• Journal of Navigation and Port Research
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• v.28 no.5
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• pp.449-454
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• 2004

These days ships medical treatment is under unfavorable circumstances, because there is no professional doctor who can diagnose and treat the patients accurately on the ships. Therefore, ship's telemedicine should be urgently actualized to elevate the crew's my of boarding life and welfare. A purpose of this research is to develope the telemedicine system which can acquire the patient's informations like ECG and phonocardiogram used for the diagnosis and transmit those to the doctors of shore medical center. In this paper, we designed the communication part which could transmit the bio-signals acquired from the developed ship's digital ECG and stethoscope based on personal computer by the INMARSAT Also we inspected data errors through the comparison between the sent and received data And we confirmed the possibility and compatibility of the telemedicine using ship's communication system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4443-4449
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• 2011

ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.

• Journal of Platform Technology
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• v.9 no.1
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• pp.40-45
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• 2021

Our daily lives have changed a lot because of the COVID-19 pandemic. This is essential to reduce the spread of COVID-19 due to public health measures such as social distancing. At the same time, however, they are exposed to depression, anxiety disorders, depressive disorders, trauma and stress-related disorders. For this, we developed an application that can check HRV data and stress index by measuring ECG and PPG based on a Smart Watch device that can measure bio-signals in real time. In the case of the developed application, the heart rate is measured and displayed in real time to deliver basic exercise information by measuring exercise, steps and calories, and total distance, and smoothly based on blood pressure, heart rate, and HRV data as a measurement program. Through this, I believe that the IoT Smart Stress Care Application, which can manage mental health by itself, will be helpful to patients with stress and depressive trauma disorders.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.233-234
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• 2012

무선 심전도 모니터링 기술로 인해 환자가 병원 내 어디에 있든 이들의 심전도를 원격에서 상시 관찰하는 것이 가능해 졌다. 그러나 무선 구간은 에러에 취약할 수 밖에 없으며 그 결과 원격 모니터에서 복원한 심전도 신호는 왜곡될 수 밖에 없다. 본 연구에서는 이러한 문제를 완화하여 복원 심전도 신호의 판독 가능성을 높이기 위한 두 가지 손실 데이터 보간 기법을 소개하고 그들의 성능을 평가한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.627-629
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• 2023

생체 신호를 분석하여 사용자의 건강과 정신 상태를 예측하고, 관련 질병에 관해 예방하는 연구가 늘어나고 있다. 생체 신호 중 심박은 사람의 육체, 정신적인 상태를 반영하는 대표적인 신호이지만 기존의 접촉 패드를 통한 ECG나 광학 센서를 통한 PPG로 심박을 예측할 때는 구속적인 환경이 필요하여 일상적인 상황 속에 적용하기 어려웠다. 이러한 단점을 해결하고자 본 논문은 UBFC-RPPG 데이터셋의 동영상 프레임을 RGB 채널마다 다른 가중치를 적용하는 전처리를 하여 학습 데이터의 크기를 줄이면서 정확도를 높이고, 3D-CNN을 활용한 딥러닝으로 순간적인 영상에서도 PPG 신호를 예측할 수 있도록 1초 전처리 영상을 학습한 후, 신호를 예측하는 것을 목표로 한다. 이렇게 비접촉식으로 취득된 신호는 더 다양한 환경에서의 감정분류, 우울증 진단, 질병 감지 등 다양한 분야에 활용될 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.126-134
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• 2017

There have been numerous studies on extracting the R-peak from electrocardiogram (ECG) signals. However, most of the detection methods are complicated to implement in a real-time portable electrocardiograph device and have the disadvantage of requiring a large amount of calculations. R-peak detection requires pre-processing and post-processing related to baseline drift and the removal of noise from the commercial power supply for ECG data. An adaptive filter technique is widely used for R-peak detection, but the R-peak value cannot be detected when the input is lower than a threshold value. Moreover, there is a problem in detecting the P-peak and T-peak values due to the derivation of an erroneous threshold value as a result of noise. We propose a robust R-peak detection algorithm with low complexity and simple computation to solve these problems. The proposed scheme removes the baseline drift in ECG signals using an adaptive filter to solve the problems involved in threshold extraction. We also propose a technique to extract the appropriate threshold value automatically using the minimum and maximum values of the filtered ECG signal. To detect the R-peak from the ECG signal, we propose a threshold neighborhood search technique. Through experiments, we confirmed the improvement of the R-peak detection accuracy of the proposed method and achieved a detection speed that is suitable for a mobile system by reducing the amount of calculation. The experimental results show that the heart rate detection accuracy and sensitivity were very high (about 100%).

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.269-278
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• 1998

This paper describes a development of efficient stress ECG signal analysis algorithm. The algorithm consists of wavelet adaptive filter(WAF), QRS detector and ST segment detector. The WAF consists of a wavelet transform and an adaptive filter. The wavelet transform decomposed the ECG signal into seven levels using wavelet function for each high frequency bank and low frequency bank. The adaptive filter used the signal of the seventh lowest frequency band among the wavelet transformed signals as primary input. For detection of QRS complex, we made summed signals that are composed of high frequency bands including frequency component of QRS complex and applied the adaptive threshold method changing the amplitude of threshold according to RR interval. For evaluation of the performance of the WAF, we used two baseline wandering elimination filters including a standard filter and a general adaptive filter. WAF showed a better performance than compared filters in the noise elimination characteristics and signal distortion. For evaluation of WAF showed a better performance than compared filters in the noise elimination characteristics and signal distortion. For evaluation of results of QRS complex detection, we compared our algorithm with existing algorithms using MIT/BIH database. Our algorithm using summed signals showed the accuracy of 99.67% and the higher performance of QRS detection than existing algorithms. Also, we used European ST-T database and patient data to evaluate measurement of the ST segment and could measure the ST segment adaptively according to change of heart rate.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.33-39
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• 2023

As the number of people with chronic diseases increases due to an aging society, it is urgent to prevent and manage their diseases. Although biometric authentication methods and Telemedicine Systems have been introduced to solve these problems, it is difficult to solve the security problem of medical information and personal authentication. Since smart healthcare includes personal medical information of subjects, the security of personal information is the most important field. Therefore, in this paper, we tried to propose a Telemedicine System using a smart wearable device ECG in the form of a wristband and face personal authentication in a private blockchain environment. This system targets various medical personnel and patients with chronic diseases in all regions, and uses a private blockchain that can increase data integrity and transparency, ECG and face authentication that are difficult to forge and alter and have high personal identification to provide a system with high security and reliability. composed. Through this, it is intended to contribute to increasing the efficiency of chronic disease management by focusing on disease prevention and health management for patients with chronic diseases at home.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.587-594
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• 2012

IEEE 802.15.6 standard technology is proposed for low-power wireless communication in, on and around body, where vital signs such as pulse, blood pressure, ECG, and EEG signals are transmitted as a type of data packet. Especially, these vital signs should be delivered in real time, so that the latency from slave node to hub node can be one of the pivotal performance requirements. However, in the case of IEEE 802.15.6 technology data retransmission caused by transmission failure can be done in the next superframe. In order to overcome this limitation, we propose an adaptive polling algorithm for IEEE 802.15.6 technology. The proposing algorithm makes the hub to look for an appropriate time period in order to make data retransmission within the superframe. Through the performance evaluation, the proposing algorithm achieves a 61% and a 73% latency reduction compared to those of IEEE 802.15.6 technology in the environment of 70% traffic offered load with 10ms and 100ms superframe period. In addition, the proposing algorithm prevents bursty traffic transmission condition caused by mixing retransmission traffic with the traffic reserved for transmission. Through the proposing adaptive polling algorithm, it will be possible to transmit time-sensitive vital signs without severe traffic delay.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.203-211
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• 2015

The implanted telemetry system provides the monitoring of species while they move within their cages. Species monitored include mice, rats, rabbits, dogs, pigs, primates, sheep, horses, cattle, and others. A miniature transmitter implanted in each animal measures one or more parameters. Parameters measured include arterial pressure, intra-pleural pressure, left ventricular pressure, intra-ocular pressure, bladder pressure, ECG, EMG, EEG, EOG, temperature, activity, and other parameters and transmits the data via radio frequency signals to a nearby receiver. Every conventional dedicated transmitter contains one or more sensors, cpu and battery. Due to the expected life of battery, the measuring time is limited. To overcome these problems, electromagnetic inductive coupling based wireless power transmission technology using multiple transmit coils were proposed. Each coil having different active area are driven by the coil driver. In this research, parallel resonance based coil driver was proposed. In addition, the device to detect where the receiver is positioned was proposed. From the experiments we show how to determine the driving condition of coil driver.