10 randomly selected people, and the results are presented in the Simulation Results section.

• 한국정보통신학회논문지
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• 제25권10호
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• pp.1279-1286
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• 2021

Electrocardiogram (ECG) 신호는 심장의 이상을 조기에 진단하기 위한 좋은 지표이다. ECG 신호는 사람마다 기준이 되는 정상 신호의 형태가 다르고, 진단에 많은 데이터가 필요하다. 본 논문에서는 ECG 신호 진단을 효율적으로 가속하기 위한 OpenCL을 기반 FPGA-GPU 혼합 계층 적응형 플랫폼을 제안한다. 플랫폼에서 MIT-BIH 부정맥 신호데이터의 19870개 ECG 신호를 진단한 결과 FPGA 가속기는 진단 시간이 1.15s로 소프트웨어로 실행했을 때보다 89.94% 감소하였고, 전력 소모는 84.0% 감소하였다. GPU 가속기는 실행 시간이 소프트웨어 대비 83.56% 감소한 1.87s였으며, 전력 소모는 62.3% 감소하였다. 제안하는 FPGA-GPU 혼합 플랫폼은 FPGA 가속기보다 진단 속도가 느리지만 GPU를 이용하여 상황에 따라 유연한 알고리즘을 동작할 수 있다.

• 대한의용생체공학회:의공학회지
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• 제31권5호
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• pp.365-374
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• 2010

This paper is about the personal identification algorithm using an ECG that has been studied by a few researchers recently. Previously published algorithm can be classified as two methods. One is the method that analyzes ECG features and the other is the morphological analysis of ECG. The main characteristic of proposed algorithm uses together two methods. The algorithm consists of training and testing procedures. In training procedure, the features of all recognition objects' ECG were extracted and the PCA was performed for morphological analysis of ECG. In testing procedure, 6 candidate ECG's were chosen by morphological analysis and then the analysis of features among candidate ECG's was performed for final recognition. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 90.96% heartbeat recognition rate and 100% ECG recognition rate.

• 한국응급구조학회지
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• 제25권1호
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• pp.243-249
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• 2021

A decrease in coronary blood flow leads to an imbalance between the supply of oxygen to the myocardium and its demand, and reversible or irreversible damage to the myocardium could occur depending on the severity of the resultant ischemia and the duration of the imbalance. This imbalance results in a cascade of ischemic reactions in the following order: metabolic abnormalities, diastolic dysfunction, systolic dysfunction, and electrocardiogram changes. Variant angina is caused by the closure of the coronary artery due to reversible coronary artery spasm, resulting in myocardial ischemia and subsequent chest pain as a clinical symptom. Variant angina may be observed as ST segment elevation in electrocardiogram measured when present in chest pain. However, 12-lead electrocardiogram performed after the patient's chest pain resolves does not help in the diagnosis. Since the duration of chest pain appears to be <15 minutes, it is important to perform the 12-lead electrocardiogram when clinical symptoms are present. If nitroglycerin is administered without performing 12-lead electrocardiogram by 119 pre-hospital paramedics, the chest pain would be resolved, making it impossible to identify changes in the ST segment. Before administration of nitroglycerin, changes in the ST segment must be recorded by performing 12-lead electrocardiogram.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1370-1378
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• 2018

There is a problem to measure neutral bio-signals during sleep because of inconvenience of attaching lots of sensors. In this study, we measured single electrocardiogram(ECG) signal and analyzed the correlation with sleep. After R-peak detection from ECG signal, we extracted 9 features from time and frequency domain of heart rate variability(HRV). Mean of HRV, RR intervals differing more than 50ms(NN50), and divided by the total number of all RR intervals(pNN50) have significant differences in each sleep stage. Specially, the mean HRV has an average of 87.8% accuracy in classifying sleep and awake status. In the future, the measurement ECG signal minimizes inconvenience of attaching sensors during sleep. Also, it can be substituted for the standard sleep measurement method.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.337-340
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• 2009

Wireless tele-home-care application gives new possibilities for ECG (electrocardiogram) monitoring system with wearable biomedical sensors. Thus, continuously development of high convenient ECG monitoring system for high-risk cardiac patients is essential. This paper describes to monitor a person's ECG using wearable approach. A wearable belt-type ECG electrode with integrated electronics has been developed and has proven long-term robustness and monitoring of all electrical components. The measured ECG signal is transmitted via an ultra low power consumption wireless sensor node. ECG signals carry a lot clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed thus it bring errors due to motion artifacts and signal size changes. Variable threshold method is used to detect the R-peak which is more accurate and efficient. In order to evaluate the performance analysis, R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research. This concept able to allow patient to follow up critical patients from their home and early detecting rarely occurrences of cardiac arrhythmia.

• 디지털융복합연구
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• 제20권5호
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• pp.637-642
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• 2022

심전도(ECG, electrocardiogram) 신호를 이용한 본인 인증기술은 기존의 바이오 인증을 대체할 수 있는 본인 인증기술로 주목받고 있다. 디지털 전자키를 인식하는 장치를 차량에 탑재하여 자동차와 무선으로 데이터를 송수신할 수 있게 하고, 스마트폰 등을 활용해 자동차의 차 문을 잠금 또는 해제하거나, 시동을 걸 수 있는 기능을 스마트폰을 통해 차량 제어할 수 있다. 그러나 스마트키는 보안에 취약하여 이를 해결하고 운전자의 편의성을 제공하기 위해 바이오 인증기술을 적용한 스마트키를 연구하였다. 심전도를 이용한 개인인증 알고리즘을 시계 형태의 웨어러블 디바이스에 탑재하여 바이오인증을 하고 개인인증이 완료되면 자동차의 스마트키 기능을 할 수 있었다. 인증율 95%를 달성하였다. 운전자는 스마트키를 소지할 필요가 없고, 분실 및 해킹에 안전하게 보호할 수 있는 대안으로 스마트키를 제안한다. 심전도를 이용한 개인 인증기술을 활용한 스마트키는 개인인증을 통해 다양한 분야에 적용이 가능하고 향후 심전도를 이용한 본인확인 장치 등에 적용할 수 있는 방법을 연구할 계획이다.

• Journal of Communications and Networks
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• 제14권1호
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• pp.21-26
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• 2012

As electrocardiogram (ECG) signals are generally sampled with a frequency of over 200 Hz, a method to compress diagnostic information without losing data is required to store and transmit them efficiently on a wireless personal area network (WPAN). In this paper, an ECG signal compression method for communications onWPAN, which uses feature points based on curvature, is proposed. The feature points of P, Q, R, S, and T waves, which are critical components of the ECG signal, have large curvature values compared to other vertexes. Thus, these vertexes were extracted with the proposed method, which uses local extrema of curvatures. Furthermore, in order to minimize reconstruction errors of the ECG signal, extra vertexes were added according to the iterative vertex selectionmethod. Through the experimental results on the ECG signals from Massachusetts Institute of Technology-Beth Israel hospital arrhythmia database, it was concluded that the vertexes selected by the proposed method preserved all feature points of the ECG signals. In addition, it was more efficient than the amplitude zone time epoch coding method.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

• Journal of Biosystems Engineering
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• 제33권5호
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• pp.317-325
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• 2008

The electrocardiogram (ECG) measurement system consists of I/O interface to input the ECG signals from two electrodes, FPGA (Field programmable gate arrays) module to process the signal conditioning, and real time module to control the system. The algorithms based on wavelet transform were developed to remove the noise of the ECG signals and to determine the QRS-waves. Triangular wave tests were conducted to determine the optimal factors of the wavelet filter by analyzing the SNRs (signal to noise ratios) and RMSEs (root mean square errors). The hybrid rule, soft method, and symlets of order 5 were selected as thresholding rule, thresholding method, and mother wavelet, respectively. The developed wavelet filter showed good performance to remove the noise of the triangular waves with 10.98 dB of SNR and 0.140 mV of RMSE. The ECG signals from a total of 6 subjects were measured at different measuring postures such as lying, sitting, and standing. The durations of QRS-waves, the amplitudes of R-waves, the intervals of RR-waves were analyzed by using the finite impulse response (FIR) filter and the developed wavelet filter. The wavelet filter showed good performance to determine the features of QRS-waves, but the FIR filter had some problems to detect the peaks of Q and S waves. The measuring postures affected accuracy and precision of the ECG signals. The noises of the ECG signals were increased due to the movement of the subject during measurement. The results showed that the wavelet filter was a useful tool to remove the noise of the ECG signals and to determine the features of the QRS-waves.