• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1431-1437
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• 2016

In mobile healthcare device, when to measure the heart rate by using the PPG signal, its performance is reduced according to the motion artifacts that is the movement of user. This is because the frequency range of motion (0.01-10 Hz) and that of PPG signals overlap. Also, the motion artifacts cannot be rectified by general filters. To solve the problem, this paper proposes a method using filter banks and independent component analysis (ICA). To evaluate the performance of the proposed method, we were artificially applied various movements and compared heart rate errors of the moving average filter and ICA. In the experimental results, heart rate error of the proposed method showed very low than moving average filter and ICA. In this way, it is possible to measure stable heart rate if the proposed method is applied to the healthcare terminal design.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.485-487
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• 2003

Independent Component Analysis (ICA) is used to generate ICA filter for computing feature vector for image window. Filters that have high discrimination power are selected to classify image from these ICA filters. Proposed classification algorithm is based on probability distribution of feature vector.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.305-309
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• 2000

In this paper, we presents a new methodology for face recognition after analysing conventional ICA(Independent Component Analysis) based approach. In the literature we found that ICA based methods have followed the same procedure without any exception, first PCA(Principal Component Analysis) has been used for feature extraction, next ICA learning method has been applied for feature enhancement in the reduced dimension. However, it is contradiction that features are extracted using higher order moments depend on variance, the second order statistics. It is not considered that a necessary component can be located in the discarded feature space. In the new methodology, features are extracted using the magnitude of kurtosis(4-th order central moment or cumulant). This corresponds to the PCA based feature extraction using eigenvalue(2nd order central moment or variance). The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. ICA methodology is analysed using SVD(Singular Value Decomposition). PCA does whitening and noise reduction. ICA performs the feature extraction. Simulation results show the effectiveness of the methodology compared to the conventional ICA approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.687-692
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• 2006

In a conventional ICA(Independent Component Analysis) based face recognition method, PCA(Principal Component Analysis) first is used for feature extraction, ICA learning method then is applied for feature enhancement in the reduced dimension. It is not considered that a necessary component can be located in the discarded feature space. In the new ICA(NICA), learning extracts features using the magnitude of kurtosis (4-th order central moment or cumulant). But, the pure ICA method can not discard noise effectively. The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. Namely, PCA does whitening and noise filtering. ICA performs feature extraction. Experiment results show the effectiveness of the new ICA method compared to the conventional ICA approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.515-520
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• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1674-1677
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• 2002

In this paper, the new method to extract the features of iris signals is proposed; Multiresolution ICA (M-ICA) provides good properties to represent signals with time-frequency. The conventional methods were to use the technique of filter bank analysis, while ICA is unsupervised learning algorithm using high-order statistics. M-ICA could make use of strengths of learn- ing method and multiresolution. Also, we performed comparative studies of different feature extraction techniques applied to personal identification using iris pat- tern. To measure goodness of methods, we use Fisher’s discriminant ratio to quantify the class-separability of features generated by various techniques.

• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.71-78
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• 2016

PPG signal measured by pulse oximeter can measure pulse and the oxygen saturation of arterial blood. But the PPG signal is distorted by finger movement or other movement in the body. To detect pulse from the PPG signal with motion artifact, we use band pass filter(BPF), Independent component analysis(ICA) and nonlinear autocorrelation(NAC). BPF is used to remove DC component and high frequency noise in the PPG signal with motion artifacts. ICA is used to separate pulse signal and motion artifact. However, pulse signal separated by ICA have no choice but to accompany signal distortion because pulse signal and motion artifact are not completely independent. So, we use nonlinear autocorrelation to emphasize the pure pulse signal from the distorted signal.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.10-17
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• 2007

The echo signal on ultrasonic transducer is a mixed signal from tissues, blood vessel walls, blood cells and noise. In this mixed-signal, the signal reflected from tissues and blood vessel walls is called clutter. It is necessary to extract pure blood signal from this mixed-signal, when measuring blood flow velocity with medical ultrasonic system The quality of measured blood flow velocity is highly dependent on sufficient attenuation of the clutter signals. In this paper, we suggest a clutter rejection method using ICA For simulation, the echo signals are generated by Field n ultrasonic simulation program In this echo signals, independent signals are separated by using ICA Then the blood signal is obtained from the separated signals. Blood flow velocity is measured by 2D autocorrelation method. We compare ICA clutter rejection method with PCA-based eigen filter method using both measured blood flow velocity profiles by 2D autocorrelation. In simulation results, ICA clutter rejection method can be better applied measuring blood flow velocity in noisy echo signals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.691-697
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• 2013

Recently, u-healthcare device is developed and commercialized for healthcare management and emergency medical. The kinds of the measurable biomedical signals on the device are electrocardiogram, skin temperature, pulse oxygen, heart rate, respiration, etc. Specially, the photoplethysmograph(PPG) signal of these signals is the important signal in measuring oxygen, heart rate and peripheral vascular compliance. The accuracy of PPG signal reduce from influence of the motion artifacts that generated from the movements of user or patient. Therefore, this study suggests a new method to remove the motion artifact that is using optical power modulation and ICA(Independent Component Analysis). For analyzing the proposed method, we used variety of noises made by artificially. In the results of experiments, the proposed method showed good performances than an adaptive filter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.122-129
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• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.