scholarly journals Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qiang Yin ◽  
Dai Shen ◽  
Qian Ding
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Feature Extraction ◽  
Data Processing ◽  
Time Window ◽  
Window Size ◽  
Objective Evaluation ◽  
Noxious Stimulation ◽  
Feature Extraction Method ◽  
Window Approach

In recent decades, little progress of objective evaluation of pain and noxious stimulation has been achieved under anesthesia. Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. In the process of data processing, the empirical mode decomposition is used to decompose and recombine heart rate variability signals, and the sliding time window approach is used to extract the signal features of noxious stimulation, respectively. The influence of window size on feature extraction is studied by changing the window size. By comparing the results, the feature extraction in the process of data processing is valuable, and the selection of window size has a significant impact. With the increase of selected window sizes, we can get better detection results. But for the best choice of window size, to ensure the accuracy of the results and to make it easy to use, then, we need to get just a suitable window size.

A Study on Heart Load Affected by Industrial Noise

2011 ◽  
Vol 179-180 ◽  
pp. 549-553
Author(s):  
Qiu Yun Mo ◽  
Feng Gao
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Data Processing ◽  
Human Heart ◽  
Processing System ◽  
Data Processing System ◽  
Assessment Index ◽  
Main Frequency ◽  
Industrial Noise ◽  
Related Data

This paper demonstrates a method and its implementation on workers’ heart load affected by industry noise, and, to develop a related data processing system. Heart rate variability (HRV) is used as an assessment index. The affected regulation of human’s heart is obtained by large number of noise experiments. This method emphasizes the human heart load among the all factors of labor safeties, the noise influence discipline can be debated scientifically from both engineering physiology and psychology. The results show when to control the industrial noise it is much important to control the ingredient of noise main frequency not the audio-level.

Effects of Kampo medicine on pupillary dynamics and heart rate variability: Establishment of an objective evaluation method

2007 ◽  
Vol 137 (1-2) ◽  
pp. 104-105
Author(s):  
Akino Wakasugi ◽  
Hiroshi Odaguchi ◽  
Hisakazu Shoda ◽  
Hidenori Ito ◽  
Toshihiko Hanawa
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Evaluation Method ◽  
Objective Evaluation ◽  
Kampo Medicine

scholarly journals Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7122
Author(s):  
Alessio Rossi ◽  
Dino Pedreschi ◽  
David A. Clifton ◽  
Davide Morelli
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Missing Values ◽  
Mean Squared Error ◽  
Time Window ◽  
Small Error ◽  
Motion Artifacts ◽  
Window Length ◽  
Short Time Window ◽  
Short Time

Application of ultra–short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people’s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.

Neural network based feature extraction scheme for heart rate variability

1995 ◽  
Author(s):  
Ben Raymond ◽  
Doraisamy Nandagopal ◽  
Jagan Mazumdar ◽  
D. Taverner
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Feature Extraction ◽  
Extraction Scheme
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