Ju-Won Lee

Design of filter to reject motion artifact of pulse oximetry

Abstract The performance of the pulse oximeter, an instrument which non-invasively measures oxygen saturation in blood, is influenced by a patients respiration or movement. Generally, the frequency band of the motion artifact signal generated from the patients movement is overlapped with that of the patients pulse wave which is measured by the received light sensor. Accordingly, it is difficult to filter the motion artifact of the pulse wave by using a filter which has a cut-off frequency that is fixed. In this study, the motion artifact is removed using the filter bank and the matched filter. Compared with traditional adaptive filter methods, the ratio variation is 50% lower than that of the moving average filter, allowing more stable measurement of oxygen saturation despite the patients movement.

Design of ICA to Extract Respiration Signal From PPG Signal

Respiration signal of the vital signs is an important parameter in clinical parts. To extract the respiration signal from PPG signal for mobile healthcare system is difficult because the bands of the motion artifacts and respiration in the frequency domain are overlapped. This study to improve this problem suggested a respiration extraction method using the independent component analysis and evaluated its performances. In results of evaluation, the ICA method showed better performance than LPF suggested recently.

Design of Filter to Reject Motion Artifacts of PPG Signal by Using Two Photosensors

The photoplethysmography (PPG) signal measured from a mobile healthcare device contains various motion artifacts occurring from a patient’s movements. Recently, to reject the motion artifacts, the method of using an acceleration sensor was suggested, but such sensors are very expensive. Therefore, this study deals with a novel sensor device to replace the acceleration sensor, and evaluated the performance of the proposed sensor experimentally. In the results of the experiments, it is shown that the proposed sensor device can reconstruct the PPG signal despite the occurrence of motion artifacts, and also that the variation rate in heart rate analysis was 1.22%. According to the experimental results, the proposed method can be applied to design a low-cost device.

Design of Kalman Filter to Estimate Heart Rate Variability from PPG Signal for Mobile Healthcare

In the mobile healthcare system, a very important vital sign in analyzing the status of user health is the HRV (heart rate variability). The used signals for measuring the HRV are electrocardiograph and PPG (photoplethysmograph). In extracting the HRV from the PPG signal, an important issue is that extract the exactly HRV from PPG signal distorted from the users movements. This study suggested a design method of the Kalman filter to solve the problem, and evaluated the performances of a proposed method by PPG signals containing motion artifacts. In the results of experiments that compared with a variable step size adaptive filter proposed in recently, the proposed method showed better performance than an adaptive filter.

Heart Rate Estimation Based on PPG signal and Histogram Filter for Mobile Healthcare

The heart rate is the most important vital sign in diagnosing heart status. The simple method to measure the heart rate in the mobile healthcare device is using the PPG signal. In developing the mobile healthcare device using the PPG signal, the most important issue is the inaccuracy of the measured heart rate because the PPG signal is distorted from the users motions. To improve the problem, this study proposed the new method that is to estimate the heart rate without an additional sensor in real life. The proposed method in this study is using the histogram filter. In order to evaluate the performance of the proposed method, the study compares its results with the moving average method in motion environment. According to the experimental results, the performance of the proposed method was more than 40% better than the performances of the MAF.

A study on lung nodule detection using neural networks

In this study, the authors developed a method for disease detection using an artificial neural network and digital image processing of a chest radiograph. In a conventional physical examination radiologists check the chest image projected on a viewing box by a magnifying glass and determine what the disease is. The detection of disease on X-ray fluoroscopy images is tedious and time-consuming for humans. This lowers the efficiency for chest diagnosis as many mistakes by the radiologist are caused because of the need to detect micropathology from a film of small size. So, the authors propose a method to quickly find out what the object on a chest radiograph is. This method comprises the functions of image sampling, median filter, neural network image equalizer and neural network pattern recognition. The authors confirm that this method has improved the problems of conventional methods.

A design of adaptive neural filter banks with filter neuron

In this study, we propose that the new filter bank that is an adaptive filter bank using neural networks in time domain. Also, we propose a filter neuron as band pass filter (BPF) with hamming window, the structure and algorithm for filter banks. The performance of neural filter banks is shown from two examples. It shows its characteristics such as the simple structure and a higher speed processing compared to traditional methods (filter banks in frequency domain, etc.). In many applications, the proposed method will provide a higher performance to feature detection of signals in time domain.