• 전자공학회논문지D
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• 제35D권7호
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• pp.40-47
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• 1998

This paper presents the physiological signal processing CMOS one chip for transmitting human bodys small electrical signals such as electrocardiogram(EKG) or electromyogram(EMG) and the external system for receiving signals was implemented by the commercial ICs. For simultaneous four-subject four-channel telemetry, a new enfored synchronization techniqeu using infrared bi-directional communication has been proposed. The telemeter IC with the size of 5.1*5.1mm$^{2}$ has the following functions: receiving of command signal, initialization of internal state of all functional blocks, decoding of subject-selection signal, time multiplexing of 4-channel modulated physiological signals, transmitting of telemetry signal to external system and auto power down control. The newly designed synchronized oscillator with low supply voltage dependence in the telemeter IC operates at a supply voltage from 4.6~6.0V and the nonlinearity error of PIM modulator was less than 1.2%F.S(full scale). The power saving block operates at the period of 2.5ms even if the telemetry IC does not receive command signal from external system for a constant time.

• Industrial Engineering and Management Systems
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• 제12권4호
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• pp.380-388
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• 2013

Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 추계학술대회
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• pp.110-113
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• 1990

A new approach to impulsive noise rejection and background normalization of digitized electrocardiogram signals is presented using mathematical morphological operators that incoporate the shape information of a signal. A brief introduction to these nonlinear signal processing operators, as well as detailed description of the new algorithm, is presented. Empirical results show that the new algorithm has good performance in impulsive noise rejection and background normalization.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권3호
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• pp.179-183
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• 2006

ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1993년도 춘계학술대회
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• pp.74-77
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• 1993

Estimation and removal of power line interference in the electrocardiogram using adaptive noise cancelling techniques is presented. The system identification concepts are used to design the noise cancelling filter and the prediction error method is used to adjust filter coefficients. Computer simulation were performed to compare this method with the Lekov's method.

• 대한인간공학회지
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• 제9권1호
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• pp.3-14
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• 1990

This article develops a cost-effective and accurate measurement system for heart best intervals. The system is composed of an analog to digital (A/D) converter, an IBM personal computer (an 8088 microprocessor, an 8253-5 timer, an 8259A interrupt controller, and memories) and assembler programs for controlling these hardware components. An exponential smoothing algorithm effectively reduced noise effects from A/D converted electrocardiogram (ECG) signals influenced by 60 Hz alternating current (AC). The system can collect 15000 heart beat intervals with an 1/5400 second unit.

• 한국멀티미디어학회논문지
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• 제20권3호
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• pp.465-473
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• 2017

Electrocardiogram(ECG) signal is one of the unique bio-signals of individuals and is used for personal authentication. The existing studies on personal authentication method using ECG signals show a high detection rate for a small group of candidates, but a low detection rate and increased execution time for a large group of candidates. In this paper, we propose a hierarchical algorithm that extracts fiducial points based on curvature of ECG signals as feature values for grouping candidates ​and identifies candidates using waveform-based comparisons. As a result of experiments on 74 ECG signal records of QT-DB provided by Physionet, the detection rate was about 97% at 3-heartbeat input and about 99% at 5-heartbeat input. The average execution time was 22.4 milliseconds. In conclusion, the proposed method improves the detection rate by the hierarchical personal authentication process, and also shows reduced amount of computation which is plausible in real-time personal authentication usage in the future.

• Journal of Electrical Engineering and Technology
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• 제3권4호
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• pp.576-583
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• 2008

In this study, we attempt to design a real-time autonomic nervous system(ANS) evaluation system usable during exercise using heart instantaneous frequency(HIF). Although heart rate variability(HRV) is considered to be a representative signal widely used ANS evaluation system, the R-peak detection process must be included to obtain an HRV signal, which involves a high sampling frequency and interpolation process. In particular, it cannot accurately evaluate the ANS using HRV signals during exercise because it is difficult to detect the R-peak of electrocardiogram(ECG) signals with exposure to many noises during exercise. Therefore, in this study, we develop the ground for a system that can analyze an ANS in real-time by using the HIF signal circumventing the problem of the HRV signal during exercise. First, we compare the HRV and HIF signals in order to prove that the HIF signal is more efficient for ANS analysis than HRV signals during exercise. Further, we performed real-time ANS analysis using HIF and confirmed that the exerciser's ANS variation experiences massive surges at points of acceleration and deceleration of the treadmill(similar to HRV).

• 대한인간공학회지
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• 제31권2호
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• International Journal of Control, Automation, and Systems
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• 제3권1호
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• pp.137-142
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• 2005

Biomedical signals such as ECG, EMG, and EEG are extremely important in the diagnosis of patients. It is difficult to filter noise from these signals, and errors resulting from filtering can distort a biomedical signal. Existing systems have shown poor performance when complicated noise appears. Adaptive filtering is selected to contend with these defects. Existing adaptive filters can adjust the filter coefficient with the given filter order, but they can produce an unsuitable order in different environments. In order to solve this problem, an optimal adaptive filter with a dynamic structure was designed. Positive experimental results were obtained.