Daniel Wedekind

Automated identification of cardiac signals after blind source separation for camera-based photoplethysmography

In the field of camera-based photoplethysmography the application of blind source separation (BSS) techniques has extensively stressed to cope with frequently occurring artifacts and noise. Although said techniques can help to extract the cardiac component from a mixture of input sources, permutation indeterminacy inherit to BSS techniques often introduces inaccuracies or requires manual intervention. The current contribution focuses on methods to automatically select the cardiac component from the output of BSS techniques applied to camera-based photoplethysmograms. To that end, we propose simple Markov models to describe and subsequently identify cardiac components. It is shown that good results can be obtained by combining different simple Markov models.

Camera-based photoplethysmography in critical care patients

BACKGROUND Camera-based photoplethysmography (cbPPG) is an optical measurement technique that reveals pulsatile blood flow in cutaneous microcirculation from a distance. cbPPG has been shown to reflect pivotal haemodynamic events like cardiac ejection in healthy subjects. In addition, it provides valuable insight into intrinsic microcirculatory regulation as it yields dynamic, two-dimensional perfusion maps. In this study, we evaluate the feasibility of a clinical cbPPG application in critical care patients. METHODS A mobile camera set-up to record faces of patients at the bed site was constructed. Videos were made during the immediate recovery after cardiac surgery under standard critical care conditions and were processed offline. Major motion artefacts were detected using an optical flow technique and suitable facial regions were manually annotated. cbPPG signals were highpass filtered and Fourier spectra out of consecutive 10s signal segments calculated for heart rate detection. Signal-to-noise ratios (SNR) of the Fourier spectra were derived as a quality measure. Reference data of vital parameters were synchronously acquired from the bed site monitoring system. RESULTS Seventy patient videos of an average time of 28.6±2.8 min were analysed. Heart rate (HR) was detected within a±5 bpm range compared to reference in 83% of total recording time. Low SNR and HR detection failure were mostly, but not exclusively, attributed to non-physiological events like patient motion, interventions or sudden changes of illumination. SNR was reduced by low arterial blood pressure, whereas no impact of other perioperative or disease-related parameters was identified. CONCLUSION Cardiac ejection is detectable by cbPPG under pathophysiologic conditions of cardiovascular disease and perioperative medicine. cbPPG measurements can be seamlessly integrated into the clinical work flow of critical care patients.

Cascaded output selection for processing of capacitive electrocardiograms by means of independent component analysis

Innovative measurement systems allow for the contactless recording of vital signs. Thus, applications with medical background for daily life become possible. Aquired signals, however, often cannot compete with their clinically established counterparts. In fact, typical characteristics as small signal amplitudes on the one hand, frequently occuring artefacts and noise on the other hand, introduce the apparent need for sophisticated processing techniques to allow for a reliable function when thinking of contactless measurements. This contribution investigates the possibility of using multichannel capacitive electrocardiogram (cECG) recordings to derive the heart rate for driver monitoring. We propose a processing scheme consisting of a spatio-temporal independent component analysis applied to the cECG together with a newly developed method to select the most appropriate of the output channels by analyzing their frequency characteristics. By an experimental study incorporating 27 healthy subjects we prove the applicability of our method and discuss its advantages compared to existing methods.

Assessment of blind source separation techniques for video-based cardiac pulse extraction

Abstract. Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-to-noise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.

Skin Detection and Tracking for Camera-Based Photoplethysmography Using a Bayesian Classifier and Level Set Segmentation

Camera-Based Photoplethysmography is a measuring technique that permits the remote assessment of vital signs by using cameras. The face is the preferred area of measurement (region of interest: ROI) that has to be selected automatically for convenient application. Most works use common face detection algorithm for this purpose. However, these approaches often fail if the face is partly occluded or distorted. In this work, we propose an automatic method for ROI detection and tracking that does not rely on facial features. First, a Bayesian skin classifier was applied. Second, the detected areas were refined and tracked by level set segmentation. We tested our method on videos of 70 patients. The determined ROIs were used for signal extraction and heart rate (HR)estimation. The results showed that our method can detect and track suitable skin regions. We achieved a median HR detection rate of 80% which was only 6% lower than when applying manually defined ROIs.

Assessment of source separation techniques to extract vital parameters from videos

Camera-based photoplethysmography is a contactless mean to assess vital parameters, such as heart rate and respiratory rate. In the field of camera-based photoplethysmography, blind source separation (BSS) techniques have been extensively applied to cope with artifacts and noise. Despite their wide usage, there is no consensus that common BSS approaches contribute to an improved analysis of camera-based photoplethysmograms (cbPPG). This contribution compares previously proposed multispectral BSS techniques to a novel spatial BSS approach for heart rate extraction from cbPPG. Our analysis indicates that the application of BSS techniques not necessarily improves cbPPGs analysis but signal properities like the signal-to-noise-ratio should be considered before i applying BSS techniques.

Camera-based photoplethysmography in an intraoperative setting

BackgroundCamera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment.MethodsWe recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity.ResultsThe results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations.ConclusionsThe proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.

Relation between pulse pressure and the pulsation strength in camera-based photoplethysmograms

Abstract Camera-based photoplethysmography (cbPPG) is an innovative measuring technique that enables the remote extraction of vital signs using video cameras. Most studies in the field focus on heart rate detection while other physiological quantities are often ignored. In this work, we analyzed the relation between the pulse pressure and the pulsation strengths of cbPPG signals for 70 patients after surgery. Our results show a high correlation between the two measures (r = 0.54). Furthermore, the influence of technical and medical factors was tested. The controlled impact of these factors proved to enhance the correlation by between 9 and 27 %.

Cardiovascular assessment by imaging photoplethysmography – a review

Abstract Over the last few years, the contactless acquisition of cardiovascular parameters using cameras has gained immense attention. The technique provides an optical means to acquire cardiovascular information in a very convenient way. This review provides an overview on the technique’s background and current realizations. Besides giving detailed information on the most widespread application of the technique, namely the contactless acquisition of heart rate, we outline further concepts and we critically discuss the current state.