• Proceedings of the KOSOMBE Conference
• /
• v.1989 no.05
• /
• pp.13-14
• /
• 1989

Prolongation in QT corrected interval (QTc), measured in surface ECG, has been shown in the majority af patients to be marker of bad prognosis in postmyocardial infarction patients (PMIP). Hence it would seem logical that dynamic QTc interval measurement can be a very usefull indicator to stratify prognosis in PMIP. We present a new algorithm for QT as well as for QTP (distance value from Q wave onset to T wave peak) intervals measurement in 24 hour ECG Holter tapes. Validation of the algorithm by hand measurement has been done on first beats of 18 Holter tapes, resulting in a magnitude of deviations between 10 and 15 ms. Application on 24 hour Holter ECG signal has also been done.

• 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.

• Transactions on Electrical and Electronic Materials
• /
• v.16 no.1
• /
• pp.16-19
• /
• 2015

A wearable sensing ECG T-shirt for ubiquitous vital signs sensing is proposed. The sensor system consists of a signal processing board and capacitive sensing electrodes which together enable measurement of an electrocardiogram (ECG) on the human chest with minimal discomfort. The capacitive sensing method was employed to prevent direct ECG measurement on the skin and also to provide maximum convenience to the user. Also, low power integrated circuits (ICs) and passive electrodes were employed in this research to reduce the power consumption of the entire system. Small flexible electrodes were placed into cotton pockets and affixed to the interior of a worn tight NIKE Pro combat T-shirt. Appropriate signal conditioning and processing were implemented to remove motion artifacts. The entire system was portable and consumed low power compared to conventional ECG devices. The ECG signal obtained from a 24 yr. old male was comparable to that of an ECG simulator.

• Journal of Fashion Business
• /
• v.21 no.2
• /
• pp.78-90
• /
• 2017

In this study, brassiere prototype was developed for attaching the measuring module of ECG measurement and body movement while sleeping. For ECG measurement, textile electrodes was made of stretch fabric containing polyurethane in consideration of elasticity of brassiere band. It was used as a conductive yarn by silver coating on the warp. The textile electrodes was woven with twisted twill to increase the density of conductive yarns. The pressure of the brassiere band was enough to sensing stably the ECG, and the elastic band of the brassiere was designed to be wider than 3cm to install the textile electrodes inside, so that textile electrodes was close fitting to the skin at a constant pressure without lifting. The textile electrodes coated with silicon on rear was attached to brassiere elastic band, and the module was installed with a snap connector to textile electrodes of brassiere band. The module was suitable to monitering ECG measurement of a typical R peak, pulse rate and body movement while sleeping without interfering.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.49 no.1
• /
• pp.1-7
• /
• 2012

In general, currently developed ECG(electrocardiogram) based biometrics approaches are not suitable for real market applications since they require high cost ECG monitoring device and their measurement methods showed poor usability. In this paper, we developed lead I signal based biometrics system using special purpose ECG measurement hardware. To guarantee signal quality for biometrics from various signal measurement environment in our ordinary life, several filters are applied. In addition, to enhance usability, only two skin on electrodes without reference point are used for measurement. Lead I signals of seventeen candidates are measured from developed hardware and features are extracted. Extracted features are applied to support vector machine (SVM) pattern classifier for biometrics, and the experimental results showed 98.59% of sensitivity (SN) and 97.21% of accuracy (ACC). Compare to conventional ECG biometrics approaches, proposed system showed enhanced usability with low-cost measurement hardware.

• Journal of Sensor Science and Technology
• /
• v.15 no.5
• /
• pp.334-340
• /
• 2006

A compact biosignal monitoring device was developed. Electrodes for electrocardiogram (ECG) and a LED and silicon detector for photoplethysmogram (PPG) were used. A lead II type was arranged for ECG measurement and reflected light was measured at the finger tip for PPG. A single chip microprocessor (model ADuC812, Analog Device) controlled a measurement protocol and processed measured signals. PPG and ECG had a sampling rate of 300 Hz with 8-bit resolution. The maximum power consumption was 100 mW. The microprocessor computed pulse transit time (PTT) between the R-wave of ECG and the peak of PPG. To increase the resolution of PTT, analog peak detectors obtained the peaks of ECG and PPG whose interval was calculated using an internal clock cycle of 921.6 kHz. The device was designed to be operated by 3-volt battery. Biosignals can be measured for $2{\sim}3$ days continuously without the external interruptions and data is stored to an on-board memory. Our system was successfully tested with human subjects.

• 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..

• Journal of Biomedical Engineering Research
• /
• v.16 no.4
• /
• pp.403-414
• /
• 1995

It is very difficult to quantize the ECG analysis because the decision criterion for ECG is different with each other depending on the medical specialists of the heart and there are measured detecting errors for each ECG measurement system. Therefore, we developed the real-time ECG identification system using digital fuzzy processor for STD-BUS, in order to reduce ambiguity generated in the process of ECG identification and to analyze the irregular ECG stastically to ECG's repetition interval. The variables such as AGE (months), width of QRS, average RRI, and RRI were used to classify the ECG, and were applied to ECG signal indentification system which is developed for the purpose of research. It was found that the automatic diagnosis of ECG signal was possible in the real time process which was impossible in general process of algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2007.11a
• /
• pp.356-357
• /
• 2007

In this paper, we developed an integrated miniaturized device which acquires and transmits the signal of ECG an interested heartbeat and body's temperature and humidity. Using an amplifier circuit on the electrodes and the radio frequency transmission, the developed system dispenses with the use of cables among the electrodes, amplifier, and the post processing system. The sensor signals are transmitted to the RF-wireless terminal that was developed using the micro controller Aduc812, LCD, RF-module (frequency 424MHz, 9600-bps). In results, the developed system improves not only the signal-to-noise ration in dynamic ECG & and body's temperature and humidity measurement, but also the user convenience.

• Journal of Biomedical Engineering Research
• /
• v.37 no.5
• /
• pp.161-167
• /
• 2016

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.