• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.216-221
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• 1999

A mental task combined with noise background is an effective model of laboratory stress for study of psychophysiology of the autonomic nervous system (ANS). The intensity of the background noise significantly affects both a subjective evaluation of experienced stress level during test and the physiological responses associated with mental load in noisy environments. Providing tests of similar difficulties we manipulated the background noise intensity as a main factor influencing a psychophysiological outcome and the analyzed reactivity along withe the noise intensity dimension. The goal of this study was to identify the patterns of ANS responses and the relevant subjective stress scores during performance of word recognition tasks on the background of white noise (WN) of the different intensities (55, 70 and 85 dB). Subjects were 27 college students (19-24 years old). BIOPAC, Grass Neurodata System and AcqKnowlwdge 3.5 software were used to record ECG, PPG, SCL, skin temperature, and respiration. Experimental manipulations were effective in producing subjective and physiological responses usually associated with stress. The results suggested that the following potential autonomic mechanisms might be involved in the mediation of the observed physiological responses: A sympathetic activation with parasympathetic withdrawal during mild 55 and 70dB noise (featured by similar profiles) and simultaneous activation of sympathetic and parasympathetic systems during intense 85dB WN. The parasympathetic activation in this case might be a compensatory effect directed to prevent sympathetic domination and to maintain optimal arousal state for the successful performance on mental stress task. It should be mentioned that obtained results partially support Gellhorn's (1960; 1970) "tuning phenomenon" as a possible mechanism underlying stress response.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.287-292
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• 2003

Even though MCG has many advantages over ECG, MCG signa)s are easily corrupted by external magnetic noises Since multi-channel MCG signals are recorded simultaneously at many spatial positions, it is effective to apply spatial fitters as well as the conventional temporal filters to remove external magnetic noises. The spatial filters can be designed by utilizing the fact that the noise signals caused by external noise sources are more spatially correlated than the original MCG signals. In this paper, we introduce a spatial filtering method for the noise reduction in MCG based on the principal component analysis. Healthy volunteer study results obtained with a 61-channel MCG system are presented.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.49-56
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• 1995

This paper is a study on the design of adptive filter for QRS complex detection. We propose a simple adaptive algorithm to increase capability of noise cancelation in QRS complex detection with two stage adaptive filter. At the first stage, background noise is removed and at the next stage, only spectrum of QRS complex components is passed. Two adaptive filters can afford to keep track of the changes of both noise and QRS complex. Each adaptive filter consists of prediction error filter and FIR filter. The impulse response of FIR filter uses coefficients of prediction error filter. The detection rates for 105 and 108 of MIT/BIH data base were 99.3% and 97.4% respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.166-174
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. The important problem in recording ECG signal is a baseline wandering, which is occurred by rhythm of respiration and muscle contraction attaching to an electrode. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, baseline wander removing method based on morphological filter for efficient QRS detection method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. The signal distortion ratio of the proposed method is compared with other filtering method. Also, R wave detection is evaluated by using MIT-BIH arrhythmia database. Experiment result show that proposed method removes baseline wanders effectively without significant morphological distortion.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.372-377
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• 2014

Being able to monitor the heart will allow the diagnosis of heart diseases for patients during daily activities, and the detection of burden on the heart during strenuous exercise. Furthermore, with the help of U-health technology, immediate medical action can be taken, in the case of abnormal symptoms of the heart in daily life. Therefore, it appears to be necessary to develop the corresponding technology to monitor the condition of the heart daily. In this study, a novel wearable smart system was proposed, to monitor the activity of the heart in daily life, and to further evaluate the rhythm of arrhythmia. The wearable system includes three modified bipolar conductive fiber electrodes in the chest part, which can resolve the reduction problem of the magnitude of the signal, by magnifying the signal and removing the noise, to obtain high affinity and validity for medical-type usage (<0.903%). The biological signal acquisition and data lines, and the signal processing engine and communication consist of a conductive ink, and the pic18 and ANT protocol nRF24AP2, respectively. The proposed algorithm was able to detect a strong ECG, signal and r-point passing over the noise. The confidence intervals were 96 %, which could satisfy the requirement to detect arrhythmia under the unconstrained conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.76-84
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• 2022

Arrhythmia is a condition in which the heart beats abnormally or irregularly, early detection is very important because it can cause dangerous situations such as fainting or sudden cardiac death. However, performance degradation occurs due to personalized differences in ECG signals. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-LSTM. For this purpose, the R wave is detected from noise removed signal and a single bit segment was extracted. It consisted of eight convolutional layers to extract the features of the arrhythmia in detail, used them as the input of the LSTM. The weights were learned through deep learning and the model was evaluated by the verification data. The performance was compared in terms of the accuracy, precision, recall, F1 score through MIT-BIH arrhythmia database. The achieved scores indicate 92.3%, 90.98%, 92.20%, 90.72% in terms of the accuracy, precision, recall, F1 score, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4443-4449
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• 2011

ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.134-137
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• 2001

A variable bandwidth filter (VBF) is proposed for cleaning signals with known instantaneous bandwidth (IB). Also, the behaviors and the characteristics of the VBF are examined in time-frequency domain (TFD). The proposed VBF clips a noisy signal along the boundaries of IB and rejects noise in the outside of the IB of the signal in TFD. It is possible to construct four kinds of VBF that are low-pass VBF, high-pass VBF, band-pass VBF and band-stop VBF.

• Progress in Superconductivity
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• v.11 no.2
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• pp.147-151
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• 2010

We have developed a high-$T_c$ SQUID magnetocardiogram (MCG) system for small laboratory animals. White noise of the measurement system was about 30 fT/$Hz^{1/2}$ when measured in a magnetically shielded room. We optimized the measurement position to obtain clear MCG wave from rat's small heart by using grid measurements. With the optimization, the MCG signal was successfully detected with the peak amplitude of about 30 pT. We could observe well defined P-, QRS-, and T-waves from the rat MCG. The results suggest that the developed system has a strong potential to monitor the progress of the heart disease model by using a laboratory rat.