• Proceedings of the KIEE Conference
• /
• 1987.07b
• /
• pp.1268-1270
• /
• 1987

This paper suggests several simple and efficient algorithms for detecting the ECG Signal by Microcomputer's software. The ECG signal detection was performed with the Linear Approximation and the feature extraction. The linear transformation approximates a given waveform by a piecewise-linear function with a preset upper bound on the absolute error between the functional values of the original function and the approximation. And the feature extraction from ECG signal, the features are different wave amplitudes, durations and interwave intervals, used the slope, the amplitude and time-Duration of ECG Sinal.

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.1159-1162
• /
• 1999

The ECG signal is very important information for diagnosis of patient and a cardiac disorder. It is hard to remove the noise because that is mixed with a lot of noise, and the error of the filtering will distort the ECG signal. The existing method for the filtering of the ECG signal has structure that has many steps for filtering, so that structure is complex and the processing speed is slow. For the improvement of that problem, we propose the method of filtering that has simple structure using the RBF neural networks and have good results.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.6
• /
• pp.825-832
• /
• 2013

In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.7
• /
• pp.1728-1736
• /
• 2015

Several algorithms have been developed to classify arrhythmia which either 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 design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose arrhythmia classification method using QRS Pattern of ECG signal according to personalized type. For this purpose, we detected R wave through the preprocessing method and define QRS pattern of ECG signal by QRS feature Also, we detect and modify by pattern classification, classified arrhythmia duplicated QRS pattern in realtime. Normal, PVC, PAC, LBBB, RBBB, Paced beat classification is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.98%, 97.22%, 95.14%, 91.47%, 94.85%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.1085-1087
• /
• 2015

본 논문에서는 심장의 전기활성도를 반영하는 ECG 신호 중 일부를 명세한다. 꾸준히 축적되었고 통용되는 ECG 신호의 비정형 명세를 정형 명세로 바꾸는 과정에서 선형 시제 논리보다 시간을 다루는 명세 및 양적 평가에 유리한 신호 시제 논리(Signal Temporal Logic)를 사용한다. ECG 신호를 감지했다는 가정하에 특징점을 추상화하여 신호를 맹세했고, 양적으로 평가해주는 모델 기발 실시간 ECG 모니터링 시스템의 신속한 개발 필요성을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.7
• /
• pp.1287-1294
• /
• 2001

As size of waveform is very small, ECG(electrocardiogram) signal is difficult to analyze for noise which is occurred when it measures. In order to obtain ECG clearly, it must eliminate that power line interference, baseline wandering, noise of muscle constriction. In ECG, the worst problem which is recorded signal of ECG is the baseline wandering elimination, which is occurred by rhythm of respiration and muscle constriction of part from attaching to an electrode. Such the baseline is roughly irregular wandering and shaking up and down therefore the part of the baseline wandering elimination is very important because it is difficulty of ECG diagnosis. In this study, as implementation of real-time signal processing digital filter it is applicable to analyze patient's heart disease by way of design of the baseline wandering elimination system.

• Proceedings of the KOSOMBE Conference
• /
• v.1992 no.05
• /
• pp.175-178
• /
• 1992

This paper proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex. the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to set of three states pulse-cord train relative to the original ECG signal. The pulse-cord train has the advantage of easy implementation in digital hardware circuits to achive automated ECG diagnosis. The algorithm performs very well feature extraction in arrythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contration) detection has a at least 90 percent sensityvity for arrythmia data.

• Journal of Sensor Science and Technology
• /
• v.20 no.6
• /
• pp.382-387
• /
• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.2
• /
• pp.75-81
• /
• 2006

The purpose of this paper is to recognize the feature points of ECG and human pulse -which signal shows the electric and physical characteristics of heart respectively- using wavelet transform. Wavelet transform is proper method to analyze a signal in time-frequency domain. In the process of wavelet decomposition and reconstruction of ECG and human pulse signal, we removed the noises of signal and recognized the feature points of signal using some of decomposed component of signal. We obtained the result of recognition rate that is estimated about 95.45$\%$ in case of QRS complex, 98.08$\%$ in case of S point and P point and 92.81$\%$ in case of C point. And we computed diagnosis parameters such as RRI, U-time and E-time.

• Proceedings of the Safety Management and Science Conference
• /
• 2004.05a
• /
• pp.241-245
• /
• 2004

In this paper, we implement a portable bioelectric signal measurement system for the safety of industrial workers. The developed system consists of two parts: the one is boielectric signal measurement unit and the other is signal analyzer system with PDA. The former includes signal processing part, A/D convertor, and 8051 based microprocessor, the latter includes software for signal analysis and display. The developed system detects industrial worker's ECG and displays and stores it to PDA. The ECG data in PDA can be transmitted to PC located in a distance, allowing a doctor to review the ECG and make a treatment decision. A doctor analyzes the ECG data and gives medical treatment to industrial worker.