• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.204-210
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• 2011

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.1
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• pp.60-70
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• 2014

Purpose: Bedside electrocardiograph (ECG) monitoring is continuously used for assessing patients' cardiac status in intensive care units. However, it has not been explored whether it is used with proper knowledge and nursing practices; if not, its usage will be limited and the risk for compromised patient safety might be significant. This study, therefore, explored knowledge and nursing practices regarding bedside ECG monitoring in nurses working at intensive care units. Methods: Participants in this survey research were a convenience sample of 156 nurses from 25 intensive care units distributed in five hospitals with more than 1,000 beds each in Seoul, South Korea. Results: Participants showed limited and incorrect knowledge and nursing practices. Only 4 (2.6%) participants correctly answered to all electrode placement sites of RA, LA, LL, and V1. Lead II was the most frequently monitored unit regardless of the main purpose of ECG monitoring, and nursing practices to manage noisy signals did not include skin care at the top priorities. Conclusion: Educators and clinicians alike need to make an effort to ensure that a safe level of knowledge and practices for the monitoring is maintained in order to make sure that patient outcomes are not compromised.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1130-1133
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• 2008

In this paper, a mini-Electrocardiograph(ECG) system operated by a general 9V alkaline battery is designed and implemented. The manufactured ECG consists of the instrumentation amplifier stage for detecting and amplifying the heart signal, the high pass filter(HPF), the low pass filter(LPF), the differentiator cirduit, and the peak detector. The detected heart signal through three leads is displayed cleanly on the oscilloscope, which shows the good operation of our ECG. As the detected heart signal is digitalized and displayed on the small LCD unit, the convenience of easy checkup and portability of the implemented ECG can be largely improved. Therefore, whenever and wherever anyone may checkup his/her cardiac state with ease.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.345-351
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• 2000

This study is to dvelop a radiotelemetry system to transmit and receive ECG (electrocardiograph) and event signals by using the frequency division multiplexing(FDM) technique. ECG signal sensed by the electrodes is amplified and added to the event signals acting in different frequency range for lead-off, nurse call and low level battery by using FDM. The sub-carrier oscillator using Colpitts circuits and main carrier frequency which is multiplied is frequency modulated by this superhetrodyne technique, and demodulated from the compose IF signal through the quadrature demodulator. A pulse counter demodulator and filtering circuits extract the original ECG and event signals.

• ETRI Journal
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• v.29 no.4
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• pp.530-532
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• 2007

This paper presents a simple method to implement a complete on-line portable wireless holter including an electrocardiogram (ECG) monitoring, processing, and communication protocol. The proposed algorithm significantly reduces the hardware resources of threshold estimation for ECG compression, using the standard deviation updated with each new input signal sample. The new method achieves superior performance in terms of hardware complexity, channel occupation and memory requirements, while keeping the ECG quality at a clinically acceptable level.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.73-78
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• 2013

This paper proposes a portable electrocardiograph and smart device-based heart health monitoring and risk notification system. The proposed system consists of a portable electrocardiograph and a smart device for a system user, and a web-based monitoring system for observers. This system can improve the convenience and efficiency of measurement by using a light-weight portable electrocardiograph and a smart device. In addition, any authorized person such as caregiver or family member who is not related to medical institution can monitor users'heart health in real-time using the web-based monitoring system. Therefore, a user and authorized remote observers can efficiently monitor and manage user's heart health in daily-life even without any medical institution's help, and can preemptively deal with any possible dangerous situations, such as degeneration of a cardiac disorder and sudden cardiac death.

• Journal of Biomedical Engineering Research
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• v.35 no.5
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• pp.111-118
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• 2014

As assessment of quantitative energy expenditure for effective exercise is becoming more important, many researches about the monitoring system for exercise status or result have been actively carried out. In this case, however, bicycle riders feel restrained and uncomfortable because the riders should wear a belt-type electrocardiograph or a watch-type accelerometer or GPS system during the assessment of bicycle exercise. In this study, therefore, an electrocardiograph based on textile electrode was developed for measuring ECG and calculating heart rate from the bicycle rider without feeling restraint, and smartphone application was also developed for monitoring the heart rate.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.