Comparison of Blind Source Separation Algorithms for Optical Heart Rate Monitoring

Christinaki, Eirini; Giannakakis, Giorgos; Chiarugi, Franco; Pediaditis, Matthew; Iatraki, Galateia; Manousos, Dimitris; Marias, Kostas; Tsiknakis, Manolis

doi:10.4108/icst.mobihealth.2014.257248

Cited by 20 publications

(29 citation statements)

References 26 publications

(28 reference statements)

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“…The results showed that some algorithms (i.e. FastICA, RADICAL) gave better results than other (JADE, Robust ICA) [4,5]. However, in [5] the authors showed that sometimes there were problems with ICA algorithms, for example, FastICA failed to properly initialize.…”

Section: Introductionmentioning

confidence: 93%

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Reliability of Pulse Measurements in Videoplethysmography

Rumiński¹

2016

Metrology and Measurement Systems

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Reliable, remote pulse rate measurement is potentially very important for medical diagnostics and screening. In this paper the Videoplethysmography was analyzed especially to verify the possible use of signals obtained for the YUV color model in order to estimate the pulse rate, to examine what is the best pulse estimation method for short video sequences and finally, to analyze how potential PPG-signals can be distinguished from other (e.g. background) signals. The presented methods were verified using data collected from 60 volunteers.

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Section: Introductionmentioning

confidence: 93%

“…Later, many different ICA algorithms have been evaluated including FastICA, Jade, RADICAL [4]. The results showed that some algorithms (i.e.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Pulse Measurements in Videoplethysmography

Rumiński¹

2016

Metrology and Measurement Systems

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“…2002 [1]. Since then, many researchers have worked with camera-based photoplethysmography, most often to assess the heart rate [2][3][4][5][6][7][8]. Since the technique is highly sensitive to artifacts, induced by movements and lightening changes, The authors would like to thank the Saxon state ministry of science and culture (SMWK) and the European Union for funding the project CardioVisio -Contactless acquistion of vital parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Proposed techniques use segments of different color channels (typically RGB) extracted from regions of interest (ROI), typically the face, as input to PCA or JADE ICA [4,[9][10][11][12][13][14]. FastICA [15] has also been applied to RGB signals [6,14] and achieved a slightly better performance in comparison to other ICA algorithms [6]. Tsouri et al [16], on the other hand, used RGB information of a face ROI as input for a constrained ICA.…”

Section: Introductionmentioning

confidence: 99%

Assessment of source separation techniques to extract vital parameters from videos

Wedekind

Trumpp

Andreotti³

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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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 cbPPG's analysis but signal properties like the signal-to-noise-ratio should be considered before applying BSS techniques.

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“…The cardiac refractory period ∆t R is considered as 0.3 s [10], whereas the maximum peak-topeak distance ∆t max is considered as 1.5 s (i.e. a minimum heart rate of 45 bpm [13]). Accordingly, a sequence (x) ∈ {0, 1} is obtained by:…”

Section: Spatio-temporal Bss Output Channel (Bss) Including Its Envelmentioning

confidence: 99%

Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation

Wedekind

Kleyko

Osipov

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Wearable sensor technology like textile electrodes provides novel ambulatory health monitoring solutions but most often goes along with low signal quality. Blind Source Separation (BSS) is capable of extracting the Electrocardiogram (ECG) out of heavily distorted multichannel recordings. However, permutation indeterminacy has to be solved, i.e. the automated selection of the desired BSS output. To that end we propose to exploit the sparsity of the ECG modeled as a spike train of successive heartbeats. A binary code derived from a two-item dictionary {peak, no peak} and physiological a-priori information temporally represents every BSS output component. The (best) ECG component is automatically selected based on a modified Hamming distance comparing the components' code with the expected code behavior.Non-standard ECG recordings from ten healthy subjects performing common motions while wearing a sensor garment were subsequently processed in 10 s segments with spatio-temporal BSS. Our sparsity-based selection RCODE achieved 98.1% heart beat detection accuracy (ACC) by selecting a single component each after BSS. Traditional component selection based on higherorder statistics (e.g. skewness) achieved only 67.6% ACC. IntroductionAmbulatory vital sign recording supplements the standard clinical data acquisition by long-term measurements for early diagnosis of diseases or health and stress monitoring of people performing potentially dangerous tasks. Using measurement techniques like textile electrodes for wearable sensing, the recorded electrocardiogram (ECG) is of non-standard nature compared to its clinical counterpart. Moreover, the minimal-conductive measurement principle which allows for a flexible health monitoring is also strongly affected by movement artifacts [1].Blind Source Separation (BSS) is a signal processing technique capable of separating signal mixtures (e.g. mixtures of ECG and distortions) into its constituting components [2]. Spatio-temporal Independent Component Analysis (ICA) based on the FastICA algorithm [2] is one realization of BSS which has shown a superior performance on wearable data compared to the standard ICA [1]. Despite its ability to separate ECG from distortions, ICA is typically only solved up to a permutation (i.e. separated components are available but the output is mostly unordered). Accordingly, a desired output component (e.g. the one best representing the ECG) has to be automatically selected. This selection gains special interest while processing a large number of channels. This is typical for ambulatory multi-channel health recordings and particularly spatio-temporal ICA which adds extra channels during the processing [3].Two principles for handling permutation indeterminacy have been proposed in the context of ECG processing. The first principle identifies and discards the undesired components (i.e. artifacts) thus indirectly obtaining the ECG component. A combination of second-order and higherorder statistics was used for that purpose in [4] whereas auto-correlat...

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Comparison of Blind Source Separation Algorithms for Optical Heart Rate Monitoring

Cited by 20 publications

References 26 publications

Reliability of Pulse Measurements in Videoplethysmography

Reliability of Pulse Measurements in Videoplethysmography

Assessment of source separation techniques to extract vital parameters from videos

Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation

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