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

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

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1215-1221
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• 2009

A novel approach for electrocardiogram (ECG) analysis within a functional sensor node has been developed and evaluated. The main aim is to reduce data collision, traffic overload and power consumption in healthcare applications of wireless sensor networks(WSN). The sensor node attached on the patient's body surface around the heart can perform ECG analysis based on a QRS detection algorithm to detect abnormal condition of the patient. Data transfer is activated only after detected abnormality in the ECG. This system can reduce packet loss during transmission by reducing traffic overload. In addition, it saves power supply energy leading to more reliable, cheap and user-friendly operation in the WSN for ubiquitous health monitoring.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.175-178
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• 1996

The purpose of this thesis is the propose of various signal processing algorithm for the ECG(electrocardiogram) and the design of realtime automated ECG analyzer feasible with these algorithms. The algorithms are composed of (1)filtering procedure fer the estimation and removal of baseline drift, 60Hz power line interference, and muscle artifacts (2)detection procedure of QRS complex and P wave (3)typification procedure for the pattern classification according to the morphologies (4) selection of representative beat, significant point and wave boundary decision procedure and (5) parameter extraction and diagnosis procedure. All verifications are carried out between the algorithms proposed in this paper and other algorithms already proposed by many researchers, for the objective comparison in each procedure. The efficiency of proposed algorithms are demonstrated with the aid of internationally validated CSE database and the performances of filtering procedure are compared on artificial noise signal as well as actual ECG signals with appropriate noise components. for the comparison on the performance of designed automated ECG analyzer, the diagnosis results were compared with ECG analyzer manufactered by Fukuda denshi in Japan.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.8
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• pp.691-697
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• 2002

Myocardial ischemia is a disorder of cardiac function caused by insuficient blood flow to the muscle tissue of the heart. We can diagnose myocardial ischemia by observing the change of ST-segment, but this change is temporary. Our primary purpose is to detect the temporary change of the 57-segment automatically In the signal processing, the wavelet transform decomposes the ECG(electrocardiogram) signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex more easily. Amplitude comparison method is adopted to detect QRS complex. Reducing the effect of noise to the minimum, we grouped ECG by 5 data and compared the amplitude of maximum value. To recognize the ECG .signal pattern, we adopted the polynomial approximation partially and statistical method. The polynomial approximation makes possible to compare some ECG signal with different frequency and sampling period. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. After removing the distorted ECG by calculating the difference between the orignal ECG and the approximated ECG for polynomial, we compared the approximated ECG pattern with the database, and we detected and classified abnormality of ECG.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1509-1518
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• 2016

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.87-93
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• 2015

The accidents caused by drivers while driving are considered as the major causes along with other causes such as conditions of roads, weather and cars. In this study, we investigated the driver's workloads under three different driving conditions (Weather, Driving time zone, and Traffic density) through analyzing biological signals obtained from a car driving simulator system. The proposed method is able to detect R waves and R-R interval calculation in the ECG. Heart rate variability (HRV) was investigated for the time domain to determine the changes in driver's conditions.

• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.164-171
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• 2008

Recently, many of hospitals have hurried to computerize the resulting data from medical devices, in order to introduce Electric Medical Record(EMR). In terms of the linkage between medical devices and hospital information systems, however, many difficulties have arisen due to some reasons such as the variety of prescription input, the format difference of the resulting data sheet, and the interface difference between medical devices from different companies. To solve these problems, many researches on standardization of the resulting data of medical devices have been performed. In this study, the linkage between hospital information systems and resulting datum in Electrocardiogram(ECG) generating biosignal waveform was tested by applying Medical waveform Format Encoding Rules(MFER) Version 1.02, which has more advantages than existing global standard. MFER viewer, in addition, was made to display the resulting data on a screen. The MFER viewer was tested and compared to the existing Scalable Vector Graphics (SVG) Viewer. The results showed that this method is more effective in the interface the data storage and application, because of simplicity and easiness in data applications. And the results show that the MFER is convenience and effective for physician. It is considered that the role of MFER as the interface in biosignal waveform including Electrocardiogram medical devices would expand in the near future.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.82-87
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• 2016

The photoplethysmogram (PPG) signal is one of the mainly considered bio signals along with the electrocardiogram (ECG) signal. PPG signals can be used to estimate the speed of flow of blood in vein, saturation of peripheral oxygen and etc. The heartbeat rate is a common feature in order to evaluate those checkup lists. To estimate the correct heartbeat rate, dynamic noises must be removed in the PPG signal. Conventionally, the acceleration signal is used to remove dynamic noises. This method, however, increases the computational complexity. In this paper, we proposes a solution that uses only PPG signals to calculate the heartbeat rate, and which can be used as a basement in real-time healthcare solution.