• Journal of Biosystems Engineering
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• 제33권5호
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• pp.317-325
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• 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.

• Journal of Multimedia Information System
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• 제3권2호
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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.

• 한국정보통신학회논문지
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• 제9권3호
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• pp.516-521
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• 2005

UWB 통신 기술은 초광대역 특성 때문에 높은 전송 속도와 저전력 통신이 가능하여 의료장비에 응용이 가능하다 특히 근거리 무선 통신 기술로 급부상하고 있는 UWB 통신 기술의 응용으로 의료 측정기인 ECG 센서 시스템을 고려할 수 있다 본 논문에서는 유선 심전도 센서를 무선 링크로 대체하는 것으로, UWB에 사용가능한 각종 펄스의 스펙트럼 특성을 FCC Spectral Mask와 FCC indoor limit에 비교하였다. 또한, 적용하고자 하는 시스템 환경에서 발생할 수 있는 UWB 신호 간섭의 영향에 따른 UWB 송수신 시스템을 근거리 송수 거리에 따른 시뮬레이션을 수행하였으며, Rake 수신기를 사용하면 약 10m까지 신뢰성을 가지는 통신을 할 수 있음을 확인하였다.

• 한국의학물리학회지:의학물리
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• 제11권2호
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• pp.141-146
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• 2000

전력 스펙트럼 분석은 자율신경계 활성도를 정량화하기 위한 강력한 비침습적인 도구이다. 본 논문에서는 자율신경계의 활성도를 측정하기 위한 시스템을 개발하였다. 시스템에서는 환자의 안전을 고려 하여 분리 전원을 채택했다. 출력으로는 ECG 시간 변이와 호흡 시간 변이를 획득하였다. 시간 변이는 자율신경계와 관련한 질병을 발견하는 데 유용하다. 시험에 적용된 범위는 심전도는 30～240 bpm 그리고 호흡에서는 15～80bpm 을 적용하였다.

• 한국컴퓨터정보학회논문지
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• 제15권12호
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• pp.19-26
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• 2010

본 논문에서는 일상생활 속에서 무자각적으로 생체신호를 측정하고 분석하여 감성을 평가할 수 있는 임베디드 시스템에 관하여 연구하였다. 지속적으로 변화하는 감성을 일관적이며 신뢰성이 높은 생리적인 방법으로 평가하기 위해 심전도(ECG:Electrocardiogram), 맥파(PPG:Photoplethysmography)의 두 가지 생체신호를 측정하고, 무선전송(Bluetooth) 장치를 이용하여 측정한 생체신호를 실시간으로 노트북PC로 전송하여 분석하였다. 생체신호의 분석방법은 고속 퓨리에 변환(FFT:Fast Fourier Transform)과 전력 스펙트럼 밀도(PSD:Power Spectrum Density)를 이용한 주파수 분석방법으로 두 생체신호의 특정 주파수 대역이 가지는 자율신경계의 활성도의 비율을 분석하여 비교 연구하였다. 또한 보다 빠르고 정확한 감성을 평가하기 위하여 분석 알고리즘의 연산을 최소화 하였으며 그래프를 이용한 분석결과의 시각화를 하였다. 본 논문에서는 무자각적인 생체 신호 측정 시스템을 이용하여 다양한 상황에서 생체신호를 측정하고, 개발한 분석 알고리즘으로 분석한 결과의 차이를 연구하여 정확도 및 신뢰도를 기준으로 감성을 평가하기 위한 분석 시스템을 평가하였다.

• Journal of Multimedia Information System
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• 제3권2호
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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.

• 한국의류학회지
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• 제45권2호
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• pp.305-316
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• 2021

In this study, the effect of wearing functional compression pants is verified using a lower-limb wearable robot through a bio-signal analysis and subjective fit evaluation. First, the compression area to be applied to the functional compression pants is derived using the quad method for nine men in their 20s. Subsequently, functional compression pants are prepared, and changes in Electroencephalogram (EEG) and Electrocardiogram (ECG) signals when wearing the functional compression and normal regular pants inside a wearable robot are measured. The EEG and ECG signals are measured with eyes closed and open. Results indicate that the Relative alpha (RA) and Relative gamma wave (RG) of the EEG signal differ significantly, resulting in increased stability and reduced anxiety and stress when wearing the functional compression pants. Furthermore, the ECG analysis results indicate statistically significant differences in the Low frequency (LF)/High frequency (HF) index, which reflect the overall balance of the autonomic nervous system and can be interpreted as feeling comfortable and balanced when wearing the functional compression pants. Moreover, subjective sense is discovered to be effective in assessing wear fit, ease of movement, skin friction, and wear comfort when wearing the functional compression pants.

• 한국의류학회지
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• 제33권10호
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• pp.1665-1672
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• 2009

This study examines the comfort of outdoorwear by electrocardiogram (ECG) and electroencephalogram (EEG) analyses. An experiment that consisted of rest (30 min), exercise (30 min), and recovery (20 min) periods was administered in a climate chamber with 10 healthy male participants. Two kinds of outdoorwear made of 100% cotton fabrics ('Control') and specially engineered fabrics having the feature of quick sweat absorbency and high speed drying fabric ('Functional') are evaluated in the experiment. ECG and EEG signals were obtained during the rest and recovery periods for the two outdoorwear conditions. The ECG analysis identified a smaller decrement of high frequency (HF) power for the 'Functional' when compared with the 'Control' during the recovery period. Next, the EEG analysis showed that the relative band powers of slow $\alpha$ and mid $\alpha$ increased for the 'Functional' while they decreased for the 'Control' and that the ratio of $\alpha$ power to high $\beta$ power was higher for the 'Functional'. The evaluation results indicate that the participants could remain relaxed more with less stress while wearing the functional outdoorwear that demonstrated the positive effects on autonomic nervous system (ANS) activities. The present study is significant in regard that use of ECG and EEG for the assessment of wear comfort is the first in the field of clothing and textile.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1910-1911
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• 2007

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. In conclusion, we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

• 감성과학
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• 제6권4호
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• pp.9-14
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• 2003

수면단계는 수면감을 평가하는 데 있어서 중요한 생리지표로서 사용되어 왔다. 그러나 수면다원검사를 이용한 전통적 수면단계 분류방법은 뇌전도(electroencephalogram : EEG), 안전도(electrooculogram : EOG), 심전도(electrocardiogram : ECG), 근전도(electromyogram : EMG) 등을 종합적으로 측정하므로 수면단계를 비교적 정확히 분류할 수 있지만 피험자에게 심한 구속감을 주는 문제가 있다. 본 연구에서는, 각성상태에서 교감신경계가 지배적인 반면에 수면 중에는 부교감 신경계가 더 활동적인 점에 착안하여 수면단계를 간단히 분류할 수 있는 방법을 찾고자 수면단계에 따른 심박동변이도(heart rate variability : HRY)를 분석하였다. 이 실험에는 건강한 대학생 6명이 2일씩 전체 12회의 야간수면에 참여하였다. 수면다원검사 장치를 이용하여 피험자들이 수면을 취하고 있는 동안, EEG, EOG, ECG, EMG(턱 및 다리)를 측정하여 수면단계를 "Standard scoring system for sleep stage"에 따라 자동으로 분류하였다. 그런 뒤, 본 연구를 통하여 제작된 Sleep Data Acquisition/Analysis 시스템을 이용하여 수면다원검사 장치로부터 ECG신호만 추출하여 HRV의 전력스펙트럼을 3개의 영역[저주파수대역(low frequency : LF), 중간주파수대역(medium frequency : MF), 고주파수대역(high frequency : HF)]으로 나누어 분석하였다. 단일채널 ECG를 이용하여 수면단계별로 HRV의 LF/HF를 분석한 결과, W(wakefulness)단계가 2단계에 비하여 325%높게(p＜.05), 3단계에 비하여 628%높게(p＜.001), 4단계에 비하여 800%높게(p＜.001) 나타났으며, 4단계는 REM(rapid eye movement)단계에 비하여 427% 낮게(p＜.05), 1단계에 비하여 418% 낮게(p＜.05) 나타났다. 또한 LF/HF가 수면단계에 따라 변화하는 양상은 W, REM, 1, 2, 3, 4단계의 순으로 단조 감소하였다. 한편, 수면단계별 MF/(LF+HF)의 차이는 유의하지 않았으나 표본집단의 기술통계치를 살펴본 바 REM단계와 3단계의 평균치가 가장 높았다.치가 가장 높았다.