scholarly journals An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6144
Author(s):  
Tae Wuk Bae ◽  
Sang Hag Lee ◽  
Kee Koo Kwon
Keyword(s):  
Detection Method ◽  
Median Filter ◽  
Sampling Rate ◽  
Sliding Window ◽  
Sensor Technology ◽  
Ecg Signals ◽  
Measuring Devices ◽  
Adaptive Median Filter ◽  
Filter Size ◽  
Point Detection

With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique.

scholarly journals Research on Yarn Diameter and Unevenness Based on an Adaptive Median Filter Denoising Algorithm

2020 ◽  
Vol 28 (1(139)) ◽  
pp. 36-41
Author(s):  
Xiao Wang ◽  
Ru-Meng Hou ◽  
Xiao-Yan Gao ◽  
Bin-Jie Xin
Keyword(s):  
Detection Method ◽  
Median Filter ◽  
Morphological Processing ◽  
Median Filtering ◽  
Yarn Diameter ◽  
Testing Data ◽  
Adaptive Median Filter ◽  
Image Separation ◽  
Image Acquisition System

In this paper an adaptive median filtering denoising algorithm is proposed to measure yarn diameter and its unevenness. Images of nine different yarn samples were captured using one set of a self-developed yarn image acquisition system. Image separation of the background and yarn sections was conducted using a combination of adaptive median filtering, adaptive threshold segmentation and morphological processing. The noise-free yarn image was used for diameter detection of the subsequent yarn image and the discrimination of the yarn unevenness. Experimental results show that the testing data of yarn unevenness detection based on the adaptive median filter denoising algorithm is very consistent with the data using the traditional method. It is proved that the yarn detection method proposed, based on an adaptive median filter denoising algorithm, is feasible. It can be used to calculate yarn diameter accurately and measure yarn unevenness efficiently, so as to determine the quality of yarn appearance objectively.

scholarly journals Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

2019 ◽  
Vol 9 (19) ◽  
pp. 4128
Author(s):  
Tae Wuk Bae ◽  
Kee Koo Kwon
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Detection Method ◽  
Sliding Window ◽  
Ecg Signal ◽  
Qrs Detection ◽  
Real Time Processing ◽  
Ecg Signals ◽  
Ecg Signal Processing ◽  
Processing Techniques

Recently, with the active development of wearable electrocardiogram (ECG) devices such as smart-bands or portable ECG devices, efficient ECG signal processing technology that can be applied in real-time has been actively studied. However, a wearable ECG device is exposed to various noise situations, thereby reducing the reliability of the detected R point or QRS interval. In addition, as early warning techniques in healthcare systems have been studied, real-time ECG signal processing techniques have become very important in wearable ECG devices. In this paper, we propose an efficient real-time R and QRS detection method using two kinds of first-order derivative filters and a max filter to analyze ECG signals measured from wearable ECG devices in real-time. The proposed method detects the R point and QRS interval in units of a sliding window for real-time processing and combines the detected R points in each sliding window. Also, the reliability of the detected R points and RR intervals is examined through noise region analysis using the histogram characteristic of a sample point. The performance of the proposed method was verified by the MIT-BIH database (DB), CYBHi DB and real ECG data measured from the developed wearable ECG patch. The proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB. In addition, the proposed method enables accurate R point detection and heart rate variability (HRV) analysis even with noisy ECG signals.

Design of Swimming Pool Temperature Dot Matrix Screen Display

2014 ◽  
Vol 556-562 ◽  
pp. 2208-2211
Author(s):  
Xue Feng Yang
Keyword(s):  
Water Temperature ◽  
Real Time ◽  
Room Temperature ◽  
Detection Method ◽  
Swimming Pool ◽  
Real Time Monitoring ◽  
Real Time Processing ◽  
The Core ◽  
Swimming Pool Water ◽  
Point Detection

According to the need of the real-time monitoring and displaying of the environment in many areas,to put forward a method of temperature monitoring and displaying, using STC11F32XE microcontroller as the core controller, DS18B20 as temperature acquisition chip, 32X64LED dot matrix screen as a display screen,using the mothod of multi point detection method,real-time monitoring of swimming pool water temperature and room temperature, real-time displaying of Multipoint collecting information, Real time processing the detected temperature, the page display to multipoint temperature display through the wireless remote control module,the system will alarm When the water temperature is too high or too low, to remind managers of real-time processing.To design a clear temperature display for the swimming pool,real time monitoring and controlling is very convenient,after the experimental verification, the system reaches the anticipative goal,the system is an ideal and effective.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

2013 ◽  
Vol 373-375 ◽  
pp. 1155-1158
Author(s):  
Kang Yan ◽  
Zhong Yuan Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Back Propagation Neural Network ◽  
Threshold Method ◽  
Safe Operation ◽  
Composite Insulator ◽  
Adaptive Median Filter

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

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