• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.6
• /
• pp.18-26
• /
• 2009

In this paper, we proposed multipurpose signal detection methods for ECG (electrocardiogram) based u-healthcare systems. For ECG based u-healthcare system, QRS signal extraction for cardiovascular disease diagnosis is essential. Also, for security and convenience reasons, it is desirable if u-healthcare system support biometric identification directly from user's bio-signal such as ECG for this case. For this, from Lead II signal, we developed QRS signal detection method and also, we developed signal extraction method for biometric identification using Lead II signal which is relatively robust from signal alteration by aging and diseases. For QRS signal detection capability from Lead II signal, ECG signals from MIT-BIH database are used and it showed 99.36% of accuracy and 99.68% of sensitivity. Also, to show the performance of signal extraction capability for biometric diagnosis purpose, Lead III signals are measured after drinking, smoking, or exercise to consider various monitoring conditions and it showed 99.92% of accuracy and 99.97% of sensitivity.

• Journal of Biomedical Engineering Research
• /
• v.24 no.2
• /
• pp.107-112
• /
• 2003

This paper describes a new simple QRS detection algorithm using index function based on resonance theory. The ECG signal can be modeled with several sinusoidal pulses and its first difference has some relations with the amplitude and frequency of sinusoidal pulse. Based on above fact, an index function, similar to the square of the imaginary part of a simple R-L-C circuit, was designed. A QRS complex is detected by applying the adaptive method to the response of index function. The algorithm showed a performance comparable to or higher than the other algorithms. Because it does not require any complicated preprocessing or postprocessing, it can be implemented in real time.

• Journal of Biosystems Engineering
• /
• v.33 no.5
• /
• pp.317-325
• /
• 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.

• Clinical and Experimental Pediatrics
• /
• v.60 no.6
• /
• pp.196-201
• /
• 2017

Purpose: Myocardial dysfunction and dysrhythmias are inevitable consequences of Duchenne muscular dystrophy. We aimed to evaluate specific trends of electrocardiographic changes that reflect the progress of cardiomyopathy in patients with Duchenne muscular dystrophy. Methods: Fifty electrocardiograms (ECGs) of 30 patients (ages 1 to 27 years) who had not been prescribed medications for heart failure treatment at the time of examination were retrospectively analyzed and compared with 116 ECGs of age-matched healthy 116 controls. Heart rate, leads with fragmented QRS (fQRS), corrected QT, Tpeak-to-Tend, and Tpeak-to-Tend/QT were analyzed. Results: The patients with Duchenne muscular dystrophy failed to show a normal age-related decline in heart rate but showed an increasing trend in the prevalence of fQRS, corrected QT, corrected Tpeakto-Tend, and Tpeak-to-Tend/QT over time. In the ${\leq}10-year-old$ patient group, a significant difference was found only in the prevalence of fQRS between the patients and the controls. The prevalence of fQRS, heart rate, Tpeak-to-Tend/QT, and corrected Tpeak-to-Tend demonstrated significant differences between the patients and the controls in the middle age group (11 to 15 years old). All the indexes were statistically significantly different in the ${\geq}16-year-old$ patient group. Conclusion: The prevalence of lead with fQRS representing regional wall motion abnormalities was higher in the young patients than in the young healthy controls, and this might be one of the first signs of myocardial change in the patients. Markers of depolarization and repolarization abnormalities were gradually prominent in the patients aged >10 years. Further studies are needed to confirm these findings.

• The Korean Journal of Physiology
• /
• v.16 no.1
• /
• pp.41-50
• /
• 1982

The effects of $NaHCO_3$ on the electrocardiogram of rats were studied in the induced hyperkalemia. The subjects were divided into 4 groups: the group 1 was normal control and the data on this normal control had teen obtained from the following three groups before administration of KCl or $NaHCO_3$, the group 2 (KCl) was administered 40 ml per kg body weight of the 10 per cent KCl solution, the group 3 $(NaHCO_3)$ was administered 40 ml per kg body weight of the 10 per cent $NaHCO_3$ solution, and the group 4 $(KCl+NaHCO_3)$ was received 10 per cent KCl, which was followed by administration of 10 per cent $NaHCO_3$ at one and half hours later. In KCl, the heart rate was decreased rapidly, and then maintained its level, later rapid decreasing heart rate was followed by the cardiac stand still. The mean electrical axis of QRS complex became progressively deviated to the left. The amplitude of T wave was increased transiently but was not changed thereafter. There was prolongation of the P-Q interval and the Q-T interval at the beginning and then they were shortened. In $NaHCO_3$, the heart rate was decreased rapidly at the beginning, later showed a tendency of recovery. The mean electrical axis of QRS was not changed initially, but later became deviated to the left. The amplitude of T wave was not changed. There was prolongation of the P-Q interval and the Q-T interval at the beginning and then they were shortened. In $KCl+NaHCO_3$, there were a tendency of recovery of both the amplitude of the T wave and the electrical axis of the QRS complex after administration of $NaHCO_3$ but the heart rate was not recovered. There was prolonged P-Q interval, but the Q-T interval was relatively unchanged.

• Biomedical Science Letters
• /
• v.21 no.4
• /
• pp.208-217
• /
• 2015

The exact diagnosis of left ventricular hypertrophy (LVH) is very important in the treatment of hypertension. The purpose of our study is to determine the relationship between left ventricular remodeling patterns and electrocardiography (ECG) findings in hypertensive patients. We divided 137 patients into four groups according to left ventricular mass index (LVMI) and the relative wall thickness: normal, concentric remodeling, concentric hypertrophy, eccentric hypertrophy. LVH on the ECG was defined by three ECG criteria: Sokolow-Lyon voltage criteria, Cornell voltage criteria and Romhilt-Estes point score. LVH on the echocardiography was defined by LVMI. The prevalence of ECG LVH was increased in concentric hypertrophy and eccentric hypertrophy group. The QRS voltages by Sokolow-Lyon voltage criteria (r = 0.494, P = 0.002) and Cornell voltage criteria (r = 0.628, P < 0.001), and Romhilt-Estes point score (r = 0.689, P < 0.001) were positively correlated with LVMI. Also, the QRS voltages and point scores were significantly increased in the concentric hypertrophy and eccentric hypertrophy group with increased LVMI. The QRS voltage and Romhilt-Estes point scores were positively correlated with LVMI. The QRS voltages and Romhilt-Estes point scores were also increased in the left ventricular remodeling groups with increased LVMI.

• 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 Journal of the Korea institute of electronic communication sciences
• /
• v.11 no.11
• /
• pp.1141-1148
• /
• 2016

This paper proposed a heart attack predictive monitoring system using QRS pattern of ECG for wearable healthcare. It detects abnormal heart pattern with a ECG (X, Y) coordinate pattern DB on wearable monitoring smart watch. We showed the acute heart failure prevention system and method with a proposed scheme. Especially, It proved the method which can do first aid in gold time through abnormal heart analysis with a digital ECG(X, Y) pattern information when acute heart failure occurs.

• Journal of Sensor Science and Technology
• /
• v.16 no.5
• /
• pp.361-368
• /
• 2007

Small size real-time ECG signal analysis function by QRS-complex detection was put into sensor nodes. Wireless sensor nodes attached on the patient’s body transmit ECG data continuously in normal u-healthcare system. So there are heavy communication traffics between sensor nodes and gateways. New developed platform for real-time analysis of ECG signals on sensor node can be used as an advanced diagnosis and alarming system for healthcare. Sensor node does not need to transmit ECG data all the time in wireless sensor network and to server PC via gateway. When sensor node detects suspicion or abnormality in ECG, then the ECG data in the network was transmitted to the server PC for further powerful analysis. This system can reduce data packet overload and save some power in wireless sensor network. It can also increase the server performance.

• 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.