• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.135-140
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• 2012

As the recent advanced in BIO signal measurement technology, our computing platform is rapidly shifting from desktop PCs to Embedded System. Therefore, In this paper introduces an implementation of the same precision as a hospitan ECG system on the Android. The most important fact of the hospital system is connectivity among the PC such as separate means of communication, we can eliminate the separate means of communication through the Porting Embedded System on Android that can be receive ECG signal directly. We also implementation ECG App on Android that can analyze and show the data result directly.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.85-89
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• 2014

Electrocardiogram (ECG) is a diagnostic test which records the electrical activity of the heart, shows abnormal rhythms and detects heart muscle damages. With this ECG signal, medical centers diagnose patients' heart disease symptoms. A normal resting heart rate for adults rages from 60 to 100 beats a minute. An irregular heartbeat is called "arrhythmia", and arrhythmia is also called "cardiac dysrhythmia". In an arrhythmia, the heartbeat maybe too slow(slower than 60beats), too rapid(faster than 100beats), too irregular, etc. Among these symptoms of arrhythmia, if the heart beat is slower than the normal range, the symptom is called "bradycardia", and if it is faster than the range, it is called "tachycardia" In this letters, we proposed the detection algorithm of cardiac arrhythmia in ECG signal using R-R interval through the detection of R-peak.

• 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.

• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.817-822
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• 1992

Distortion analysis of digital filters for the ECG signal processing is presented. Several band pass and band reject filters are designed for the analysis. Computer simulations are performed to compare the distortions of the Butterworth type filters and linear phase optimal fitters. The designed filters are applied to power line interference cancelling in ECG signals.

• Journal of the Korea society of information convergence
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• v.6 no.1
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• pp.1-6
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• 2013

The electrocardiogram(ECG) is widely used for the diagnosis of heart disease recent. In order to correct diagnosis, wavelet and thresholding is studied. In this study, we study hard inverse thresholding that is apply the existing hard thresholding. It apply to hard inverse thresholding on Pan-Tomkins algorism, that was simplified. The results of mit-bih No. 103 ECG signal is detected R peaks was detected unaffected by signal distortion and noise.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.109-112
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• 1995

This paper is a study on the detection of the significant point in ECG signal. ECG signal consists of two components; one is high frequency component to be detected and the other is low frequency component to be removed. AR model is appropriate for modelling and removing the low frequency component. AR model coefficients are updated by artificial neural network algorithm. We can remove the background noise(low frequency) by passing through the AR filter. The remaining signals which include high frequency noise are sent to the matched filter to pass only the signal which we want to extract. The template used in matched filter is updated adaptively.

• Smart Media Journal
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• v.8 no.3
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• pp.62-69
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• 2019

Electrocardiogram (ECG) signals cannot be counterfeited and can easily acquire signals from both wrists. In this paper, we propose a method of generating a coupling image using direction information of ECG signals as well as its usage in a personal recognition method. The proposed coupling image is generated by using forward ECG signal and rotated inverse ECG signal based on R-peak, and the generated coupling image shows a unique pattern and brightness. In addition, R-peak data is increased through the ECG signal calculation of the same beat, and it is thus possible to improve the recognition performance of the individual. The generated coupling image extracts characteristics of pattern and brightness by using the proposed convolutional neural network and reduces data size by using multiple pooling layers to improve network speed. The experiment uses public ECG data of 47 people and conducts comparative experiments using five networks with top 5 performance data among the public and the proposed networks. Experimental results show that the recognition performance of the proposed network is the highest with 99.28%, confirming potential of the personal recognition.

• Korean Circulation Journal
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• v.54 no.3
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• pp.140-153
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• 2024

Background and Objectives: Although a single-lead electrocardiogram (ECG) patch may provide advantages for detecting arrhythmias in outpatient settings owing to user convenience, its comparative effectiveness for real-time telemonitoring in inpatient settings remains unclear. We aimed to compare a novel telemonitoring system using a single-lead ECG patch with a conventional telemonitoring system in an inpatient setting. Methods: This was a single-center, prospective cohort study. Patients admitted to the cardiology unit for arrhythmia treatment who required a wireless ECG telemonitoring system were enrolled. A single-lead ECG patch and conventional telemetry were applied simultaneously in hospitalized patients for over 24 hours for real-time telemonitoring. The basic ECG parameters, arrhythmia episodes, and signal loss or noise were compared between the 2 systems. Results: Eighty participants (mean age 62±10 years, 76.3% male) were enrolled. The three most common indications for ECG telemonitoring were atrial fibrillation (66.3%), sick sinus syndrome (12.5%), and atrioventricular block (10.0%). The intra-class correlation coefficients for detecting the number of total beats, atrial and ventricular premature complexes, maximal, average, and minimal heart rates, and pauses were all over 0.9 with p values for reliability <0.001. Compared to a conventional system, a novel system demonstrated significantly lower signal noise (median 0.3% [0.1-1.6%] vs. 2.4% [1.4-3.7%], p<0.001) and fewer episodes of signal loss (median 22 [2-53] vs. 64 [22-112] episodes, p=0.002). Conclusions: The novel telemonitoring system using a single-lead ECG patch offers performance comparable to that of a conventional system while significantly reducing signal loss and noise.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.137-144
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• 2004

ECG signals are often contaminated with high-frequency noise such as muscle artifact, power line interference, and others. In the ECG signal processing, especially during a pre-processing stage, numerous noise removal techniques have been used to reduce these high-frequency noise without much distorting the original signal. This paper proposes a new type of digital filter with a continuously variable cutoff frequency to improve the signal quality This filter consists of a cutoff frequency controller (CFC) and variable cutoff frequency lowpass filter (VCF-LPF). From the noisy input ECG signal, CFC produces a cutoff frequency control signal using the signal slew rate. We implemented VCF-LPF based on two new filter design methods called convex combination filter (CCF) and weight interpolation fille. (WIF). These two methods allow us to change the cutoff frequency of a lowpass filter In an arbitrary fine step. VCF-LPF shows an excellent noise reduction capability for the entire time segment of ECG excluding the rising and falling edge of a very sharp QRS complex. We found VCF-LPF very useful and practical for better signal visualization and probably for better ECG interpretation. We expect this new digital filter will find its applications especially in a home health management system where the measured ECG signals are easily contaminated with high-frequency noises .