• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2011.11a
• /
• pp.21-22
• /
• 2011

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.4
• /
• pp.181-187
• /
• 2005

In this study, a microchip system fabricated with MEMS technology was developed to detect bioelectrical signals. The developed microchip using the conductivity of gold nanoparticles could detect the biopotential with a high sensitivity. For designing the microchip, simulations were performed to understand the effects of the size and number of nanoparticles, and the sensing width between electrodes on the detection of biosignals. Then, a series of experiment was performed to validate the simulation results and understand the feasibility of the proposed microchip design. Both simulation and experimental results showed that as the sensing width between electrodes increased the conductivity decreased. Also, the conductivity increased as the density of gold nanoparticles increased. In addition, it was found that the conductivity that changes with the nanoparticles density could be approximated by a cumulative normal distribution function. The developed microchip system could effectively apply when a biosignals should be measured with a high sensitivity.

• Journal of Biomedical Engineering Research
• /
• v.22 no.3
• /
• pp.275-283
• /
• 2001

This study was performed to extract and analyze the biosignals to find the relationship between the level of anesthesia and the variations of physiological parameters during epidural anesthesia. Seven male and twenty female patients(ages from 45 to 70 years old) were participated for the experiment, and ECGs, PPGs, SKTs, SCRs were obtained during anesthesia. As results, the HF/LF ratios of HRV were decreased after the injection anesthetics. For skin temperatures, values measured from the palm was reduced and the temperatures from four channels, measured from armpit through the right side of the body, were increased. SCRs were decreased for all channels after the injection of anesthetics. However the heart rate and PPGs showed no significant changes. It was concluded that the injection of anesthetics result the changes in biosignals, and it could be explained by the degree of the sympathetic and/or parasympathetic nerve activities. Results of this study could provide the valuable information for the estimation of level for the spinal and general anesthesia, and could be extended to the development of a system which could quantify the level of anesthesia.

• Journal of Information Processing Systems
• /
• v.7 no.4
• /
• pp.717-732
• /
• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• Journal of Biomedical Engineering Research
• /
• v.24 no.5
• /
• pp.465-471
• /
• 2003

The purpose of this research was to evaluate chronic stress using physiological parameters. Wistar rats were exposed to the sound stress for 14 days. Biosignals were acquired hourly. To develop a fuzzy inference system which can integrate physiological parameters. the parameters of the system were adjusted by the adaptive neuro-fuzzy inference system. Of the training dataset, input dataset was the physiological parameters from the biosignals and output dataset was the target values from the cortisol production. Physiological parameters were integrated using the fuzzy inference system. then 24-hour results were analyzed by the Cosinor method. Chronic stress was evaluated from the degree of circadian rhythm disturbance. Suppose that the degree of stress for initial rest period is 1. Then. the degree of stress after 14-day sound stress increased to 1.37, and increased to 1.47 after the 7-day recovery period. That is, the rat was exposed to 37%-increased amount of stress by the 14-day sound and did not recover after the 7-day recovery period.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.7
• /
• pp.97-106
• /
• 2012

Management of biosignal data in mobile devices causes many problems in real-time transmission of large volume of multimedia data or storage devices. Therefore, this research paper intends to suggest an m-Health system, a clinical data processing system using mobile in order to provide quick medical service. This system deployed health system on IP network, compounded outputs from many bio sensing in remote sites and performed integrated data processing electronically on various bio sensors. The m-health system measures and monitors various biosignals and sends them to data servers of remote hospitals. It is an Android-based mobile application which patients and their family and medical staff can use anywhere anytime. Medical staff access patient data from hospital data servers and provide feedback on medical diagnosis and prescription to patients or users. Video stream for patient monitoring uses a scalable transcoding technique to decides data size appropriate for network traffic and sends video stream, remarkably reducing loads of mobile systems and networks.

• Journal of Biomedical Engineering Research
• /
• v.41 no.1
• /
• pp.42-47
• /
• 2020

A personal authentication system based on biosignals has received increasing attention due to its relatively high security as compared to traditional authentication systems based on a key and password. Electrocardiography (ECG) measured from the chest or wrist is one of the widely used biosignals to develop a personal authentication system. In this study, we investigated the feasibility of using similar ECG measured behind the ears to develop a personal authentication system. To this end, similar ECGs were measured from thirty subjects using a pair of three electrodes attached behind each of the ears during resting state during which the standard Lead-I ECG was also simultaneously measured from both wrists as baseline ECG. The three ECG components, Q, R, and S, were extracted for each subject as classification features, and authentication accuracy was estimated using support vector machine (SVM) based on a 5×5-fold cross-validation. The mean authentication accuracies of Lead I-ECG and similar ECG were 90.41 ± 8.26% and 81.15 ± 7.54%, respectively. Considering a chance level of 3.33% (=1/30), the mean authentication performance of similar ECG could demonstrate the feasibility of using similar ECG measured behind the ears on the development of a personal authentication system.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.22 no.1
• /
• pp.57-67
• /
• 2018

Objectives The objective of this study is to examine the interpretability of the questionnaire-based pattern identification in terms of biosignals. For this purpose, we investigate the relationship between electrochemical skin conductance (ESC) and Qi-Blood-Yin-Yang Deficiency Questionnaire (QBYY-Q) in diabetic patients. Methods A total of 40 patients with diabetes mellitus answered the QBYY-Q and their ESC were measured by SUDOSCAN device (a diabetes screening device, France). To analyze the relationship between QBYY-Q and ESC, ANOVA analysis and Scheffe test were performed and Pearson correlation coefficients were obtained. Results Of the 40 diabetic patients, 23 (57.5%) were males and 17 (42.5%) were females. According to the QBYY-Q, 9 patients were classified into Qi deficiency pattern (QD), 9 patients were Blood deficiency pattern (BD), 10 patients were Yin deficiency pattern (YiD) and 12 patients were Yang deficiency pattern (YaD). Demographic information (age, body mass index, duration of illness, etc.), signs of vitality (blood pressure, body temperature, etc.), fasting plasma glucose and glycated hemoglobin were not significantly different in each deficiency pattern. The ESC of the right leg was significantly lower in the BD group compared to the YiD group (p<0.022). Pearson's correlation coefficient was negatively correlated with the BD questionnaire score (r=-0.343, p <0.05). Finally, ESC showed a positive correlation with hemoglobin and erythrocyte levels in all limbs (r=0.483, p<0.01). Conclusions We showed that ESC could be used to classify the Deficiency pattern identifications in diabetic patients. Especially, the ESC was significantly lower in the BD group and was negatively correlated with the BD scores. It implies the potential utility of the ESC to understand the BD in terms of modern biosignals.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.905-910
• /
• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.1-9
• /
• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.