• Journal of Sensor Science and Technology
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• v.19 no.1
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• pp.43-51
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• 2010

In this study, wearable ECG measurement system was implemented for health monitoring during daily life. A wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenience in wearing. The measured ECG signal is transmitted via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. The ECG monitoring program is developed at end user which is personal computer. The measured ECG contains many noises mainly due to motion artifacts. For ECG signal processing, adaptive filtering process is proposed which can reduce motion artifacts efficiently and accurately than digital filter. The experimental results show that a reliable performance with high quality ECG signal can be achieved using this wearable ECG monitoring system.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.175-178
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• 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
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• v.21 no.1
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• pp.39-45
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• 2012

Recently the convergence of healthcare technology is used for daily life healthcare monitoring. Cardiac arrhythmia is presented by the state of the heart irregularity. Abnormal heart's electrical signal pathway or heart's tissue disorder could be the cause of cardiac arrhythmia. Fatal arrhythmia could put patient's life at risk. Therefore arrhythmia detection is very important. Previous studies on the detection of arrhythmia in various ECG analysis and classification methods had been carried out. In this paper, an ECG signal processing techniques to detect abnormal ECG based on DTW minimum accumulation distance through the template matching for normalized data and variable threshold method for ECG R-peak detection. Signal processing techniques able to determine the occurrence of normal ECG and abnormal ECG. Abnormal ECG detection algorithm using DTW minimum accumulation distance method is performed using MITBIH database for performance evaluation. Experiment result shows the average percentage accuracy of using the propose method for Rpeak detection is 99.63 % and abnormal detection is 99.60 %.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.4-55
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• 2001

Analyzing the ECG signal, we can find heart disease. Myocardial ischemia is a disorder of cardiac function caused by insufficient blood flow to the muscle tissue of the heart. Myocardial ischemia is inscribed on ST-segment of the ECG during and after patient takes exercise or is under stress, but after long time past, the ECG pattern is return to steady state. Therefore, it is necessary to monitor and analyze the ECG signal continuously for patient or aged people. Our primary purpose is the detection of temporary change of the ST-segment of ECG automatically. 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 more easily ...

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.29-34
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• 2015

The ECG(Electrocardiograph) ambulatory test as called Holter is performed usually to diagnose several heart diseases causing different arrhythmias. This paper exposes the insights of the design of a 10-lead ambulatory ECG recorder. Reducing the size and minimizing the power consumption of the ECG recorder are crucial to allow long recording time without causing discomfort to the patient. This paper proposes lower hardware design and differential compression algorithm to extend the maximum 72 hours recording time in consideration of smaller and light-weighted recorder size. The performance results by newly introduced compression algorithm are shown and discussed.

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

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.742-745
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• 1988

Control algorithm for remote transmission processing system for ECG signals is proposed. Software for the system hardware consists of system control algorithm and signal processing algorithm. Since signal processing algorithm is now under developing, this paper describes the details of system control only.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.6
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• pp.608-616
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• 2013

This paper presents an adaptive sampling method for electrocardiogram (ECG) signal detection. First, by employing the strings matching process with compression dictionary, we recognize each segment of ECG with different characteristics. Then, based on the non-uniform sampling strategy, the sampling rate is determined adaptively. As the results of simulation indicated, our approach reconstructed the ECG signal at an optimized sampling rate with the guarantee of ECG integrity. Compared with the existing adaptive sampling technique, our approach acquires an ECG signal at a 30% lower sampling rate. Finally, the experiment exhibits its superiority in terms of energy efficiency and memory capacity performance.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

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