The combination of Kaiser window and moving average for the low-pass filtering of the remote ECG signals

Bai, Ying-Wen; Chu, Wen-Yang; Chen, Chien‐Yu; Lee, Yi-Ting; Tsai, Yi -Ching; Tsai, Cheng‐Hung

doi:10.1109/cbms.2004.1311727

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Removing noises is particularly important in applications that require using biosignals and must be performed during the preprocessing step. Various digital filtering methods are used, such as a band-pass filter [25], median filter [26], and moving average filter [27], which are useful and easy tools for removing noises recorded in ECGs including interference of power cables or electrodes. However, removing noises by applying only digital filters to original signals cannot effectively remove outlier signals of which the morphological nature of signals has been damaged by motion artifacts, as shown in Figure 2.…”

Section: A Outlier Signal Removal Based On the Rr Interval Of Ecgsmentioning

confidence: 99%

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Song,

Kim,

Kim

et al. 2024

IEEE Access

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In the information age, people are increasingly being exposed to stress as societies are experiencing sudden changes fueled by advancements in cutting-edge scientific technology and the Information Technology (IT) industry. Consequently, research has been actively conducted on stress classification to mitigate psychological and physical diseases caused by constantly feeling stressed. Specifically, the number of studies examining electrocardiograms (ECGs), which record biosignals that provide insight into the response level of the body's autonomic nervous system, has increased. However, previous studies on stress classification based on ECG used only one-dimensional feature data, thus entailing difficulties in analyzing the data more closely and comprehensively owing to bias toward a specific aspect. Therefore, to overcome the limitations of conventional stress classification based on ECGs, this paper developed a stress classification method based on multi-dimensional feature fusion of LSTM and Xception using ECGs from which outliers have been removed. Experimental results showed that applying multidimensional feature fusion of the weighted average method using ECG data with outlier signals removed resulted in a stress classification of 99.51%, a 1.25% improvement from previous studies which used only one-dimensional feature data of ECGs, thus highlighting the excellent performance of the proposed stress classification method using ECG based on multi-dimensional feature fusion of LSTM and Xception.

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Section: A Outlier Signal Removal Based On the Rr Interval Of Ecgsmentioning

confidence: 99%

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Song,

Kim,

Kim

et al. 2024

IEEE Access

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“…Furthermore, in 2016, Jinkwon Kim et al conducted a Simple and Robust Realtime QRS Detection Algorithm Based on Spatiotemporal Characteristic of the QRS Complex [10]. The aim of this research was to develop an intuitive real-time QRS detection based on the physiological characteristics of the electrocardiogram waveform [11]. The proposed algorithm has the function of finding the QRS complex based on the required criteria of the amplitude and duration of the QRS complex [12].…”

Section: Introductionmentioning

confidence: 99%

QRS Detection On Heart Rate Variability Readings using Two Moving Average Methods

Rizhky

Wisana

Das³

2023

Indones.J.electronic.electromed.med.inf

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Heart Rate Variability or heart rate in humans is used to unify the heart rate in humans, the function of the heart rate monitor is used to unite the human heart rate. The purpose of making this tool is to read the human heart rate using the Two Moving Average method or moving average which makes it easy to find the R peak to peak signal, making it easier to read. This is achieved by discovering and studying each window size change specified by, so that it can be seen the change in every two moving averages for each window size value. This study uses the Arduino Nano system for data processing and uses Delphi to display the processed data. In this study examined signaling and heart rate monitoring for 5 minutes. In this study it can be said that, the best window size with the best signal results for measuring heart rate is the window size 15 . this method is a method with a good accuracy rate of 98%. And also this method can be displayed directly by displaying the RR Interval and HRV value for 10 minutes with results close to 0. This method is recommended to detect high enough P and T signal.

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Modification of the Adaptive Moving Average Filter for the Signal Parameters Measurement

Podobuev,

Kacharsky,

Nurtdinova

et al. 2024

2024 International Conference on Electrical Engineering and Photonics (EExPolytech)

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The combination of Kaiser window and moving average for the low-pass filtering of the remote ECG signals

Cited by 5 publications

References 6 publications

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

QRS Detection On Heart Rate Variability Readings using Two Moving Average Methods

Modification of the Adaptive Moving Average Filter for the Signal Parameters Measurement

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