• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.1-10
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• 2020

In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.109-114
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• 2010

In this paper, the implementation of a human signal sensing module that has capabilities to check and restore the weak signals from the human body is presented. A module presented in this paper consists of processing and sensing elements related to human pulse and body temperature and a controller implemented with SoC design method. PPG data is detected by a noise filtering process toward the amplified signal which is from the operating frequency between 0.1Hz - 10Hz. A digital temperature sensor is used to check the body temperature. A sensor outputs the corresponding value of the electric voltage according to the body temperature. Moreover, this paper discusses the implementation of an enhanced microprocessor which is synthesized with VHDL as a part of the SoC development and used to control the entire module. The SoC processor is implemented on a Xilinx Spartan 3 XC3S1000 device and has the achieved operating frequency of 10MHz. The implemented SoC processor core is successfully tested with macro memories in FPGA and the experimental results are hereby shown.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.191-198
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• 2022

The most basic thing to measure the condition of a companion animal is to check the breathing and pulse. There are several methods to measure the breathing and pulse of a companion animal, and the PPG method is generally used to measure the oxygen saturation (SpO₂) in a companion animal. However, since the input PPG signal is inputted with various information as well as oxygen saturation, it is necessary to separate and extract oxygen saturation information from the PPG signal in order to measure the oxygen saturation. Therefore, in this paper, a wearable measuring device for companion animals that can be measured by applying the PPG method was designed and implemented, and an algorithm for separating oxygen saturation information from the PPG signal input through the wearable measuring device was proposed.