20,323 research outputs found
Constrained independent component analysis for non-obtrusive pulse rate measurements using a webcam
Assessment of cardiac function of a patient is very important for understanding a patient\u27s physiological state. Remote measurements of the cardiac pulse can provide comfortable physiological assessment by minimizing the amount of wires and cables and allowing for near continuous measurements. It has been found that state-of-the-art algorithms based on independent component analysis (ICA) suffer from a sorting problem which hinders their performance. This effect is demonstrated in this work. The automated pulse detection techniques are applied to RGB color video recordings of the facial region of a person being monitored for cardiac function in a remote sensing environment. Automated face tracking is employed to locate the region of interest and address motion artefacts. This work proposed and evaluates a novel algorithm based on constrained source separation, aka, constrained independent source separation (cICA) to accurately estimate the pulse rate of a patient by solving the sorting problem observed in the ICA based approach. The constrained optimization problem incorporates prior information and additional requirements in the form of constraints. A reference signal with a single tone frequency corresponding to a possible heart rate is fed to the cICA algorithm. This forces the output signal to match the reference signal embodying prior knowledge about an underlying IC. It is also shown that with this algorithm a near photoplethysmography (PPG) signal corresponding to the variations in blood volume in the body can be extracted. An IRB approved study encompassing 45 subjects resulted in Bland-Altman analysis with an FDA-approved finger blood volume pulse (BVP) sensor demonstrating that the proposed algorithm provides significantly improved accuracy
Blind Source Separation for the Processing of Contact-Less Biosignals
(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features
A multimodal approach to blind source separation of moving sources
A novel multimodal approach is proposed to solve the
problem of blind source separation (BSS) of moving sources. The
challenge of BSS for moving sources is that the mixing filters are
time varying; thus, the unmixing filters should also be time varying,
which are difficult to calculate in real time. In the proposed approach,
the visual modality is utilized to facilitate the separation for
both stationary and moving sources. The movement of the sources
is detected by a 3-D tracker based on video cameras. Positions
and velocities of the sources are obtained from the 3-D tracker
based on a Markov Chain Monte Carlo particle filter (MCMC-PF),
which results in high sampling efficiency. The full BSS solution
is formed by integrating a frequency domain blind source separation
algorithm and beamforming: if the sources are identified
as stationary for a certain minimum period, a frequency domain
BSS algorithm is implemented with an initialization derived from
the positions of the source signals. Once the sources are moving, a
beamforming algorithm which requires no prior statistical knowledge
is used to perform real time speech enhancement and provide
separation of the sources. Experimental results confirm that
by utilizing the visual modality, the proposed algorithm not only
improves the performance of the BSS algorithm and mitigates the
permutation problem for stationary sources, but also provides a
good BSS performance for moving sources in a low reverberant
environment
Surface EMG decomposition using a novel approach for blind source separation
We introduce a new method to perform a blind deconvolution of the surface electromyogram (EMG) signals generated by isometric muscle contractions. The method extracts the information from the raw EMG signals detected only on the skin surface, enabling longtime noninvasive monitoring of the electromuscular properties. Its preliminary results show that surface EMG signals can be used to determine the number of active motor units, the motor unit firing rate and the shape of the average action potential in each motor unit
Noninvasive methods for children\u27s cholesterol level determination
Today, there is a controversy about the role of cholesterol in infants and the measurement and management of blood cholesterol in children. Several scientific evidences are supporting relationship between elevated blood cholesterol in children and high cholesterol in adults and development of adult arteriosclerotic diseases such as cardiovascular and cerebrovascular disease. Therefore controlling the level of blood cholesterol in children is very important for the health of the whole population. Non-invasive methods are much more convenient for the children because of their anxieties about blood examinations. In this paper we will present a new try to find non-invasive methods for determining the level of blood cholesterol in children with the use of intelligent system
Multimodal methods for blind source separation of audio sources
The enhancement of the performance of frequency domain convolutive
blind source separation (FDCBSS) techniques when applied to the
problem of separating audio sources recorded in a room environment
is the focus of this thesis. This challenging application is termed the
cocktail party problem and the ultimate aim would be to build a machine
which matches the ability of a human being to solve this task.
Human beings exploit both their eyes and their ears in solving this task
and hence they adopt a multimodal approach, i.e. they exploit both
audio and video modalities. New multimodal methods for blind source
separation of audio sources are therefore proposed in this work as a
step towards realizing such a machine.
The geometry of the room environment is initially exploited to improve
the separation performance of a FDCBSS algorithm. The positions
of the human speakers are monitored by video cameras and this
information is incorporated within the FDCBSS algorithm in the form
of constraints added to the underlying cross-power spectral density
matrix-based cost function which measures separation performance. [Continues.
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