Heart Rate and Heart Rate Variability From Single-Channel Video and ICA Integration of Multiple Signals

Favilla, Riccardo; Zuccalà, Veronica Chiara; Coppini, Giuseppe

doi:10.1109/jbhi.2018.2880097

Cited by 51 publications

(22 citation statements)

References 35 publications

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“…In 2014, iPPG signals were extracted using a constrained independent component analysis (ICA) method and adaptive filters [18]. Favilla et al used ICA pre-processing to enhance iPPG signals from different regions of the face [19]. Zhang et al proposed a six-channel ICA algorithm to separate more potential sources from smartphone videos [20].…”

Section: Related Workmentioning

confidence: 99%

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Cai

Yue

et al. 2020

IEEE Access

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Video-based noncontact detection of heart rate has a wide range of applications in the field of medicine and health. However, this method is susceptible to noise interference, making it difficult to effectively extract blood volume pulse (BVP) signals. To overcome this problem, a new method of noncontact heart rate estimation that can suppress noise interference is proposed in this paper. First, the established data acquisition system conducts video collection, and the captured videos are divided into multiple small regions. Subsequently, the initial signals of BVP are extracted in accordance with the chrominance features extracted through multi-channel data fusion. The BVP signals are separated using the FastICA algorithm. The kurtosis value and signal-to-noise ratios of the power spectrum of the separated signals are analyzed to determine the effective separation component. Results show that this method can extract and process pulse signals, effectively suppressing non-periodic interference. The experiment also proves that the method has good consistency with the measurement of pulse oximeter and has good stability and accuracy in the detection of heart rate of the human body. INDEX TERMS Chrominance features, kurtosis, photoplethysmography(PPG), data harvesting fusion.

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Section: Related Workmentioning

confidence: 99%

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Cai

Yue

et al. 2020

IEEE Access

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“…The ECG signal was analyzed as described in [12]. The tachogram was firstly extracted from the ECG signals.…”

Section: Ecg Signal Analysismentioning

confidence: 99%

“…The video signal was analyzed as described in [12]. According to [12], for each video sequence, the Cascading Classifiers algorithm (OpenCV v. 3.0.0) was used to detect the face of the subject and locating the center of each eye in the first frame of the recorded video.…”

Section: Video Signal Analysismentioning

confidence: 99%

Unobtrusive Monitoring of ECG and EEG During Mild Stress Stimuli

Zuccalà

Favilla

Coppini

2020

2020 Computing in Cardiology Conference (CinC)

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This study is part of a research project aiming to build a model for quantifying an individual wellness status through unobtrusive measurements of psychophysical parameters and self-reported data. In particular, we focus on the evaluation of the individual response to mild stress stimuli. The experimental setup included: EG05000 Medlab Five Channel Module, Gigabit Ethernet camera, BioHarness 3 Zephyr chest belt, and Muse 2 Headband. Experimental results show increased heart rate and respiration rate, and changes of the brain activity in the stress condition. This is consistent with a "fight or flight" response in accordance with literature. Therefore, the methodology applied in this study can be used to monitor the individual wellness status in conditions of mild stress stimuli.

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“…Since a color camera is normally one-half to one meter away from a subject's skin, non-ideal illumination conditions and the subject's voluntary movements can influence the signal quality. Although effective denoising operations [25]- [28] have been employed to deal with realistic conditions and improve HR estimation accuracy from the rPPG signal, the research in RR estimation from such signal is still limited, and previous works [21], [29], [30] mainly estimate RR via heart rate variability (HRV) extraction [31] in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Modulation Model of the Photoplethysmography Signal for Vital Sign Extraction

Chen

Zhu

et al. 2021

IEEE J. Biomed. Health Inform.

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This paper introduces an amplitude and frequency modulation (AM-FM) model to characterize the photoplethysmography (PPG) signal. The model indicates that the PPG signal spectrum contains one dominant frequency component-the heart rate (HR), which is guarded by two weaker frequency components on both sides; the distance from the dominant component to the guard components represents the respiratory rate (RR). Based on this model, an efficient algorithm is proposed to estimate both HR and RR by searching for the dominant frequency component and two guard components. The proposed method is performed in the frequency domain to estimate RR, which is more robust to additive noise than the prior art based on temporal features. Experiments were conducted on two types of PPG signals collected with a contact sensor (an oximeter) and a contactless visible imaging sensor (a color camera), respectively. The PPG signal from the contactless sensor is much noisier than the signal from the contact sensor. The experimental results demonstrate the effectiveness of the proposed algorithm, including under relatively noisy scenarios.

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Heart Rate and Heart Rate Variability From Single-Channel Video and ICA Integration of Multiple Signals

Cited by 51 publications

References 35 publications

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Unobtrusive Monitoring of ECG and EEG During Mild Stress Stimuli

Modulation Model of the Photoplethysmography Signal for Vital Sign Extraction

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