Hee Don Seo

A New QRS Detection Method Using Wavelets and Artificial Neural Networks

We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.

EPILEPTIC SPIKE DETECTION USING CONTINUOUS WAVELET TRANSFORMS AND ARTIFICIAL NEURAL NETWORKS

We propose a new method for detection and classification of noisy recorded epileptic transients in Electroencephalograms (EEG) using the continuous wavelet transform (CWT) and artificial neural networks (ANN). The proposed method consists of a segmentation, feature extraction and classification stage. For the feature extraction stage, we use best basis mother wavelet functions and wavelet thresholding technique. For the classification stage, multilayer perceptron neural networks were implemented according to standard backpropagation learning formulations. We demonstrate the efficiency of our feature extraction method on data to improve the ANN detection performance. As a result, we achieved the accuracy in detection and classification of seizure EEG signals with 94.69%, which is relatively good comparing with the available algorithms at present time.

The Effect of Acupuncture at PC-6 on the Electroencephalogram and Electrocardiogram

The present study aims to examine the effect of acupuncture stimulation of an acupuncture point (PC-6) and nonacupuncture point on electroencephalograms (EEGs) and electrocardiograms (ECGs). We used EEG in 10 healthy subjects to investigate cortical activation during stimulation of acupuncture points (neiguan: PC-6) and nonacupuncture points. Our most interesting finding was the marked differences of amplitude of EEG power between acupuncture points and nonacupuncture points stimulation. Wavelet transform was used as the EEG signal processing method, because it has advantages in a time domain and frequency domain characteristics analysis. EEGs were collected from 16 channels, and the alpha-wave (8-13 Hz), beta-wave (13-30 Hz), theta-wave (4-8 Hz) and delta-wave (0.5-4 Hz) were used as standards for frequency bands. According to the experiment results, EEG signals increased considerably after acupuncture stimulation; in each frequency band, the average amplitude was higher after acupuncture stimulation; ECG heart rates were faster by at least 10% after acupuncture stimulation. Consequently, it will be possible to verify the function of acupuncture stimulation on neiguan (acupuncture points; PC-6) more effectively.

Study of biosignal response during acupuncture points stimulations

The present study examined the effect of electro-acupuncture stimulation of PC5 and PC6, acupuncture points located on the pericardium meridian, on electroencephalogram (EEG) changes, bioimpedance value, and electrocardiogram (ECG) heart rate. The EEGs of the frontal, central, temporal, parietal, and occipital lobes were measured. Generally, the EEGs increased at large amplitude in the all lobe during electro-acupuncture (EA) stimulation of PC5 and PC6. According to the present experiments, after the EA stimulation of PC5, EEG was verified to change at an extremely large amplitude in the temporal lobe. A comparison of biological active points (BAPs) and non-BAPs in bioimpedance measurement experiments revealed that reactance resistance had low-resistance characteristics and characteristics frequency had frequency characteristics. The ECG heart rate decreased at PC6 and increased at PC5. PC6 therefore may have a certain effect on sedation of the human nervous system. We applied CWT to detect short-time event and the result image representation of the signal has showed us that one can easily find the discontinuity at the time scale representation. The results of the present study verified that the EA stimulation of BAPs (PC5, PC6) affects EEG, human bioimpedance, and ECG heart rate, and such stimulation may be capable of affecting the nervous system.

A wavelet based method for detecting and localizing epileptic neural spikes in EEG

The recording of seizures is of primary interest in the evaluation of epileptic transients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behaviour usually lasts from seconds to minutes. Since seizures in general occur infrequently and unpredictably, an automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper proposes a new method for the detection of epileptic transients in EEG by using continuous wavelet transform (CWT) with suitable mother wavelet functions and thresholding method. We demonstrate the efficiency of our method on data to identify and clearly locate in time the seizure activities. The method is superior both in separation from noise and in identifying superimposed epileptic action potentials based on in sets of combined scales. We prove that this method is fast and simple which also reduces real time computations.

Improvement of thermal response in temperature controlled precise three-axis accelerometer with stabilized characteristics over a wide temperature range

Improvement of the thermal response time of a temperature controlled three-axis accelerometer for high temperature environments with integrated microheaters and temperature sensors is presented. A detailed analysis of thermal response is carried out, and variation of thermal response with supply power is investigated using a simplified finite element method (FEM) model based on thermal response analysis. The thermal response analysis of the devices is investigated with the FEM program, ANSYS and infrared thermal measurement systems. The availability to application fields from the viewpoint of a short thermal response time is discussed. The time for a three-axis high temperature accelerometer to reach 300/spl deg/C, with integrated micro-heaters and temperature sensors to reduce thermal drift characteristics, was analyzed as the thermal response time of the device. The simulated thermal response time (time until SOI piezoresistors actually become 300/spl deg/C) of the three-axis high temperature accelerometer (using ANSYS) is about 600 ms, and the result measured by infrared temperature measurement systems is about 640 ms. Thermal measurement experimental results agreed well with these theoretical results. If electric power of about 260 mW is supplied to the integrated micro-heaters at around room temperature, the three-axis accelerometer reaches 300/spl deg/C within 90 ms.

Near-infrared spectroscopy in the analysis of functional brain activity during cognitive tasks

This paper presents our empirical results by using 32 channel NIRS system to examine the changes in oxygenation in regions of prefrontal cortex. Four healthy subjects participated in the experiment by performing various cognitive tasks such as mathematical, imaginary and computer game. The concentration levels in oxyHb and deoxyHb varied depending on subjects mental workload as a result we could locate strong brain activation regions. We analyzed hemoglobin concentrations in wavelet domain to detect and extract the true brain stimulus from noisy NIRS measurements.

Thermal Characteristics in Motion Sensor for High Temperature environments

In this paper, improvement of thermal response time of a temperature controlled motion sensor for high temperature environments with integrated micro-heaters and temperature sensors is presented. More detailed analysis of thermal response is carried out, and variation of thermal response with supply power energy is investigated using simplified finite element method (FEM) model based on thermal response analysis. Thermal response analysis of the devices is investigated with FEM program, ANSYS and infrared thermal measurement systems. And availability to application fields from a viewpoint about short thermal response time is discussed. In this paper, the time of motion sensor for high temperatures becoming 300degC by integrated micro-heaters and temperature sensors to reduce thermal drift characteristics was analyzed as a thermal response time of this device. The simulated thermal response time (time until SOI piezoresistors actually becomes 300degC) of motion sensor for high temperatures with ANSYS is about 600 ms, and measured result with infrared temperature measurement systems is about 640 ms. Experimental results using infrared thermal measurement systems agreed well with these theoretical results. As the results, if the electric power of about 260 mW is supplied to the integrated micro-heaters being around room temperature, the motion sensor reached at 300degC within 90 ms.