• International Journal of Advanced Culture Technology
• /
• v.11 no.2
• /
• pp.310-314
• /
• 2023

In this paper, we show that the limitations of Holter monitoring and Wearable Electrocardiogarphy Devices and their arrhythmia detection. Sudden death caused by cardiovascular disease, often referred to as the "silent killer" due to its unpredictable nature, is a major health concern. Electrocardiography (ECG) is a basic diagnostic tool for detecting heart disease, but its limitations make it difficult to detect arrhythmia, a significant indicator of an irregular heart state. To address this limitation, a long-term continuous ECG recording device has been developed, Holter ECG device and wearable device. A significant number of studies have focused on the differences between Holter monitoring and wearable devices. The Holter tests were useful for detecting regularly occurring arrhythmias, whereas wearable patches were better at detecting random and infrequent arrhythmias. Wearable patches were effective in detecting episodes of arrhythmia and myocardial ischemia. Despite the concern, wearable devices had less signal loss than Holter monitoring and patients also preferred wearable devices over Holter monitoring due to convenience. These results could mean that the wearable devices can perfectly replace the Holter test.

• Journal of electromagnetic engineering and science
• /
• v.3 no.1
• /
• pp.45-49
• /
• 2003

Wireless life signal detecting system is implemented with using the mechanism of Doppler Effect. This system can measure the respiration and heart rates with the periodic movement of skin and muscle near the heart. The system is consisted of antenna, RF transmitter, receiver, and display part. We did use two operating frequencies at 1.9 ㎓ and 10 ㎓. Firstly, the link budget about detecting system is analyzed and the signal detected from the system is compared with electrocardiogram(ECG) of monitor which is using for patient monitoring in hospital. Secondly, the detection of vital sign is also performed according to the different distances, and including behind the wall.

• Proceedings of the KOSOMBE Conference
• /
• v.1994 no.05
• /
• pp.49-52
• /
• 1994

We have developed a biological signal measurement, archiving, and communication system (SiMACS). The front end of the system is the intelligent data processing unit (IDPU) which includes ECG, EEG, EMG, blood pressure, respiration, temperature measurement modules, module control and data acquisition unit, real-time display and signal processing unit. IDPUS are connected to central data base unit through LAN(Ethernet). Workstations which receive signals from central DB and provide various signal analysis tools are also connected to the network. The developed PC-based SiMACS is described.

• Journal of Digital Convergence
• /
• v.14 no.9
• /
• pp.331-339
• /
• 2016

Chronic diseases are increasing nowadays as daily habits changed due to economic growth. Among chronic diseases, heart cerebrovascular disease is one of the major causes of death in South Korea that accounts for approximately 20% of mortality. Tests to measure anomaly of the heart is ECG tests, which measures and analyzes the electrical heart activity. Any mistakes in lead attachment location critically affects ECG testings, and statistical facts showed that only 2.8% of the nurses properly located leads to patients. As a solution, this paper proposes a system based on a projection-based augmented reality technology to generate X-ray images to the patient's chest to point out exact attachment locations of ECG leads. Evaluation comparison results showed a 2.6 cm difference between the conventional system and the proposed system. ECG test results also showed significant signal differences between the systems in leads V1, V2, and V3. The ECG measured accurately by the proposed system would help greatly in patient management and clinical decisions of clinicians.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.10
• /
• pp.207-216
• /
• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2009.11a
• /
• pp.186-189
• /
• 2009

본 연구에서는 폭력영상 시청 시 개인의 공격성향에 따른 생체신호(ECG, GSR)의 변화를 분석하였다. 남자 23 명(21.4 세, ${\pm}1.8$ 세)의 피험자의 공격성을 설문지로 측정하였다. 실험은 Rest(15 분), 안정영상 시청(2 분 14 초), 폭력영상 시청(50 초), 안정영상 시청(2 분 14 초)으로 구성되어 있다. 폭력영상은 패싸움·마루타 실험장면을 보여주었으며, 안정영상은 바다·산·계곡 등의 영상을 보여주었다. 폭력영상 시청 시 개인의 공격성향과 ECG 신호 사이의 상관관계는 관찰 할 수 없었으나, GSR 신호와는 음의 상관관계를 관찰 할 수 있었다. 이 결과는 공격성이 높은 사람일수록 폭력영상 시청 시 생체신호 변화가 작을 수 있음을 시사한다.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.453-456
• /
• 2005

일반적으로 심전도는 심장계통의 질환을 판단할 때 사용된다. 이러한 심장질환의 이상 유무를 자동으로 진단하기 위해서는 QRS파형 검출을 필요로 하며, 이를 위하여 웨이블렛변환 방법이나 템플릿매칭, 룰 베이스 방법 등 여러 가지 방법들이 쓰이고 있으나, 심전도 신호가 표준화된 형태를 갖지 않는 경우는 검출 능력에 많은 한계를 갖고 있다. 본 논문은 파형의 베이스라인(baseline)을 기준으로 진폭 값에 절대치을 취하는 방법으로 파형의 R피크값을 검출하는 알고리즘을 제안한다. 결과를 검증하기 위해 MIT-BIH 데이타베이스에서 제공하는 데이터와 R피크값을 본 논문의 알고리즘으로 추출된 R피크값과 비교한 결과 96.7%의 검출률을 보였다.

• Progress in Medical Physics
• /
• v.11 no.2
• /
• pp.141-146
• /
• 2000

Power spectrum analysis is a powerful noninvasive tool for quantifying autonomic nervous system activity. In this paper, We developed a measuring system for Autonomic Nervous Activity by using power spectrum analysis method to obtain the activities of autonomic nervous system. This system adopt a isolated power for patient's safety. In this system, Two output signal is obtained - R-R interval time variability and Respiration time variability. Time variability is use to find out some disease related to Autonomic Nervous System. Experimental tested range is 30 ～ 240 BPM for ECG and 15～80 BPM for Respiration.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2003.05a
• /
• pp.1-4
• /
• 2003

본 연구는 경편성물의 특성에 영향을 미치는 중요한 구조변수인 조직, 편환의 계폐여부, 가이드바의 상호 움직임 방향에 따른 소리특성과 생리적 반응 사이의 관계를 파악함으로써 총합적 감성소재 설계를 위한 기초 자료를 제공하고자 하였다. 직물소리발생장치를 이용하여 7가지의 경편성물에 대한 마찰음을 녹음한 후, 이를 마찰음이 유발하는 생리반응을 측정하여 slow alpha파, fast beta파, ECG R-R, RESP, HF/LF, SCL, PV등을 분석하였다. 조직에 따라서는 LPT, Loundness(Z), Roughness(Z)와 Fluctuation strength(Z) 가장 큰 Sharkskin에서 fast beta파, ECG R-R, RESP는 증가하였으며 slow alpha파, PV, HF/LF는 감소하였다. 편환의 개폐여부에서는 폐환보다는 개환에서 Loudness(Z), Sharpness(Z), Fluctuation strength(Z)가 증가하였으며 RESP는 증가하고 HF/LF는 감소하여 개환이 더 불쾌한 감성을 유발하였다. 가이드바의 상호 움직임 방향은 counter보다는 parallel에서 slow alpha의 변화량이 감소하고 fast beta의 변화량이 증가하여 counter보다는 parallel이 더 불쾌한 감성을 유발하는 것으로 보인다. 또한 편성물의 음향특성 중 ΔL, Δf 와 Roughness(Z), Fluctuation Strength(Z)가 생리반응을 예측하는 중요한 요인으로써 나타났다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.3 no.3
• /
• pp.45-50
• /
• 2010

In this study, Discrete Wavelet Transform(DWT), which can detect more correct QRS complex, approximated through impulse response for reducing complexity to suit real-time system during exercise. Also, rhythm information, which is Arrythmia, Bradycardia and Tachycardia, is provided through continuously monitoring QRS complex. Proposed algorithm is evaluated by computer simulation of ECG signal that is measured during exercise.