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

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.136-142
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• 2014

Distinct features in heart rate signals during the driver's wake and sleep states could provide an initiative for the development of a safe driving systems such as drowsiness detecting sensor in a smart wheel. We measured ECG from health subjects ($23.5{\pm}2.5$ in age) during the wake and drowsiness states. The proposed method is able to detect R waves and R-R interval calculation in the ECG even when the signal includes in abnormal signals. Heart rate variability(HRV) was investigated for the time domain and frequency domains. The STD HR(0.029), NN50(0.044) and VLF power(0.0018) of the RR interval series of the subjects were significantly different from those of the control group (p < 0.05). In conclusion, there are changes in heart rate from wake to drowsiness that are potentially to be detected. The results in our study could be useful for the development of drowsiness detection sensors for effective real-time monitoring.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.857-861
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• 2016

In this study, we proposed a new method that can be measure ECG (Electrocardiography) and PPG (Photoplethysmography) in realtime on the site of the wrist for check the state of health in daily life. For convenience measurement of ECG the lead I method was used on the wrist, and omit the reference junction ECG I was measured in the right hand and the left hand of the potential difference. Then the measured electrocardiogram was amplified by the differential amplifier and the signals were passed HPF, LPF, and BPF filters. For removing the PPG's noise from the Motion artifact and temperature, we apply the reflective photoelectric volume pulse wave measurement method using green LED as a light source. The circuits was designed to be able to check the waveform using higher active amplification method at weak signals. For the validation of our device, the measured signals were compared with E2-KIT on same time. The results shows that the error does not exceed the maximum one, most of the data is confirmed to be issued Peak inspection of the same number.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.432-440
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• 2023

This study introduces an efficient readout circuit designed for two-electrode electrocardiogram (ECG) recording, characterized by its low-noise and low-power consumption attributes. Unlike its three-electrode counterpart, the two-electrode ECG is susceptible to common-mode interference (CMI), causing signal distortion. To counter this, the proposed circuit integrates a common-mode charge pump (CMCP) with a window comparator, allowing for a CMI tolerance of up to 20 V_PP. The CMCP design prevents the activation of electrostatic discharge (ESD) diodes and becomes operational only when CMI surpasses the predetermined range set by the window comparator. This ensures power efficiency and minimizes intermodulation distortion (IMD) arising from switching noise. To maintain ECG signal accuracy, the circuit employs a chopper-stabilized instrumentation amplifier (IA) for low-noise attributes, and to achieve high input impedance, it incorporates a floating high-pass filter (HPF) and a current-feedback instrumentation amplifier (CFIA). This comprehensive design integrates various components, including a QRS peak detector and serial peripheral interface (SPI), into a single 0.18-㎛ CMOS chip occupying 0.54 mm². Experimental evaluations showed a 0.59 µV_RMS noise level within a 1-100 Hz bandwidth and a power draw of 23.83 µW at 1.8 V.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.27-33
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• 2009

With the upcoming u-healthcare era, a way of measurement for vital sign monitoring of cardiac patients is changing as well. In existing measurement of cardiac patients, various wire in ECG measuring equipment has caused much discomfort and inconvenience. In order to decrease the problem, we are developing an efficient measurement of ECG signal using Wireless sensor network. In this paper, we present a way to reduce amount of data by transmitting ECG data collected from radio electrocardiogram sensor based on Zigbee after calculating cardiac rate. And in order to control the error which can be caused by the different ECG signal intensity each individual can has, we also suggest an adaptive pulse measurement model which can measure heart rate with correcting according to different ECG intensity. To verify the suggested model, sensor application was developed and the data was acquired in TinyOS 2.0 environment and the adaptive pulse measurement model was evaluated through the data from the experiments.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.666-672
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• 2016

We extracted 50 LVH patients out of 30'~80's who performing ECG and echocardiography examination. We used Devereux's theory to examinate LVH with echocardiography and used Sokolow-Lyon's theory to examinate LVH with ECG. We used regression and correlation analysis by SPSS, used ROC curve analysis to decide predominance of two ways of .Age, BMI, SBP and DBP whice are the danger factors of LVH and standard value of LVH diagnosis examination seems correlated. Out of 50 LVH patients, 50 patients were diagnosed LVH by echcardiography examination and only 21 patients were diagnosed LVH by ECG examination. Also echocardiography was AUC 99%, sensitivity 96%, singularity 95%, accuracy 95.5%. And ECG was AUC 76%, sensitivity 62%, singularity 76%, accuracy 68%.By comparing accuracy between echocardiography and ECG in diagnosing LVH, we could tell echocardiography was examination with higher accuracy. Therefore, if one was diagnosed with summit on 1st examination with ECG, considering age, body mass index, systolic blood pressure and dilator blood pressure, should offer echocardiography examination.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.653-659
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• 2011

In general, electrocardiogram(ECG) signals are sampled with a frequency over 200Hz and stored for a long time. It is required to compress data efficiently for storing and transmitting them. In this paper, a method for compression of ECG data is proposed, using by Non Uniform B-spline approximation, which has been widely used to approximation theory of applied mathematics and geometric modeling. ECG signals are compressed and reconstructed using B-spline basis function which curve has local controllability and control a shape and curve in part. The proposed method selected additional knot with each step for minimizing reconstruction error and reduced time complexity. It is established that the proposed method using B-spline approximation has good compression ratio and reconstruct besides preserving all feature point of ECG signals, through the experimental results from MIT-BIH Arrhythmia database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.37-43
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• 2020

Recently, many researches have been actively to diagnose symptoms of heart disease using ECG signal, which is an electrical signal measuring heart status. In particular, the electrocardiogram signal can be used to monitor and diagnose arrhythmias that indicates an abnormal heart status. In this paper, we proposed 1-D convolutional neural network for arrhythmias classification systems. The proposed model consists of deep 11 layers which can learn to extract features and classify 5 types of arrhythmias. The simulation results over MIT-BIH arrhythmia database show that the learned neural network has more than 99% classification accuracy. It is analyzed that the more the number of convolutional kernels the network has, the more detailed characteristics of ECG signal resulted in better performance. Moreover, we implemented a practical application based on the proposed one to classify arrythmias in real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.9-14
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• 2003

Sleep stages have been useful indicator to check a person's comfortableness in a sleep, But the traditional method of scoring sleep stages with polysomnography based on the integrated analysis of the electroencephalogram(EEG), electrooculogram(EOG), electrocardiogram(ECG), and electromyogram(EMG) is too restrictive to take a comfortable sleep for the participants, While the sympathetic nervous system is predominant during a wakefulness, the parasympathetic nervous system is more active during a sleep, Cardiovascular function is controlled by this autonomic nervous system, So, we have interpreted the heart rate variability(HRV) among sleep stages to find a simple method of classifying sleep stages, Six healthy male college students participated, and 12 night sleeps were recorded in this research, Sleep stages based on the "Standard scoring system for sleep stage" were automatically classified with polysomnograph by measuring EEG, EOG, ECG, and EMG(chin and leg) for the six participants during sleeping, To extract only the ECG signals from the polysomnograph and to interpret the HRV, a Sleep Data Acquisition/Analysis System was devised in this research, The power spectrum of HRV was divided into three ranges; low frequency(LF), medium frequency(MF), and high frequency(HF), It showed that, the LF/HF ratio of the Stage W(Wakefulness) was 325% higher than that of the Stage 2(p＜.05), 628% higher than that of the Stage 3(p＜.001), and 800% higher than that of the Stage 4(p＜.001), Moreover, this ratio of the Stage 4 was 427% lower than that of the Stage REM (rapid eye movement) (p＜.05) and 418% lower than that of the Stage l(p＜.05), respectively, It was observed that the LF/HF ratio decreased monotonously as the sleep stage changes from the Stage W, Stage REM, Stage 1, Stage 2, Stage 3, to Stage 4, While the difference of the MF/(LF+HF) ratio among sleep Stages was not significant, it was higher in the Stage REM and Stage 3 than that of in the other sleep stages in view of descriptive statistic analysis for the sample group.