• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.313-316
• /
• 2009

A novel approach for electrocardiogram (ECG) analysis within a functional sensor node has been developed and evaluated. The main aim is to reduce data collision, traffic over loads and power consumption in healthcare applications of wireless sensor networks (WSN). The sensor node attached on the patient's bodysurface around the heart can perform ECG analysis based on a QRS detection algorithm to detect abnormal condition of the patient. Data transfer is activated only after detected abnormality in the ECG. This system can reduce packet loss during transmission by reducing traffic overload. In addition, it saves power supply energy leading to more reliable, cheap and user-friendly operation in the WSN based ubiquitous health monitoring.

• Journal of IKEEE
• /
• v.22 no.4
• /
• pp.953-958
• /
• 2018

In this study, a portable detection system was developed that can detect harmful gas and signals simultaneously in an enclosed space of industrial sites and underground facilities. The developed system is a sensor module for gas detection, a patch type 1 channel small ECG sensor, a module for three-axial acceleration detection sensor, and a system for statistics. In order to verify the performance of the system modules, the digital resolution, signal frequency, output voltage, and ultra-small modules were evaluated. As a result of the performance of the developed system, the digital resolution was 300 (rps) and the signal amplification gain was 500 dB or more, and the ECG module was manufactured with $50mm{\times}10mm{\times}10mm$ to increase patch utilization. It is believed that the product of this research will be valuable if it is used as an IoT-based management system for real-time monitoring of industrial workers.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.7
• /
• pp.29-36
• /
• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.29 no.3
• /
• pp.134-141
• /
• 2017

The purpose of this study was to analyze physiological changes via a cold air reaction experiment to generate basic data that are useful for the development of an automobile active air conditioning system to prevent drowsiness. The $CO_2$ concentration causing drowsiness in vehicle operation was kept below a certain level. Air was blown to the driver's face by using an indoor air cooling apparatus. Sleepiness and the arousal state of the driver in cold wind were measured by physiological signals. It was evident in the EEG that alpha waves decreased and beta waves increased, caused by cold air stimulation. The ${\alpha}/{\beta}$ ratio was reduced by about 52.9% and an alert state confirmed. In the electrocardiogram analysis, the efficiency of cold air stimulation was confirmed by the mean heart rate interval change. The R-R interval had a delay time of about one minute compared to the EEG response. The findings confirmed an arousal effect from sleepiness due to cold air stimulation.

• The Korean Journal of Applied Statistics
• /
• v.26 no.2
• /
• pp.335-347
• /
• 2013

There are various types of real world signals. For example, an electrocardiogram(ECG) represents myocardium activities (contraction and relaxation) according to the beating of the heart. ECG can be expressed as the fluctuation of ampere ratings over time. A signal is a composite of various types of signals. An orchestra (which boasts a beautiful melody) consists of a variety of instruments with a unique frequency; subsequently, each sound is combined to form a perfect harmony. Various research on how to to decompose mixed stationary signals have been conducted. In the case of non-stationary signals, there is a limitation to use methodologies for stationary signals. Huang et al. (1998) proposed empirical mode decomposition(EMD) to deal with non-stationarity. EMD provides a data-driven approach to decompose a signal into intrinsic mode functions according to local oscillation through the identification of local extrema. However, due to the repeating process in the construction of envelopes, EMD algorithm is not efficient and not robust to a noise, and its computational complexity tends to increase as the size of a signal grows. In this research, we propose a new method to extract a local oscillation embedded in a signal by utilizing the second derivative.

• Science of Emotion and Sensibility
• /
• v.22 no.1
• /
• pp.45-54
• /
• 2019

Empathy has been observed to be pivotal in enhancing both social relations and the efficiency of task performance. Empathetic interaction has been shown to begin with individuals mirroring each other's facial expressions, vocal tone, actions, and so on. The internal responses of the cardiovascular activity of people engaged in empathetic interaction are also known to be synchronized. This study attempted to objectively and quantitatively define the rules of empathy with regard to the synchronization of cardiac rhythm between persons. Seventy-four subjects participated in the investigation and were paired to imitate the facial expressions of their partner. An electrocardiogram (ECG) measurement was taken as the participants conducted the task. Quantitative indicators were extracted from the heart rhythm pattern (HRP) and the heart rhythm coherence (HRC) to determine the difference of synchronization of heart rhythms between two individuals as they pertained to empathy. Statistical significance was confirmed by an independent sample t-test. The HRP and HRC correlation(r) between persons increased significantly with empathy in comparison to an interaction that was not empathetic. A difference of the standard deviation of NN intervals (SDNN) and the dominant peak frequency decreased. Therefore, significant parameters to evaluate empathy have been proposed through a step-wise discrimination analysis. Empathic interactions may thus be managed and monitored for high quality social interaction and communication.

• Journal of Biomedical Engineering Research
• /
• v.20 no.4
• /
• pp.461-469
• /
• 1999

In this paper, an ECG-NIBP patient monitor is designed. This is an essential equipment to measure and monitor patient's physical condition - electrocardiogram(ECG) wave, heart rate(HR), and noninvasive blood pressure(NIBP) - in ICU, CCU, and operating room. The ECG is an electrical waveform produced by relaxation and contraction of the cardiac muscle. Most physicians diagnose patient's cardiac states from ECG pattern. A blood pressure is one of the clinical indexes measured in a emergency room or operating room. In this paper, the blood pressure is measured in artery by using the nonivasive oscillometric method. The developed patient monitor was inspected and compared with other instruments in operating rooms. The results were 1bpm of maximum difference in the heart rate, 15mmHg in the systolic pressure, 16mmHg in the diastolic pressure, and 25mmHg in the mean blood pressure. But the total results were 0.15bpm of the mean difference in the heart rate, 5mmHg in the systolic pressure, 10mmHg in the diastolic pressure, and 9mmHg in the mean blood pressure. The designed ECG-NIBP patient monitor can measure the ECG wave, HR, and BP. And the multi-tasking module of pulse oximetry . respiration . temperature monitor will be added in the near future.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.9
• /
• pp.2105-2112
• /
• 2010

The noise component of electrocardiogram is not distributed in a certain frequency band. It is expressed in various types of signals by rater's physical and environmental conditions. Particularly, since the baseline wander is occurred by the mixture of the original signal and 0 ~ 2 [Hz] range of the frequency components according to muscle constraction of part attaching to an electrode and respiration rythm, it makes the ECG signal analysis difficult. Several methods have been proposed to eliminate the wandering effectually. However, they have some problems. In some methods, the high processing time is required due to the computational complexity, while in other cases ECG signal morphology can be distorted. This paper suggests a simple and effective algorithm that eliminates baseline wander with low computational complexity and without distorting signal morphology. First, the algorithm detects and segments a baseline wandering interval using slope analysis and curve point detection, second, approximates the wandering in the interval as a sinusoid, and then subtracts the sinusoid from signal. Finally, ecg signals without baseline wander are obtained. In order to evaluate the performance of the algorithm, several ECG signals with baseline wandering in MIT/BIH ECG database 101, 111, 113, 234 record were chosen and applied to the algorithm. It is found that the algorithm removes baseline wanders effectively without significant morphological distortion.

• 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 Korea Academia-Industrial cooperation Society
• /
• v.18 no.5
• /
• pp.10-15
• /
• 2017

Research interest has strongly focused on developing a method for effectively transmitting bio-signals over a distance using a wireless wearable device. In this paper, we describe a procedure for the design and fabrication of a wearable antenna based on embroidering conductive threads to clothing capable of transmitting electrocardiogram signals. 3D electromagnetic simulation software and embroidery software were used to design and fabricate the conductive thread-based antenna, respectively. The measurement results show that the reflection coefficient of the fabricated antenna prototype exhibits excellent antenna impedance matching characteristics of less than -10dB in the Zigbee 2.4GHz frequency band. We also verified that the electrocardiogram data could be effectively received and monitored in real-time by a receiver 220m away from the transmitter.