• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.4
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• pp.300-308
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• 2010

This paper describes the development of a low-power electrocardiogram (ECG) touch sensor intended for the use with two dry metal electrodes. An equivalent ECG extraction circuit model encountered in a ground-free two-electrode configuration is investigated for an optimal sensor read-out circuit design criteria. From the equivalent circuit model, (1) maximum sensor resolution is derived based on the electrode's background thermal noise, which originates from high electrode-skin contact impedance, together with the input referred noise of instrumentation amplifier (IA), (2) 60 Hz electrostatic coupling from mains and motion artifact are also considered to determine minimum requirement of common mode rejection ratio (CMRR) and input impedance of IA. A dedicated ECG read-out front end incorporating chopping scheme is introduced to provide an input referred circuit noise of 1.3 ${\mu}V_{rms}$ over 0.5 Hz ~ 200 Hz, CMRR of IA > 100 dB, sensor resolution of 7 bits, and dissipating only 36 ${\mu}W$. Together with 8 bits synchronous successive approximation register (SAR) ADC, the sensor IC chip is implemented in 0.18 ${\mu}m$ CMOS technology and integrated on a 5 cm $\times$ 8 cm PCB with two copper patterned electrodes. With the help of proposed touch sensor, ECG signal containing QRS complex and P, T waves are successfully extracted by simply touching the electrodes with two thumbs.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.10 no.1
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• pp.46-53
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• 2006

Magnetocardiography provides non-invasive three-dimensional electroanatomical imaging of cardiac electrophysiology. The aim of this study is to find the index for using the indicator of magnetocardiography. An object of this study is 214 papers registered on www.pubmed.com. The index used in magnetocardiography is the interval, morphology, amplitude and integrals of QRS complex, P wave, T wave. The current density and electric field map are used, too. A new try of the index is LAS, W-HEAD, Relative Smoothness index(RS index) etc.

• Clinical and Experimental Pediatrics
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• v.53 no.5
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• pp.644-647
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• 2010

Purpose: To evaluate myocardial conductivity to understand cardiac involvement in patients with mitochondrial disease. Methods: We performed retrospective study on fifty-seven nonspecific mitochondrial encephalopathy patients with no clinical cardiac manifestations. The patients were diagnosed with mitochondrial respiratory chain complex defects through biochemical enzyme assays of muscle tissue. We performed standard 12-lead electrocardiography (ECG) on all patients. Results: ECG abnormalities were observed in 30 patients (52.6%). Prolongation of the QTc interval (>440 ms) was seen in 19 patients (33.3%), widening of the corrected QRS interval in 15 (26.3%), and bundle branch block in four (7.0%). Atrioventricular block, premature atrial contraction and premature ventricular contraction were seen in two patients each (3.5%) and Wolff-Parkinson-White syndrome in one patient (1.8%). Conclusion: Given this finding, we recommend active screening with ECG in patients with mitochondrial disease even in patients without obvious cardiac manifestation.

• Clinical and Experimental Pediatrics
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• v.56 no.7
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• pp.308-311
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• 2013

In this report, we present a pediatric case of severe symptomatic hypermagnesemia resulting from the use of magnesium oxide as a laxative in a child undergoing continuous cyclic peritoneal dialysis for end-stage renal disease. The patient showed abnormal electrocardiography (ECG) findings, such as tall T waves, a widened QRS complex, and irregular conduction, which were initially misdiagnosed as hyperkalemia; later, the correct diagnosis of hypermagnesemia was obtained. Emergent hemodialysis successfully returned the serum magnesium concentration to normal without complications. When abnormal ECG changes are detected in patients with renal failure, hypermagnesemia should be considered.

• Journal of Multimedia Information System
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• v.7 no.3
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• pp.231-238
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• 2020

This work presents a fully analog baseline wander tracking and removal circuitry using high-pass filter (HPF) based R-peak detection and quadratic interpolation that does not require digital post processing, thus suitable for compact and low power long-term ECG monitoring devices. The proposed method can effectively track and remove baseline wander in ECG waveforms corrupted by various motion artifacts, whereas minimizing the loss of essential features including the QRS-Complex. The key component for tracking the baseline wander is down sampling the moving average of the corrupted ECG waveform followed by quadratic interpolation, where the R-peak samples that distort the baseline tracking are excluded from the moving average by using a HPF based approach. The proposed circuit is designed using CMOS 0.18-㎛ technology (1.8V supply) with power consumption of 19.1 ㎼ and estimated area of 15.5 ㎟ using a 4^th order HPF and quadratic interpolation. Results show SNR improvement of 10 dB after removing the baseline wander from the corrupted ECG waveform.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.665-675
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• 2007

The morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. Generally ST segment deviation is concerned with myocardial abnormality. The aim of this study is to detect the change of ST in shape using a polynomial approximation method and the reference ST type. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The detection of QRS complex is accomplished using it's the morphological characteristics such as the steep slope and high amplitude. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes of the specified points between the reference ST set and the least square curve. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.227-232
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• 2011

The QRS complex in ECG analysis is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. This paper presents the preprocessor method to detect R-peak, RR interval, and HRV in wireless sensor node. The derivative of the electrocardiogram is efficiency of preprocessing method for resource hungry wireless sensor node with low computation. We have implemented R-peak and RR interval detection application based on dECG for wireless sensor node. The sensor node only transfers meaning parameter of ECG. Thus, implementation of sensor node can save power, reduce traffic, and eliminate congestion in a WSN.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.293-299
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• 2004

This paper is about the extraction of basis function for ECG signal processing. In the first step, it is assumed that ECG signal consists of linearly mixed independent source signals. 12 channel ECG signals, which were sampled at 600sps, were used and the basis function, which can separate and detect source signals - QRS complex, P and T waves, - was found by applying the fast fixed point algorithm, which is one of learning algorithms in independent component analysis(ICA). The possibilities of significant point detection and classification of normal and abnormal ECG, using the basis function, were suggested. Finally, the proposed method showed that it could overcome the difficulty in separating specific frequency in ECG signal processing by wavelet transform. And, it was found that independent component analysis(ICA) could be applied to ECG signal processing for detection of significant points and classification of abnormal beats.

• Advances in Traditional Medicine
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• v.10 no.4
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• pp.278-287
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• 2010

The present study was undertaken to evaluate the protective effect of ethanol (EtAS), ethyl acetate (EAAS) and aqueous (AQAS) extracts of Argyreia speciosa (AS) roots against Isoproterenol (ISO)-induced myocardial infarction in rats. The animals were exposed to isoproterenol (200 mg/kg. s.c) twice at an interval of 24 hrs. Cardioprotective effect was assessed by observing ECG parameters, serum marker enzymes and histopathology of the heart. Pretreatment of EAAS, and EtAS (200 mg/kg) resulted in a significant (P < 0.001) increase in P wave, QRS complex and R-R interval, whereas heart rate, QT interval and cardiac cycle were maintained near to normal values. EtAS and EAAS showed significant (P < 0.05; P < 0.001) reduction in all the tested diagnostic markers compared to ISO treated group. Histological studies on the structural changes of heart tissue supported the protective activity of AS. The result suggest that treatment of AS prior to ISO has a significant role in protecting the animals from ISO induced myocardial infarction.

• 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.