• Journal of Biomedical Engineering Research
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• v.12 no.2
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• pp.131-138
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• 1991

This paper describes an algorithm of pattern analysis of ECG signals by significant points extraction method. The significant points can be extracted by modified zerocrossing method, which method determines the real significant point among the significant point candidates by zerocrossing method and slope rate of left side and right side. This modified zerocrossing method improves the accuracy of detection of real slgnficant polnt Position. This Paper also describes the pattern matching algorithm by a hierarchical AND/OR graph of ECG signals. The decomposition of ECG signals by a hierarchical AND/ OR graph can make the pattern matching process easy and fast, Furthermore the pattern matching to the significant points reduces the processing time of ECG analysis.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.455-464
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• 1994

The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan's method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMG signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.305-316
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• 1994

All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1076-1094
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• 2022

Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

• Journal of Information Technology Applications and Management
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• v.12 no.4
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• pp.25-31
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• 2005

We know that the Hurst Exponent (HE) is a real number in [0, 1] which denotes randomness of time series. in this research, we suggest non-linear analysis of human biological signals through HE. The feasibility of human biological signals using inductive incitement provides Some diagnosis for active treatment. In our experiment, we measured the heartbeat through the MCG, 29 healthy and 34 abnormal subjects ostensibly. The raw data of acupuncture incitement are supported by opinions of gross examination and pathological diagnosis. The mean values of HE are 0.345, 0.755 and 0.805 for the periods of before, during and after acupuncture treatment, respectively in case of abnormal subjects. On the other hand, the mean values, 0.808, 0.797 and 0.785 are for normal cases, correspondingly. From this data, we show that HE is very significant in abnormal controls according to an acupuncture incitement, and the incitement effect is evidently extracted in abnormal subjects. But, in normal subjects, the incitement effect is meaningless.

• Current Optics and Photonics
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• v.7 no.1
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• pp.65-72
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• 2023

The motion of organelles inside a cell is an important intrinsic indicator for assessing cell physiology and tissue viability. Dynamic contrast full-field optical coherence tomography (D-FFOCT) is a promising imaging technology that can visualize intracellular movements using the variance of temporal interference signals caused by biological motions. However, double-path interferometry in D-FFOCT can be highly vulnerable to surrounding noise, which may cause turbulence in the interference signals, contaminating the sample dynamics. Therefore, we propose a method for stabilized D-FFOCT imaging in noisy environments by using common-path interferometry in D-FFOCT. A comparative study shows that D-FFOCT with the proposed method achieves stable dynamic contrast imaging of a scattering phantom in motion that is over tenfold more noise-insensitive compared to the conventional one, and thus this imaging capability can provide cleaner motion contrast images. With the proposed approach, the intracellular dynamics of biological samples are imaged and monitored.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.455-458
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• 1997

We are exposed to the various external stimuli input from the environment, which cause emotional changes based on the characteristics of the stimuli. Unfortunately, there are no quantitative results on relationship between human sensibility and the characteristics of physiological signals. The objective of this study was to quantify EEG signals evoked by visual stimulation based on the assumption that the analysis of the variability on the characteristics of the EEG waveform may provide the significant information regarding changes in psychological states of the subject. Seven university students were participated in this study. The experiment was devised with eleven experimental conditions, which are control and ten different types of visual stimulation based on IAPS (International Affective Picture Systems). Wavelet transformation was employed to analyze the EEG signals. Most positive and negative emotional response were compared in pairs. The results showed that the reconstructed signals at the decomposition level revealed the different energy value on the EEG signals. Also, general patterns of EEG signals in rest state compare with positive and negative stimulus were found. This study could be extended to establish an algorithm which distinguishes psychophysiological states of the subjects exposed to the visual stimulation.

• Journal of Biomedical Engineering Research
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• v.3 no.2
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• pp.105-112
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• 1982

Using electronic cochlear implant system, we studied in cats the difference in the response of the brain stem evoked response (BER) during the stimulation with the acoustic signals and the electric signals. These brain stem electric responses were analyzed using the integral pulse frequency modulation method of the auditory nervous system. Animal experimental results and the analysis show that the carrier wave hasimprored the frequency specificity. of the electronic auditory prosthesis.

• Journal of Biomedical Engineering Research
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• v.9 no.2
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• pp.149-152
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• 1988

From the analysis of proton NMR signals of human urine it is found that the signals corresponding to a phenolic compound of tyrosine are more frequently observed in cancer urine than in non-cancer urine. An effective reagent is obtained to detect the substance excreted in the urine and to find out a close connection with the result of the NMR analysis. An attempt is made to determine the reagent sensitivity and specificity for cancer diagnosis. The results of the attempt are respectively above 75% for both on an average.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.343-350
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• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.