An effective method to identify various factors for denoising wrist pulse signal using wavelet denoising algorithm

Garg, Nidhi; Ryait, Hardeep Singh; Kumar, Amod; Bisht, Amandeep

doi:10.3233/bme-171712

Cited by 7 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Denoising processing In this work, Low Pass filter and wavelet packet methods are used to denoise ECG and EOG signals. The wavelet packet method is used to remove denoise (Garg et al 2018, Kumar et al 2020, Ji et al 2021. Use wavelet with db3 basic function to perform 3-level wavelet decomposition on the signal, and apply 'sqtwolog' threshold selection rule to the wavelet coefficients to denoise the ECG signal.…”

Section: Filter Modulementioning

confidence: 99%

Sleep staging classification based on a new parallel fusion method of multiple sources signals

Hei¹,

Yuan²,

Fan³

et al. 2022

Physiol. Meas.

View full text Add to dashboard Cite

Approach: First, the heart rate variability (HRV) is extracted from EOG with the Weight Calculation Algorithm (WCA) and an “HYF” RR interval detection algorithm. Second, three feature sets were extracted from HRV segments and EOG segments: time-domain features, frequency domain features and nonlinear-domain features. The frequency domain features and nonlinear-domain features were extracted by using Discrete Wavelet Transform (DWT), Autoregressive (AR), and Power Spectral entropy (PSE), and Refined Composite Multiscale Dispersion Entropy (RCMDE). Third, a new “Parallel Fusion Method” (PFM) for sleep stage classification is proposed. Three kinds of feature sets from EOG and HRV segments are fused by using PFM. Fourth, Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) classification models is employed for sleep staging. Main results: Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the new sleep staging approach. The performance of the proposed method is testedby evaluating the average accuracy, Kappa coefficient. The average accuracy of sleep classification results by using XGBoost classification model with PFM is 82.7% and the kappa coefficient is 0.711, also by using SVM classification model with the PFM is 83.7%, and the kappa coefficient is 0.724. Experimental results show that the performance of the proposed method is competitive with the most current methods and results, and the recognition rate of S1 stage is significantly improved. Significance： As a consequence, it would enable one to improve the quality of automatic sleep staging models when the EOG and HRV signals are fused, which can be beneficial for monitor sleep quality and keep abreast of health conditions. Besides, our study provides good research ideas and methods for scholars, doctors and individuals.

show abstract

Section: Filter Modulementioning

confidence: 99%

Sleep staging classification based on a new parallel fusion method of multiple sources signals

Hei¹,

Yuan²,

Fan³

et al. 2022

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…These artifacts should be removed to get accurate physiological signal analysis. Abating these degradations requires methods ,for instance, sliding window [38],wavelet denoising [12,18,24,39,40], cascade filter [21], curve fitting [21], discrete Mayer model [36],Finite Impulse Response(FIR) filter. Even filters can be taken into service like notch filter [41], Butterworth [22], Chebyshev, Bandpass [15,19,31].The sampling rate is even maintained much larger so that information will not be lost.…”

Section: B Data Acquisitionmentioning

confidence: 99%

An Emotion Recognition Based on Physiological Signals

2019

IJITEE

View full text Add to dashboard Cite

Emotion recognition is alluring considerable interest among the researchers. Emotions are discovered by facial, speech, gesture, posture and physiological signals. Physiological signals are a plausible mechanism to recognize emotion using human-computer interaction. The objective of this paper is to put forth the recognition of emotions using physiological signals. Various emotion elicitation protocols, feature extraction techniques, classification methods that aim at recognizing emotions from physiological signals are discussed here. Wrist Pulse Signal is also discussed to fill the lacunae of the other physiological signal for emotion detection. Working on basic as well as non-basic human emotion and human-computer interface will make the system robust.

show abstract

Muscle Artifact Detection in EEG Signal Using DTW Based Thresholding

Bisht

Singh

2021

Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences

View full text Add to dashboard Cite

An effective method to identify various factors for denoising wrist pulse signal using wavelet denoising algorithm

Abstract: The empirical results proves that the methodology discussed here could be effective in denoising WPS of any morphological pattern.

Cited by 7 publications

References 29 publications

Sleep staging classification based on a new parallel fusion method of multiple sources signals

Sleep staging classification based on a new parallel fusion method of multiple sources signals

An Emotion Recognition Based on Physiological Signals

Muscle Artifact Detection in EEG Signal Using DTW Based Thresholding

Contact Info

Product

Resources

About