• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.6
• /
• pp.1257-1264
• /
• 2019

The traditional cuff-based method for BP(Blood Pressure) measurement is not suitable for continuous real-time BP measurement techniques. For this reason, the previous studies estimated various blood pressures by fusion with the electrocardiography (ECG) and photoplethysmogram (PPG) sensor signals. However, conventional techniques based on PPG bio-sensing measurement face many challenging issues such as noisy supply fluctuation, small pulsation, and drifting non-pulsatile. This paper proposed a novel BP estimation methods using PPG and ECG sensors, which can be derived from the relationship between PPG and ECG using PTT(Pulse Transit Time) and PWV(Pulse Wave Velocity). Unlike conventional height ratio features, which are extracted on the basis of the peaks in the PPG and ECG waveform. The proposed method can be reliably obtained even if there are missing peaks among the sensed PPG signal. The increased reliability comes from periodical estimation of the peak-to-peak interval time using ECG and PPG. After 250,000 times trials of the blood pressure measurement, the proposed estimation technique was verified with the accuracy of ±28.5% error, compared to a commercialized BP device.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.10
• /
• pp.1469-1476
• /
• 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 Institute of Electronics and Information Engineers
• /
• v.53 no.12
• /
• pp.82-87
• /
• 2016

The photoplethysmogram (PPG) signal is one of the mainly considered bio signals along with the electrocardiogram (ECG) signal. PPG signals can be used to estimate the speed of flow of blood in vein, saturation of peripheral oxygen and etc. The heartbeat rate is a common feature in order to evaluate those checkup lists. To estimate the correct heartbeat rate, dynamic noises must be removed in the PPG signal. Conventionally, the acceleration signal is used to remove dynamic noises. This method, however, increases the computational complexity. In this paper, we proposes a solution that uses only PPG signals to calculate the heartbeat rate, and which can be used as a basement in real-time healthcare solution.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.7
• /
• pp.1020-1026
• /
• 2013

Multirate filtering process on the biological signals like Electrocardiogram (ECG) and Photoplethysmogram (PPG) can be defined as the digital signal processing algorithm in which the sampling rate varies to omit or interpolate the intermediate values between the sampled data. With this aim, we suggest a new multirate filtering algorithm by deleting the extraneous data to eliminate the unwanted degradations such as granular noise due to the usage of high sampling frequency and simultaneously to detect the fiducial features of ECG and PPG with reducing the complexity of resolving fiducial points such as R-peak, Pulse peak and Pulse Transit Time (PTT). After the experimental simulations performed, we can conclude the fact that we can detect the fiducial features of ECG and PPG signal in terms of R-peak, Pulse peak and PTT without the loss of accuracy even if we do not maintain the original sampling frequency.

• Science of Emotion and Sensibility
• /
• v.11 no.2
• /
• pp.235-244
• /
• 2008

This study is to develop automatic extraction system of continuous blood pressure using ECG (Electrocardiogram) and PPG(Photoplethysmography) for u-health care technology. PTT (Pulse Transit Time) was determined from peak difference between ECG and PPG and its inverse made to get blood pressure. Since the peaks were vulnerable to be contaminated from noise and variation of amplitude, this study developed the adaptive algorithm for peak calculation in any noise condition. The developed method of the adaptive peak calculation was proven to make the standard deviations of PPT decrease to 28% and the detection of noise increase to 18%. Also, the correlation model such as blood pressure = -0.044 $\cdot$ PTT + 133.592 has successfully been determined for predicting the continuous pressure measured without using cuff but with using PPG and ECG, only.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.438-440
• /
• 2009

사람의 심박동은 심장 상태와 구조, 나이 등의 여러 요소에 의하여 고유한 특성을 갖고, 이러한 특성을 이용하여 개인 인증에 심박동을 적용할 수 있다. 본 논문에서는 기존의 심전도(ECG, electrocardiogram) 방법을 대신하여 빛을 이용한 광전용적맥파(PPG, Photo Plethysmogram)를 측정, 신호를 분석하였다.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.6
• /
• pp.1194-1199
• /
• 2009

Heart rate may be a very important parameter in human health. To extract heart rate, the electrocardiogram(ECG) is commonly used. But the ECG acquisition procedure is very complex. On the other hand, the acquisition of pulse wave or photoplethysmogram(PPG) is very easy. However, the peak of PPG is not so sharp as ECG. This study tries to enhance the performance of peak detection in PPG signal. The method uses the average slopes around the main peak. The crossing point of the increasing and the decreasing slopes is selected as the peak point of heart rate period. The proposed method showed smoothed heart rate graph and reduced irregularity in heart rate values.

• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.137-142
• /
• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1978_1979
• /
• 2009

Heart rate may be a very important parameter in human health. To extract heart rate, the electrocardiogram(ECG) is commonly used. But the ECG acquisition procedure is somewhat complex. On the other hand, the acquisition of pulse wave or photoplethysmogram(PPG) is very easy. However, the peak of PPG is not so sharp as ECG. This study tries to enhance the performance of period detection in PPG signal. The method uses the average slopes around the main peak. The crossing point of the increasing and the decreasing slopes is selected as the peak point of heart rate period. The proposed method showed smoothed heart rate graph and reduced irregularity in heart rate values.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.834-837
• /
• 2005

In this paper, we describe the method of non-invasive blood pressure measurement using pulse wave transit time(PWTT). PWTT is a new parameter involved with a vascular that can indicate the change of BP. PWTT is measured by continuous monitoring of ECG and pulse wave. No additional sensors or modules are required. In many cases, the change of PWTT correlates with the change of BP. We measure pulse wave using the photo plethysmograph(PPG) sensor in an earlobe and we measure ECG using the ECG monitoring device our made in the chest. The measurement device for detecting pulse wave consists of infrared LED for transmitted light illumination, pin photodiode as light detector, amplifier and filter. We composed 0.5Hz high pass, 60Hz notch and 10Hz low pass filter. ECG measurement device consists of multiplexer, amplifier, filter, micro-controller and RF module. After amplification and filtering, ECG signal and pulse wave is fed through micro-controller. We performed the initial work towards the development of ambulatory BP monitoring system using PWTT. An earlobe is suitable place to measure PPG signal without the restraint in daily work. From the results, we can know that the dependence of PWTT on BP is almost linear and it is possible to monitoring an individual BP continuously after the individual calibration.