Facial Video-Based Respiratory Rate Recognition Interpolating Pulsatile PPG Rise And Fall Times

Fiedler, Marc-André; Rapczyński, Michał; Al-Hamadi, Ayoub

doi:10.1109/isbi48211.2021.9434132

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Photoplethysmography (PPG) is a non-invasive photoelectric biomedical monitoring technology which records the changes of blood volume in microvascular tissue caused by cardiac diastolic and systolic activities [1]. It has been exploited in a wide range of medical devices for measuring heart rate [2], respiratory rate [3], assessing autonomic function [4,5], and detecting peripheral vascular disease [6]. For instance, the accuracy of estimated heart rate was up to 95% by utilizing a novel PPG sensor patch; [2] the person's respiratory rate was determined from the difference between the rise and fall times of the PPG signal with the correct rate >90%; [3] for the diagnosis of depression, PPG waveform's changes evoked by stress stimuli were used to assess the autonomic response [5].…”

Section: Introductionmentioning

confidence: 99%

“…It has been exploited in a wide range of medical devices for measuring heart rate [2], respiratory rate [3], assessing autonomic function [4,5], and detecting peripheral vascular disease [6]. For instance, the accuracy of estimated heart rate was up to 95% by utilizing a novel PPG sensor patch; [2] the person's respiratory rate was determined from the difference between the rise and fall times of the PPG signal with the correct rate >90%; [3] for the diagnosis of depression, PPG waveform's changes evoked by stress stimuli were used to assess the autonomic response [5]. Those studies validate it was a convenient and reliable method to obtain physical information from PPG signals, in recent years, more contributions were paid to real-time monitor health status of examinees from their PPG signals, with the ambitions of developing wearable cuff-less sphygmomanometer [7], heart rate meter [8], respiratory rate equipment [9] and so on.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A method for portable PPG signal dynamic acquisition and denoise based on CEEMDAN and wavelet transform

Tian

Jia

2023

J. Inst.

View full text Add to dashboard Cite

Photoplethysmography (PPG) is a non-invasive technology and widely used in medical monitoring. Nowadays, dynamic vital signs monitoring based on PPG technology is in emergence and shows great potential for commercialization, however, it is still challenged by massive noise evoked by respiration and muscle contraction. Herein, a portable PPG signal's dynamic acquisition and denoise system is constructed and applied to blood pressure (BP) estimation, in which an improved PPG denoise method based on complete ensemble empirical mode decomposition with adaptive noise and wavelet transform (CEEMDAN-WT), is described. Firstly, original PPG signal is measured by the proposed hardware, then CEEMDAN decomposes it into a group of components. Secondly, the noise dominated components are found by calculating the coefficients between the components and original signal, and removed by WT. Thirdly, fast Fourier transform is performed to remove the component whose dominant frequency exceeds 0.5–20 Hz. Fourthly, a fresh PPG signal is reconstructed and compared with the signal rebuilt by other methods, which proves that CEEMDAN-WT has higher signal-to-noise ratio and lower root mean square error. Last but not least, the reconstructed signal is applied to estimate systolic and diastolic BP, according to Windkessel model and aided by neural network algorithm. Overall, this work demonstrates the feasibility of the portable PPG dynamic acquisition and its application for dynamic vital signs monitoring, in which CEEMDAN-WT algorithm can effectively remove most of the noises in dynamic PPG signal. In conclusion, it demonstrates CEEMDAN-WT method can effectively remove noise from PPG signals in the state of motion, it may have a good potential for calculating other physiological indexes besides BP, and push PPG applications from professional medical to daily life.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A method for portable PPG signal dynamic acquisition and denoise based on CEEMDAN and wavelet transform

Tian

Jia

2023

J. Inst.

View full text Add to dashboard Cite

show abstract

Exploring facial cues: automated deception detection using artificial intelligence

Dinges,

Fiedler,

Al-Hamadi

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

Deception detection is an interdisciplinary field attracting researchers from psychology, criminology, computer science, and economics. Automated deception detection presents unique challenges compared to traditional polygraph tests, but also offers novel economic applications. In this spirit, we propose an approach combining deep learning with discriminative models for deception detection. Therefore, we train CNNs for the facial modalities of gaze, head pose, and facial expressions, allowing us to compute facial cues. Due to the very limited availability of training data for deception, we utilize early fusion on the CNN outputs to perform deception classification. We evaluate our approach on five datasets, including four well-known publicly available datasets and a new economically motivated rolling dice experiment. Results reveal performance differences among modalities, with facial expressions outperforming gaze and head pose overall. Combining multiple modalities and feature selection consistently enhances detection performance. The observed variations in expressed features across datasets with different contexts affirm the importance of scenario-specific training data for effective deception detection, further indicating the influence of context on deceptive behavior. Cross-dataset experiments reinforce these findings. Notably, low-stake datasets, including the rolling dice Experiment, present more challenges for deception detection compared to the high-stake Real-Life trials dataset. Nevertheless, various evaluation measures show deception detection performance surpassing chance levels. Our proposed approach and comprehensive evaluation highlight the challenges and potential of automating deception detection from facial cues, offering promise for future research.

show abstract

Deep face segmentation for improved heart and respiratory rate estimation from videos

Fiedler

Werner

Rapczyński

et al. 2023

J Ambient Intell Human Comput

View full text Add to dashboard Cite

The selection of a suitable region of interest (ROI) is of great importance in camera-based vital signs estimation, as it represents the first step in the processing pipeline. Since all further processing relies on the quality of the signal extracted from the ROI, the tracking of this area is decisive for the performance of the overall algorithm. To overcome the limitations of classical approaches for the ROI, such as partial occlusions or illumination variations, a custom neural network for pixel-precise face segmentation called FaSeNet was developed. It achieves better segmentation results on two datasets compared to state-of-the-art architectures while maintaining high execution efficiency. Furthermore, the Matthews Correlation Coefficient was proposed as a loss function providing a better fitting of the network weights than commonly applied losses in the field of multi-class segmentation. In an extensive evaluation with a variety of algorithms for vital signs estimation, our FaSeNet was able to achieve better results in both heart and respiratory rate estimation. Thus, a ROI for vital signs estimation could be created that is superior to other approaches.

show abstract

Facial Video-Based Respiratory Rate Recognition Interpolating Pulsatile PPG Rise And Fall Times

Cited by 3 publications

References 16 publications

A method for portable PPG signal dynamic acquisition and denoise based on CEEMDAN and wavelet transform

A method for portable PPG signal dynamic acquisition and denoise based on CEEMDAN and wavelet transform

Exploring facial cues: automated deception detection using artificial intelligence

Deep face segmentation for improved heart and respiratory rate estimation from videos

Contact Info

Product

Resources

About