Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287603
Valentin Debarnot, Paul Escande, T. Mangeat, P. Weiss
We propose a novel approach to calibrate a microscope. Instead of seeking a single linear integral operator (e.g. a convolution with a point spread function) that describes its action, we propose to describe it as a low-dimensional linear subspace of operators. By doing so, we are able to capture its variations with respect to multiple factors such as changes of temperatures and refraction indexes, tilts of optical elements or different states of spatial light modulator. While richer than usual, this description however suffers from a serious limitation: it cannot be used directly to solve the typical inverse problems arising in computational imaging. As a second contribution, we therefore design an original algorithm to identify the operator from the image of a few isolated spikes. This can be achieved experimentally by adding fluorescent micro-beads around the sample. We demonstrate the potential of the approach on a challenging deblurring problem.Important note: this paper is an abridged version of a preprint [3] by the same authors, submitted for a journal publication.
{"title":"Modelling a Microscope as Low Dimensional Subspace of Operators","authors":"Valentin Debarnot, Paul Escande, T. Mangeat, P. Weiss","doi":"10.23919/Eusipco47968.2020.9287603","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287603","url":null,"abstract":"We propose a novel approach to calibrate a microscope. Instead of seeking a single linear integral operator (e.g. a convolution with a point spread function) that describes its action, we propose to describe it as a low-dimensional linear subspace of operators. By doing so, we are able to capture its variations with respect to multiple factors such as changes of temperatures and refraction indexes, tilts of optical elements or different states of spatial light modulator. While richer than usual, this description however suffers from a serious limitation: it cannot be used directly to solve the typical inverse problems arising in computational imaging. As a second contribution, we therefore design an original algorithm to identify the operator from the image of a few isolated spikes. This can be achieved experimentally by adding fluorescent micro-beads around the sample. We demonstrate the potential of the approach on a challenging deblurring problem.Important note: this paper is an abridged version of a preprint [3] by the same authors, submitted for a journal publication.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"209 1","pages":"765-769"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80579619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287725
Marcele O. K. Mendonça, P. Diniz
To combat the inter-symbol interference (ISI) and the inter-block interference (IBI) caused by multi-path fading in orthogonal frequency-division multiplexing (OFDM) systems, it is usually recommended employing a cyclic prefix (CP) with length equal to the channel order. In some practical cases, however, the channel order is not exactly known. Looking for a balance between a full-sized CP and its absence, we investigate the redundancy issues and propose a minimum redundancy OFDM receiver using deep-learning (DL) tools. In this way, we can benefit from an improved reception performance, when compared with CP-free case, and also a better spectrum utilization when compared with the CP-OFDM case. Moreover, compared with the CP-free case, improved performance can be obtained even when the channel order is not available. Simulation results indicate that a good BER level can be achieved and the proposed technique can also be applied in other DL-based receivers.
{"title":"OFDM Receiver Using Deep Learning: Redundancy Issues","authors":"Marcele O. K. Mendonça, P. Diniz","doi":"10.23919/Eusipco47968.2020.9287725","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287725","url":null,"abstract":"To combat the inter-symbol interference (ISI) and the inter-block interference (IBI) caused by multi-path fading in orthogonal frequency-division multiplexing (OFDM) systems, it is usually recommended employing a cyclic prefix (CP) with length equal to the channel order. In some practical cases, however, the channel order is not exactly known. Looking for a balance between a full-sized CP and its absence, we investigate the redundancy issues and propose a minimum redundancy OFDM receiver using deep-learning (DL) tools. In this way, we can benefit from an improved reception performance, when compared with CP-free case, and also a better spectrum utilization when compared with the CP-OFDM case. Moreover, compared with the CP-free case, improved performance can be obtained even when the channel order is not available. Simulation results indicate that a good BER level can be achieved and the proposed technique can also be applied in other DL-based receivers.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"95 1","pages":"1687-1691"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88555371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287783
Florent Bouchard, A. Breloy, G. Ginolhac, A. Renaux
The blind source separation problem is considered through the approach based on non-stationarity and coloration. In both cases, the sources are usually assumed to be Gaussian. In this paper, we extend previous works in order to handle sources drawn from the multivariate Student t-distribution. After studying the structure of the parameter manifold in this case, a new blind source separation criterion based on the log-likelihood of the considered distribution is proposed. To solve the resulting optimization problem, Riemannian optimization on the parameter manifold is leveraged. Practical expressions of the mathematical tools required by first order based Riemmanian optimization methods for this parameter manifold are derived to this end. The performance of the proposed method is illustrated on simulated data.
{"title":"A Riemannian approach to blind separation of t-distributed sources","authors":"Florent Bouchard, A. Breloy, G. Ginolhac, A. Renaux","doi":"10.23919/Eusipco47968.2020.9287783","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287783","url":null,"abstract":"The blind source separation problem is considered through the approach based on non-stationarity and coloration. In both cases, the sources are usually assumed to be Gaussian. In this paper, we extend previous works in order to handle sources drawn from the multivariate Student t-distribution. After studying the structure of the parameter manifold in this case, a new blind source separation criterion based on the log-likelihood of the considered distribution is proposed. To solve the resulting optimization problem, Riemannian optimization on the parameter manifold is leveraged. Practical expressions of the mathematical tools required by first order based Riemmanian optimization methods for this parameter manifold are derived to this end. The performance of the proposed method is illustrated on simulated data.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"22 1","pages":"965-969"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90579993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pervasive detection and quantification of tremor for Parkinson’s Disease (PD) patients, using Commercial Off-the-self (COTS) wrist-wearable device is an important problem to investigate. Parkinsonian tremor is one of the earliest and major surrogate biomarker which indicates the progress or status of the disease for patients under treatment using drugs or deep brain stimulation (DBS) therapy. However, it is a challenging issue as tremor occurs at the minor extremities like fingers in some cases such as pill-rolling symptom, the effect of the same on a wrist-worn motion sensor system is not significant enough to be captured. In this paper, we explore the possibility of using the wrist-based photoplethysmography (PPG) as a novel sensor modality in detecting tremor at rest. Our preliminary results gathered from healthy cohorts performing simulations of Parkinsonian tremor elucidates the merit of the proposed method. Also, since PPG acquisition is power-hungry, we have leveraged a conceptual method of compressive sensing to reduce the overall power requirement of the application.
{"title":"A Novel Non-Parametric Approach Of Tremor Detection Using Wrist-Based Photoplethysmograph","authors":"Nasimuddin Ahmed, Chirayata Bhattacharyya, Avik Ghose","doi":"10.23919/Eusipco47968.2020.9287346","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287346","url":null,"abstract":"Pervasive detection and quantification of tremor for Parkinson’s Disease (PD) patients, using Commercial Off-the-self (COTS) wrist-wearable device is an important problem to investigate. Parkinsonian tremor is one of the earliest and major surrogate biomarker which indicates the progress or status of the disease for patients under treatment using drugs or deep brain stimulation (DBS) therapy. However, it is a challenging issue as tremor occurs at the minor extremities like fingers in some cases such as pill-rolling symptom, the effect of the same on a wrist-worn motion sensor system is not significant enough to be captured. In this paper, we explore the possibility of using the wrist-based photoplethysmography (PPG) as a novel sensor modality in detecting tremor at rest. Our preliminary results gathered from healthy cohorts performing simulations of Parkinsonian tremor elucidates the merit of the proposed method. Also, since PPG acquisition is power-hungry, we have leveraged a conceptual method of compressive sensing to reduce the overall power requirement of the application.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"90 1","pages":"1150-1154"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80617464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287876
E. Diop, K. Skretting, A. Boudraa
We propose here an extension to images of a sparse coding frequency separation method. The approach is based on a 2D multicomponent amplitude modulation (AM)-frequency modulation (FM) image modeling, where the 2D monocomponent parts are obtained by sparse approximations that are solved with matching pursuits. For synthetic images, a separable dictionary is built, while a patch-based dictionary learning method is adopted for real images. In fact, the total variation (TV) norm is applied on patches to select the decomposition modes with highest TV-norm, doing so yields to an interesting image analysis tool that properly separates the image frequency contents. The proposed approach turns out to share the same behaviors with the well known empirical mode decomposition (EMD) method. Obtained results are encouraging for feature and texture analysis, and for image denoising as well.
{"title":"AM-FM Image Analysis based on Sparse Coding Frequency Separation Approach","authors":"E. Diop, K. Skretting, A. Boudraa","doi":"10.23919/Eusipco47968.2020.9287876","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287876","url":null,"abstract":"We propose here an extension to images of a sparse coding frequency separation method. The approach is based on a 2D multicomponent amplitude modulation (AM)-frequency modulation (FM) image modeling, where the 2D monocomponent parts are obtained by sparse approximations that are solved with matching pursuits. For synthetic images, a separable dictionary is built, while a patch-based dictionary learning method is adopted for real images. In fact, the total variation (TV) norm is applied on patches to select the decomposition modes with highest TV-norm, doing so yields to an interesting image analysis tool that properly separates the image frequency contents. The proposed approach turns out to share the same behaviors with the well known empirical mode decomposition (EMD) method. Obtained results are encouraging for feature and texture analysis, and for image denoising as well.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"103 1","pages":"610-614"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80641154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287503
J. Demmer, A. Kitzig, G. Stockmanns, E. Naroska, R. Viga, A. Grabmaier
The paper considers the optimization of a Hidden-Markov Model (HMM) based method for the generation of averaged motion sequences. To create averaged motion sequences, motion sequences of different test persons were originally recorded with a motion capture system (MoCap system) and then averaged using an HMM approach. The resulting averaged data sets, however, partly showed serious motion artifacts and uncoordinated intermediate movements, especially in the extremities. The aim of this work was to combine only movements with similar courses in the extremities by a suitable cluster analysis. For each test person, model body descriptions of 21 body elements are available, each of which is represented in three-dimensional time series. For optimization, the MoCap data are first compared using time warp edit distance (TWED) and clustered using an agglomerative hierarchical procedure. Finally, the data of the resulting clusters are used to generate new averaged motion sequences using the HMM approach. The resulting averaged data can be used, for example, in a simulation in a multilevel biomechanical model.
{"title":"Adaptation of cluster analysis methods to optimize a biomechanical motion model of humans in a nursing bed","authors":"J. Demmer, A. Kitzig, G. Stockmanns, E. Naroska, R. Viga, A. Grabmaier","doi":"10.23919/Eusipco47968.2020.9287503","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287503","url":null,"abstract":"The paper considers the optimization of a Hidden-Markov Model (HMM) based method for the generation of averaged motion sequences. To create averaged motion sequences, motion sequences of different test persons were originally recorded with a motion capture system (MoCap system) and then averaged using an HMM approach. The resulting averaged data sets, however, partly showed serious motion artifacts and uncoordinated intermediate movements, especially in the extremities. The aim of this work was to combine only movements with similar courses in the extremities by a suitable cluster analysis. For each test person, model body descriptions of 21 body elements are available, each of which is represented in three-dimensional time series. For optimization, the MoCap data are first compared using time warp edit distance (TWED) and clustered using an agglomerative hierarchical procedure. Finally, the data of the resulting clusters are used to generate new averaged motion sequences using the HMM approach. The resulting averaged data can be used, for example, in a simulation in a multilevel biomechanical model.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"24 1","pages":"1323-1327"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83660867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287611
Yonggang Hu, T. Abhayapala, P. Samarasinghe, S. Gannot
Traditional source direction-of-arrival (DOA) estimation algorithms generally localize the elevation and azimuth simultaneously, requiring an exhaustive search over the two-dimensional (2-D) space. By contrast, this paper presents two decoupled source DOA estimation algorithms using a recently introduced source feature called the relative harmonic coefficients. They are capable to recover the source's elevation and azimuth separately, since the elevation and azimuth components in the relative harmonic coefficients are decoupled. The proposed algorithms are highlighted by a large reduction of computational complexity, thus enable a direct application for sound source tracking. Simulation results, using both a static and moving sound source, confirm the proposed methods are computationally efficient while achieving competitive localization accuracy.
{"title":"Decoupled Direction-of-Arrival Estimations Using Relative Harmonic Coefficients","authors":"Yonggang Hu, T. Abhayapala, P. Samarasinghe, S. Gannot","doi":"10.23919/Eusipco47968.2020.9287611","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287611","url":null,"abstract":"Traditional source direction-of-arrival (DOA) estimation algorithms generally localize the elevation and azimuth simultaneously, requiring an exhaustive search over the two-dimensional (2-D) space. By contrast, this paper presents two decoupled source DOA estimation algorithms using a recently introduced source feature called the relative harmonic coefficients. They are capable to recover the source's elevation and azimuth separately, since the elevation and azimuth components in the relative harmonic coefficients are decoupled. The proposed algorithms are highlighted by a large reduction of computational complexity, thus enable a direct application for sound source tracking. Simulation results, using both a static and moving sound source, confirm the proposed methods are computationally efficient while achieving competitive localization accuracy.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"29 1","pages":"246-250"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91117978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287564
E. Palma, F. Battisti, M. Carli, P. Astola, I. Tabus
This contribution presents the subjective evaluation of the compressed light field datasets obtained with four state-of- the-art codecs: two from the JPEG Pleno Light Field Verification Model and two recent methods for which codecs are publicly available. To the best of our knowledge, currently no subjective testing has been carried out to compare the performances of the four considered codecs. The evaluation methodology is based on Bradley-Terry scores, obtained from pairwise comparisons of the four codecs at four target bit-rates, for four light field datasets. The subset of pairs for which the comparisons are performed is selected according to the square design method, under two design variants, resulting in two datasets of subjective results. The analysis of the collected data, obtained by ranking the subjective scores of the codecs at various bitrates, shows high correlation with the available objective quality metrics.
{"title":"Subjective Quality Evaluation of Light Field Data Under Coding Distortions","authors":"E. Palma, F. Battisti, M. Carli, P. Astola, I. Tabus","doi":"10.23919/Eusipco47968.2020.9287564","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287564","url":null,"abstract":"This contribution presents the subjective evaluation of the compressed light field datasets obtained with four state-of- the-art codecs: two from the JPEG Pleno Light Field Verification Model and two recent methods for which codecs are publicly available. To the best of our knowledge, currently no subjective testing has been carried out to compare the performances of the four considered codecs. The evaluation methodology is based on Bradley-Terry scores, obtained from pairwise comparisons of the four codecs at four target bit-rates, for four light field datasets. The subset of pairs for which the comparisons are performed is selected according to the square design method, under two design variants, resulting in two datasets of subjective results. The analysis of the collected data, obtained by ranking the subjective scores of the codecs at various bitrates, shows high correlation with the available objective quality metrics.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"29 1","pages":"526-530"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91244346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dementia is a chronic syndrome characterized by deteriorating cognitive functions, thereby impacting the person’s daily life. It is often confused with decline in normal behavior due to natural aging and hence is hard to diagnose. Although, prior research has shown that dementia affects the subject’s speech, but it is not studied which frequency bands are being affected, and up to what extent, that in turn might influence identifying the different stages of dementia automatically. This work investigates the acoustic cues in different subsampled speech signals, to automatically differentiate Healthy Controls (HC) from stages of dementia such as Mild Cognitive Impairment (MCI) or Alzheimer’s Disease (AD). We use the Pitt corpus of DementiaBank database, to identify a set of features best suited for distinguishing between HC, MCI and AD speech, and achieve an F-score of 0.857 which is an absolute improvement of 2.8% over the state of the art.
{"title":"Dementia Classification using Acoustic Descriptors Derived from Subsampled Signals","authors":"Ayush Triapthi, Rupayan Chakraborty, Sunil Kumar Kopparapu","doi":"10.23919/Eusipco47968.2020.9287830","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287830","url":null,"abstract":"Dementia is a chronic syndrome characterized by deteriorating cognitive functions, thereby impacting the person’s daily life. It is often confused with decline in normal behavior due to natural aging and hence is hard to diagnose. Although, prior research has shown that dementia affects the subject’s speech, but it is not studied which frequency bands are being affected, and up to what extent, that in turn might influence identifying the different stages of dementia automatically. This work investigates the acoustic cues in different subsampled speech signals, to automatically differentiate Healthy Controls (HC) from stages of dementia such as Mild Cognitive Impairment (MCI) or Alzheimer’s Disease (AD). We use the Pitt corpus of DementiaBank database, to identify a set of features best suited for distinguishing between HC, MCI and AD speech, and achieve an F-score of 0.857 which is an absolute improvement of 2.8% over the state of the art.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"34 1","pages":"91-95"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89487864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287524
Arindam Ray, A. Khasnobish, Smriti Rani, A. Chowdhury, T. Chakravarty
Cardiopulmonary monitoring involves surveilling the important physiological parameters of an individual like the breathing rate (BR) and the heart rate (HR). This paper uses a simple, off-the-shelf dual multifrequency Continuous Wave (CW) radar setup to monitor the BR and HR of a static individual. The source separation problem of extracting the HR signal in presence of a higher amplitude BR signal poses a huge challenge and has been effectively solved by using an optimal channel selection process and the Variational Mode Decomposition (VMD) algorithm in this paper. Frequency extraction from the nonstationary signal modes produced by VMD has been performed by using the Fourier-Bessel transform to extract precise frequency information. Results show that the proposed system is accurate and outperforms other existing mode decomposition methods like Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) with a mean absolute error of 5.1±5.4 with respect to the number of heartbeats per minute and an accuracy of 95.87%(±4.9) with respect to the number of breaths per minute.
{"title":"Exploration of Mode Decomposition for Concurrent Cardiopulmonary Monitoring using Dual Radar","authors":"Arindam Ray, A. Khasnobish, Smriti Rani, A. Chowdhury, T. Chakravarty","doi":"10.23919/Eusipco47968.2020.9287524","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287524","url":null,"abstract":"Cardiopulmonary monitoring involves surveilling the important physiological parameters of an individual like the breathing rate (BR) and the heart rate (HR). This paper uses a simple, off-the-shelf dual multifrequency Continuous Wave (CW) radar setup to monitor the BR and HR of a static individual. The source separation problem of extracting the HR signal in presence of a higher amplitude BR signal poses a huge challenge and has been effectively solved by using an optimal channel selection process and the Variational Mode Decomposition (VMD) algorithm in this paper. Frequency extraction from the nonstationary signal modes produced by VMD has been performed by using the Fourier-Bessel transform to extract precise frequency information. Results show that the proposed system is accurate and outperforms other existing mode decomposition methods like Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) with a mean absolute error of 5.1±5.4 with respect to the number of heartbeats per minute and an accuracy of 95.87%(±4.9) with respect to the number of breaths per minute.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"1140-1144"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90817415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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