Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Cai, Ken; Yue, Hongwei; Li, Bohan; Chen, Weitong; Huang, Wenhua

doi:10.1109/access.2020.2979991

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…Fast independent component analysis is a type of ICA algorithm responsible for separating the unknown mixed signals to obtain useful independent signals using the source signal's independent and non-Gaussian nature [29]. An algorithm of FastICA works faster and is iteratively used at constant points with a simple structure and fast convergence [30].…”

Section: ) Fast Independent Component Analysismentioning

confidence: 99%

Emotional State Prediction Based on EEG Signals using Ensemble Methods

Alrebdi,

Al-Shargabi

2023

IJACSA

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The emotional state is an essential factor that affects mental health. Electroencephalography (EEG) signal analysis is a promising method for detecting emotional states. Although multiple studies exist on EEG emotional signals classification, they have rarely considered processing time as a metric for classification model evaluation. Instead, they used either model accuracy and/or the number of features for evaluation. Processing time is an important factor to be considered in the context of mental health. Many people commonly use smart devices, such as smartwatches to monitor their emotional state and such devices require a short processing time. This research proposes an EEG-based model that detects emotional signals based on three factors: accuracy, number of features, and processing time. Two feature extraction algorithms were applied to EEG emotional signals: principal components analysis (PCA) and fast independent components analysis (FastICA). In the classification process, ensemble method classifiers were adopted due to their powerful performance. Three ensemble classifiers were used: random forest (RF), extreme gradient boosting (XGBoost), and adaptive boost (AdaBoost). The experimental results showed that the RF and XGBoost achieved the best accuracy, i.e., 95%, for both methods. However, XGBoost outperformed RF in terms of the number of features; it used 33 components extracted by PCA within 14 seconds, while RF used 36 within 4 seconds. AdaBoost was the worst in terms of both accuracy and processing time in the two experiments.

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Section: ) Fast Independent Component Analysismentioning

confidence: 99%

Emotional State Prediction Based on EEG Signals using Ensemble Methods

Alrebdi,

Al-Shargabi

2023

IJACSA

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“…Therefore, GREEN proposed by Verkrysse et al [4] was included, which extracts the BVP signal from the green color channel of the RGB color space. Most ICA based rPPG methods use ICA-Poh (JADE) [2], [28], [37], [38] and FastICA [9], [12], [50], [51]; hence they were included in this analysis.JADE uses kurtosis, whereas FastICA uses a negentropy based optimization function for an unmixing matrix estimation.Two color subspace transformations CHROM [10] and POS [14], were also included in the analysis due to their dependence on optimization procedures like ICA based methods. CHROM is a motion intolerant algorithm, while POS performs better for uneven illumination variations.…”

Section: F Comparative Analysismentioning

confidence: 99%

A Motion and Illumination Resistant Non-Contact Method Using Undercomplete Independent Component Analysis and Levenberg-Marquardt Algorithm

Gupta

Ravelo-García

Morgado‐Dias

2022

IEEE J. Biomed. Health Inform.

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Heart Rate (HR) estimation is of utmost importance due to its applicability in diverse fields. Conventional methods for HR estimation require skin contact and are not suitable in certain scenarios such as sensitive skin or prolonged unobtrusive HR monitoring. Therefore remote photoplethysmography (rPPG) methods have become an active area of research. These methods utilize the facial videos acquired using a camera followed by extracting the Blood Volume Pulse (BVP) signal for heart rate calculation. The existing rPPG methods either utilized a single color channel or weighted color differences, which has certain limitations dealing with motion and illumination artifacts. This study considered BVP extraction as an undercomplete problem and proposed a method resistant to motion and illumination variation artifacts. This method is based on an undercomplete independent component analysis, aiming to estimate the unmixing matrix using a non-linear Cumulative Density Function (CDF) that has been optimized using the customized Levenberg-Marquardt algorithm. Therefore, the method is named U-LMA. The proposed method was tested under three scenarios: constrained, motion, and illumination variations scenarios. High Pearson correlation coefficient values and smaller lower-upper statistical limits of Bland-Altman plots justified the outstanding performance of the proposed U-LMA. Furthermore, its comparative analysis with the state-of-theart methods demonstrated its efficacy and reliability, which was proven by the lowest error and highest correlation values (0.01 significance level). Additionally, higher accuracy satisfying the clinically accepted error differences also justified its clinical relevance.

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“…However, the effectiveness of ROI selection-based methods is limited when the participant exhibits non-rigid motion, such as facial expressions or talking. To overcome this challenge and accurately obtain the rPPG signal, several methods have been proposed to separate the rPPG signal from mixed-signals, including independent component analysis (ICA), 13 blind source separation, 14 , 15 empirical mode decomposition (EMD), 16 and wavelet transform 17 . Furthermore, model-based methods have also been developed, such as spatial subspace rotation (2SR) 10 and adaptive pulsatile plane 18 …”

Section: Introductionmentioning

confidence: 99%

Heart rate estimation by leveraging static and dynamic region weights

Liu,

Xia,

Zhang

et al. 2023

J. Electron. Imag.

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Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Cited by 8 publications

References 35 publications

Emotional State Prediction Based on EEG Signals using Ensemble Methods

Emotional State Prediction Based on EEG Signals using Ensemble Methods

A Motion and Illumination Resistant Non-Contact Method Using Undercomplete Independent Component Analysis and Levenberg-Marquardt Algorithm

Heart rate estimation by leveraging static and dynamic region weights

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