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@misc{SameniSamieinasabDataset,
doi = {10.13026/42EG-8E59},
url = {https://physionet.org/content/sufhsdb/1.0.1/},
author = {Sameni, Reza and Samieinasab, Maryam},
title = {{Shiraz University Fetal Heart Sounds Database}},
publisher = {PhysioNet},
year = {2021}
}
@misc{EPHNOGRAMDataset,
doi = {10.13026/tjtq-5911},
url = {https://physionet.org/content/ephnogram/1.0.0/},
author = {Arsalan Kazemnejad and Peiman Gordany and Reza Sameni},
title = {{EPHNOGRAM: A Simultaneous Electrocardiogram and Phonocardiogram Database}},
publisher = {PhysioNet},
year = {2021}
}
@inproceedings{Sameni2021Hypothesis,
url = {https://doi.org/10.1109/ssp49050.2021.9513811},
year = {2021},
month = jul,
publisher = {{IEEE}},
author = {Reza Sameni and Christian Jutten},
title = {{A Hypothesis Testing Approach to Nonstationary Source Separation}},
booktitle = {2021 {IEEE} Statistical Signal Processing Workshop ({SSP}), Rio de Janeiro, Brazil}
}
@inproceedings{Amini08,
title = {{MR Artifact Reduction in the Simultaneous Acquisition of EEG and fMRI of Epileptic Patients}},
author = {L. Amini and R. Sameni and C. Jutten and G.A. Hossein-Zadeh and H. Soltanian-Zadeh},
booktitle = {{EUSIPCO2008 - 16th European Signal Processing Conf.}},
year = {2008},
address = {Lausanne, Switzerland},
month = {August 25-29},
owner = {sameni},
timestamp = {2008.04.22}
}
@conference{BeharAJOG2013,
title = {{Evaluation of the fetal QT interval using non-invasive fetal ECG technology}},
author = {Joachim Behar and Adam Wolfberg and Tingting Zhu and Julian Oster and Alisa Niksch and Douglas Mah and Terrence Chun and James Greenberg and Cassandre Tanner and Jessica Harrop and Alexander Van Esbroeck and Amy Alexander and Michele McCarroll and Timothy Drake and Angela Silber and Reza Sameni and Jay Ward and Gari Clifford},
booktitle = {{American Journal of Obstetrics and Gynecology}},
year = {2014},
address = {New Orleans, LA},
month = {February},
organization = {{Society for Maternal-Fetal Medicine}},
pages = {S283--S284},
volume = {210},
url = {https://doi.org/10.1016/j.ajog.2013.10.609}
}
@article{Behar2016Evaluation,
title = {{Evaluation of the fetal QT interval using non-invasive fetal ECG technology}},
author = {Joachim Behar and Tingting Zhu and Julien Oster and Alisa Niksch and Douglas Y Mah and Terrence Chun and James
Greenberg and Cassandre Tanner and Jessica Harrop and Reza Sameni and Jay Ward and Adam J Wolfberg and Gari D Clifford},
journal = {Physiological Measurement},
year = {2016},
month = {September},
number = {9},
pages = {1392--1403},
volume = {37},
abstract = {Non-invasive fetal electrocardiography (NI-FECG) is a promising alternative continuous fetal monitoring method that has the potential to allow morphological analysis of the FECG. However, there are a number of challenges associated with the evaluation of morphological parameters from the NI-FECG, including low signal to noise ratio of the NI-FECG and methodological challenges for getting reference annotations and evaluating the accuracy of segmentation algorithms. This work aims to validate the measurement of the fetal QT interval in term laboring women using a NI-FECG electrocardiogram monitor. Fetal electrocardiogram data were recorded from 22 laboring women at term using the NI-FECG and an invasive fetal scalp electrode simultaneously. A total of 105 one-minute epochs were selected for analysis. Three pediatric electrophysiologists independently annotated individual waveforms and averaged waveforms from each epoch. The intervals measured on the averaged cycles taken from the NI-FECG and the fetal scalp electrode showed a close agreement; the root mean square error between all corresponding averaged NI-FECG and fetal scalp electrode beats was 13.6ms, which is lower than the lowest adult root mean square error of 16.1?ms observed in related adult QT studies. These results provide evidence that NI-FECG technology enables accurate extraction of the fetal QT interval.},
url = {https://doi.org/10.1088/0967-3334/37/9/1392}
}
@article{BiglariSameni2016,
title = {Fetal motion estimation from noninvasive cardiac signal recordings},
author = {Hadis Biglari and Reza Sameni},
journal = {Physiological Measurement},
year = {2016},
month = {November},
number = {11},
pages = {2003--2023},
volume = {37},
abstract = {Fetal motility is a widely accepted indicator of the well-being of a fetus. In previous research, it has be shown that fetal motion (FM) is coherent with fetal heart rate accelerations and an indicator for active/rest cycles of the fetus. The most common approach for FM and fetal heart rate (FHR) assessment is by Doppler ultrasound (DUS). While DUS is the most common approach for studying the mechanical activities of the heart, noninvasive fetal electrocardiogram (ECG) and magnetocardiogram (MCG) recording and processing techniques have been considered as a possible competitor (or complement) for the DUS. In this study, a fully automatic and robust framework is proposed for the extraction, ranking and alignment of fetal QRS-complexes from noninvasive fetal ECG/MCG. Using notions from subspace tracking, two measures, namely the actogram and rotatogram , are defined for fetal motion tracking. The method is applied to four fetal ECG/MCG databases, including twin MCG recordings. By defining a novel measure of causality, it is shown that there is significant coherency and causal relationship between the actogram/rotatogram and FHR accelerations/decelerations. Using this measure, it is shown that in many cases, the actogram and rotatogram precede the FHR variations, which supports the idea of motion-induced FHR accelerations/decelerations for these cases and raises attention for the non-motion-induced FHR variations, which can be associated to the fetal central nervous system developments. The results of this study can lead to novel perspectives of the fetal sympathetic and parasympathetic brain systems and future requirements of fetal cardiac monitoring.},
url = {https://doi.org/10.1088/0967-3334/37/11/2003}
}
@article{CNS2010,
title = {{An Artificial Vector Model for Generating Abnormal Electrocardiographic Rhythms}},
author = {G.D. Clifford and S. Nemati and R. Sameni},
journal = {{Physiological Measurements}},
year = {2010},
month = {May},
number = {5},
pages = {595--609},
volume = {31},
owner = {sameni},
timestamp = {2010.03.17},
url = {https://dx.doi.org/10.1088/0967-3334/31/5/001}
}
@inproceedings{CliffordSameni2008,
title = {{An Artificial Multi-Channel Model for Generating Abnormal Electrocardiographic Rhythms}},
author = {G.D. Clifford and S. Nemati and R. Sameni},
booktitle = {Computers in Cardiology, 2008},
year = {2008},
address = {Bologna, Italy},
month = {September 14--17},
pages = {773--776},
abstract = {We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat are incorporated as before from varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time-and frequency-domain heart rate (HR) and heart rate variability (HRV) characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last N RR intervals). Morphology changes due to respiration are simulated by coupling the RR interval to the angular frequency of the dipole. We demonstrate an example of the use of this model by simulating T-Wave Alternans (TWA). The magnitude of the TWA effect is modeled as a disturbance on the T-loop of the dipole with a magnitude that differs in each of the three VCG planes. The effect is then turned on or off using a HMM. The values of the transition matrix are determined by the local heart rate, such that when the HR ramps up towards 100 BPM, the probability of a TWA effect rapidly but smoothly increases. In this way, no 'sudden' switching from non-TWA to TWA is observed, and the natural tendency for TWA to be associated with a critical HR-related activation level is simulated. Finally, to generate multi-lead signals, the VCG is mapped to any set of clinical leads using a Dower-like transform derived from a least-squares optimization between known VCGs and known lead morphologies. ECGs with calibrated amounts of TWA were generated by this model and included in the PhysioNet/CinC Challenge 2008 data set.}
}
@article{AJOG2011,
title = {{Clinically accurate fetal ECG parameters acquired from maternal abdominal sensors}},
author = {Gari Clifford and Reza Sameni and Jay Ward and Julian Robinson and Adam John Wolfberg},
journal = {{American Journal of Obstetrics and Gynecology}},
year = {2011},
month = {July},
number = {1},
pages = {47.e1--47.e5},
volume = {205},
owner = {sameni},
timestamp = {2011.02.23},
url = {https://doi.org/10.1016/j.ajog.2011.02.066}
}
@conference{CliffordAJOG2009,
title = {{Comparing the fetal ST-segment acquired using a FSE and abdominal sensors}},
author = {Gari D. Clifford and Reza Sameni and Jay Ward and Jim Robertson and Courtenay Pettigrew and Adam J. Wolfberg},
booktitle = {{American Journal of Obstetrics and Gynecology}},
year = {2009},
address = {Chicago, IL},
month = {December},
organization = {{Society for Maternal-Fetal Medicine}},
pages = {S242--S242},
volume = {201},
owner = {sameni},
timestamp = {2011.02.09},
url = {http://dx.doi.org/10.1016/j.ajog.2009.10.535}
}
@inproceedings{CongJSG08,
title = {{A new General Weighted Least-Squares Algorithm for Approximate Joint Diagonalization}},
author = {M. Congedo and C. Jutten and R. Sameni and C. Gouy-Pailler},
booktitle = {Proceedings of the 4th International BCI Workshop},
year = {2008},
address = {Graz, Austria},
owner = {Cedric Gouy-Pailler},
timestamp = {2008.06.25}
}
@inproceedings{FatemiCINC2013,
title = {{A Robust Framework for Noninvasive Extraction of Fetal Electrocardiogram Signals}},
author = {Marzieh Fatemi and Mohammad Niknazar and Reza Sameni},
booktitle = {Proceedings of the 40th Annual International Conference on Computers in Cardiology},
year = {2013},
address = {Zaragoza, Spain},
month = {September 22-25},
pages = {201--204},
vol = {40}
}
@article{Fatemi2017,
title = {{An Online Subspace Denoising Algorithm for Maternal ECG Removal from Fetal ECG Signals}},
author = {Marzieh Fatemi and Reza Sameni},
journal = {Iranian Journal of Science and Technology, Transactions of Electrical Engineering},
year = {2017},
month = {April},
pages = {1--15},
volume = {2017},
owner = {sameni},
timestamp = {2012.10.21},
url = {http://dx.doi.org/10.1007/s40998-017-0018-4}
}
@inproceedings{FatemiSameni2013,
title = {{Application of second and higher order subspace tracking in multichannel data analysis}},
author = {Fatemi, M. and Sameni, R.},
booktitle = {Biomedical Engineering (ICBME), 2013 20th Iranian Conference on},
year = {2013},
month = {Dec},
pages = {161-165},
abstract = {The problem of blind source separation (BSS) and tracking from time-varying mixtures is an open-problem of biomedical signal processing research. In this study we present a framework for decomposing and tracking instantaneous separation matrices of independent component analysis (ICA) solutions of BSS. The decomposition is based on the tracking of the second order statistics (SOS) and higher order statistic (HOS) stages of ICA. We investigate the variations of data subspaces by means of tracking the principal angle and Givens rotation angles of the instantaneous mixture. The application of this technique is illustrated for electrocardiogram signals. We shown how the SOS and HOS variations of time-varying mixtures can be decoupled and may correspond to the second and higher order properties of the data. This new approach is believed to have various advantages for online subspace tracking in blind and semi-blind scenarios and the better examination of statistic characteristics of multichannel data, especially for biosignal processing.},
keywords = {blind source separation;electrocardiography;independent component analysis;medical signal processing;statistics;BSS;Givens rotation angles;ICA;biomedical signal processing;blind scenarios;blind source separation;electrocardiogram signals;higher order statistic;higher order subspace tracking;independent component analysis;instantaneous separation matrices;multichannel data analysis;online subspace tracking;principal angle;second order statistics;second order subspace tracking;semiblind scenarios;time-varying mixtures;Biomedical engineering;Educational institutions;Eigenvalues and eigenfunctions;Electrocardiography;Matrix decomposition;Source separation;Vectors},
url = {http://dx.doi.org/10.1109/ICBME.2013.6782211}
}
@article{Fattahi2022CramerRao,
url = {https://doi.org/10.1109/tsp.2022.3182113},
year = {2022},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
pages = {1--12},
author = {Davood Fattahi and Reza Sameni},
title = {Cram{\'{e}}r-Rao Lower Bounds of Model-Based Electrocardiogram Parameter Estimation},
journal = {{IEEE} Transactions on Signal Processing}
}
@article{Fattahi2022HeartSound,
url = {https://doi.org/10.1109/access.2022.3170052},
year = {2022},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {10},
pages = {50715--50727},
author = {Davood Fattahi and Reza Sameni and Ethan Grooby and Kenneth Tan and Lindsay Zhou and Arrabella King and Ashwin Ramanathan and Atul Malhotra and Faezeh Marzbanrad},
title = {A Blind Filtering Framework for Noisy Neonatal Chest Sounds},
journal = {{IEEE} Access}
}
@inproceedings{GouyPailler09,
title = {{Iterative Subspace Decomposition for Ocular Artifact Removal from EEG Recordings}},
author = {C. Gouy-Pailler and R. Sameni and M. Congedo and C. Jutten},
booktitle = {Proc. of the 8th Intl. Conf. on Independent Component (ICA 2009)},
year = {2009},
address = {Paraty, Brazil},
pages = {419--426},
owner = {sameni},
timestamp = {2008.04.22},
url = {https://link.springer.com/chapter/10.1007/978-3-642-00599-2_53}
}
@inproceedings{haghpanahi2014scoring,
title = {{Scoring consensus of multiple ECG annotators by optimal sequence alignment}},
author = {Haghpanahi, Masoumeh and Sameni, Reza and Borkholder, David A},
booktitle = {Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE},
year = {2014},
organization = {IEEE},
pages = {1855--1859},
url = {https://doi.org/10.1109/EMBC.2014.6943971}
}
@inproceedings{DolzhikovaIrina2021,
title = {{An Ensemble CNN for Subject-Independent Classification of Motor Imagery-Based EEG}},
author = {Irina Dolzhikova and Berdakh Abibullaev and Reza Sameni and Amin Zollanvari},
booktitle = {Engineering in Medicine and Biology Society (EMBC), 43rd Annual International Conference of the IEEE, Guadalajara, Mexico, Nov 1--5 2021},
year = {2021},
organization = {IEEE},
pages = {1--6}
}
@inproceedings{Hassani2012,
title = {{Using matched filters for similarity search in genomic data}},
author = {Hassani Saadi, Hamed and Sameni, Reza},
booktitle = {{Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)}},
year = {2012},
address = {Shiraz, Iran},
month = {2-3 May 2012},
pages = {469--472},
owner = {sameni},
timestamp = {2012.10.23},
url = {https://doi.org/10.1109/AISP.2012.6313793}
}
@article{HassaniSaadi2017,
title = {Interpretive time-frequency analysis of genomic sequences},
author = {Hassani Saadi, Hamed and Sameni, Reza and Zollanvari, Amin},
journal = {BMC Bioinformatics},
year = {2017},
number = {4},
pages = {154},
volume = {18},
abstract = {Time-Frequency (TF) analysis has been extensively used for the analysis of non-stationary numeric signals in the past decade. At the same time, recent studies have statistically confirmed the non-stationarity of genomic non-numeric sequences and suggested the use of non-stationary analysis for these sequences. The conventional approach to analyze non-numeric genomic sequences using techniques specific to numerical data is to convert non-numerical data into numerical values in some way and then apply time or transform domain signal processing algorithms. Nevertheless, this approach raises questions regarding the relative magnitudes under numeric transforms, which can potentially lead to spurious patterns or misinterpretation of results.},
issn = {1471-2105},
url = {http://dx.doi.org/10.1186/s12859-017-1524-0}
}
@inproceedings{JSH06,
title = {{On the Relevance of Independent Components}},
author = {C. Jutten and R. Sameni and H. Hauksd\'{o}ttir},
booktitle = {Proc. of the ICA Research Network International Workshop (ICArn 2006)},
year = {2006},
address = {Liverpool, UK},
month = {September 18-19},
pages = {1--8}
}
@article{KarimzadehEtal2017,
title = {A Distributed Classification Procedure for Automatic Sleep Stage Scoring Based on Instantaneous Electroencephalogram Phase and Envelope Features},
author = {F. Karimzadeh and R. Boostani and E. Seraj and R. Sameni},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
year = {2018},
month = {Feb},
number = {2},
pages = {362-370},
volume = {26},
issn = {1534-4320},
keywords = {Electroencephalography;Entropy;Estimation;Feature extraction;Indexes;Sleep;Standards;Automatic sleep stage scoring;EEG Analytic form;EEG signal;distributed classifier;entropy;instantaneous envelope;instantaneous phase},
url = {https://doi.org/10.1109/TNSRE.2017.2775058}
}
@inproceedings{Kharabian2009,
title = {{Fetal R-wave detection from multichannel abdominal ECG recordings in low SNR}},
author = {Kharabian, S. and Shamsollahi, M.B. and Sameni, R.},
booktitle = {Proc. of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009)},
year = {2009},
address = {Minneapolis, Minnesota, USA},
month = {Sep.},
pages = {344--347},
doi = {10.1109/IEMBS.2009.5333578},
issn = {1557-170X},
journal = {Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE},
keywords = {Hilbert transform;ICA;SNR;fetal ECG extraction algorithm;fetal R-wave detection;fetal repositioning;independent component analysis;maternal ECG cancellation;multichannel abdominal ECG recordings;signal-to-noise ratio;source separation;Hilbert transforms;electrocardiography;feature extraction;independent component analysis;medical signal detection;medical signal processing;obstetrics;source separation;}
}
@article{LiuEtAl2016,
title = {An open access database for the evaluation of heart sound algorithms},
author = {Chengyu Liu and David Springer and Qiao Li and Benjamin Moody and Ricardo Abad Juan and Francisco J Chorro and Francisco
Castells and Jos\'e Millet Roig and Ikaro Silva and Alistair E W Johnson and Zeeshan Syed and Samuel E Schmidt and Chrysa D
Papadaniil and Leontios Hadjileontiadis and Hosein Naseri and Ali Moukadem and Alain Dieterlen and Christian Brandt and Hong
Tang and Maryam Samieinasab and Mohammad Reza Samieinasab and Reza Sameni and Roger G Mark and Gari D Clifford},
journal = {Physiological Measurement},
year = {2016},
number = {12},
pages = {2181--2213},
volume = {37},
abstract = {In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.},
url = {https://doi.org/10.1088/0967-3334/37/12/2181}
}
@conference{McDonnellAJOG2011,
title = {{Comparison of abdominal sensors to a fetal scalp electrode for fetal ST analysis during labor}},
author = {Caitlin McDonnell and Gari Clifford and Reza Sameni and Jay Ward and Jim Robertson and Adam Wolfberg},
booktitle = {{American Journal of Obstetrics and Gynecology}},
year = {2011},
month = {January},
organization = {{Society for Maternal-Fetal Medicine}},
pages = {S256--S256},
volume = {204},
owner = {sameni},
timestamp = {2011.02.09},
url = {http://dx.doi.org/10.1016/j.ajog.2010.10.669}
}
@article{Moraru2011,
title = {{Validation of fetal auditory evoked cortical responses to enhance the assessment of early brain development using fetal MEG measurements}},
author = {Liviu Moraru and Reza Sameni and Uwe Schneider and Jens Haueisen and Ekkehard Schleu{\ss}ner and Dirk Hoyer},
journal = {Physiological Measurements},
year = {2011},
month = {October},
number = {11},
pages = {1847--1868},
volume = {32},
abstract = {The maturation of fetal auditory evoked cortical responses (fAECRs) is an important aspect of developmental medicine, but their reliable identification is limited due to the technical restrictions in prenatal diagnosis. The signal-to-noise ratio of the fAECRs extracted exclusively from fetal magnetoencephalography is a known issue which limits their analysis as markers of brain development. The objective of this work was to develop a signal analysis strategy to address these problems and find appropriate processing steps. In this study, a group of 147 normal fetuses with gestations between 26 and 41 weeks underwent auditory evoked response testing. We combine different approaches that address data cleaning, fAECR determination and statistical fAECR validation to reduce the uncertainty in the detection of the auditory evoked responses. For the statistical validation of the evoked responses, we use parameters computed from bootstrap-based test statistics and the correlation between different averaging modes. Appropriate thresholds for those parameters are identified using linear regression analyses by looking at the maximum correlation coefficients. The results show that by using different validation parameters, the selected fAECRs conduct to similar regression slopes with an average of -13.6 ms/week gestational age which agree with previous studies. Our novel processing framework provides an objective way to identify and eliminate non-physiological variation in the data induced by artifacts. This approach has the potential to produce more reliable data needed in clinical studies for fetal brain maturation as well as extending the investigations to high-risk groups.},
owner = {sameni},
timestamp = {2011.10.01},
url = {http://dx.doi.org/10.1088/0967-3334/32/11/002}
}
@inproceedings{Moraru08,
title = {Identification of fetal auditory evoked cortical responses using a denoising method based on periodic component analysis},
author = {L. Moraru and R. Sameni and U. Schneider and C. Jutten and J. Haueisen and D. Hoyer},
booktitle = {Proceedings of the 4th European Conference of the International Federation for Medical and Biological Engineering (ECIFMBE 2008)},
year = {2008},
address = {Antwerp, Belgium},
pages = {1390--1393},
mounth = {23--27 November},
url = {https://link.springer.com/chapter/10.1007/978-3-540-89208-3_329}
}
@inproceedings{Moraru2010,
title = {Identification of fetal auditory evoked responses from biomagnetic measurements},
author = {L. Moraru and U. Schneider and R. Sameni and D. Hoyer},
booktitle = {Proceedings of the 44th Annual Conference of the German Society of biomedical engineering},
year = {2010},
address = {Rostock, Germany},
month = {October},
owner = {sameni},
timestamp = {2015.01.13}
}
@inproceedings{NarimaniSameni2015,
title = {Electrocardiogram Denoising Using H-Infinity Filters},
author = {Narimani, H. and Sameni, R.},
booktitle = {Electrical Engineering (ICEE), 2015 23rd Iranian Conference on},
year = {2015},
month = {May},
note = {In Persian}
}
@article{Sulas2021,
author = {Eleonora Sulas and Monica Urru and Roberto Tumbarello and Luigi Raffo and Reza Sameni and Danilo Pani},
journal = {Scientific Data},
title = {{A non-invasive multimodal foetal ECG-Doppler dataset for antenatal cardiology research}},
year = {2021},
month = {jan},
number = {1},
volume = {8},
url = {https://doi.org/10.1038/s41597-021-00811-3},
publisher = {Springer Science and Business Media {LLC}}
}
@article{nikahd2016high,
title = {High-Speed Hardware Implementation of Fixed and Runtime Variable Window Length 1-D Median Filters},
author = {Nikahd, Eesa and Behnam, Payman and Sameni, Reza},
journal = {IEEE Transactions on Circuits and Systems II: Express Briefs},
year = {2016},
number = {5},
pages = {478--482},
volume = {63},
publisher = {IEEE},
url = {https://doi.org/10.1109/TCSII.2015.2504945}
}
@misc{NInFEADataset,
doi = {10.13026/C4N5-3B04},
url = {https://physionet.org/content/ninfea/1.0.0/},
author = {Pani, Danilo and Sulas, Eleonora and Urru, Monica and Sameni, Reza and Raffo, Luigi and Tumbarello, Roberto},
title = {{NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research}},
publisher = {PhysioNet},
year = {2020}
}
@article{RahbarAlam2020,
author = {Rahbar Alam, Mahdi and Sameni, Reza},
title = {Automatic Wake-Sleep Stages Classification using Electroencephalogram Instantaneous Frequency and Envelope Tracking},
elocation-id = {2020.05.13.092841},
year = {2020},
url = {http://dx.doi.org/10.1101/2020.05.13.092841},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Background The study of cerebral activity during sleep using the electroencephalograph (EEG) is a major research field in neuroscience. Despite the rich literature in this field, the automatic and accurate categorization of wake-sleep stages remains an open problem.New Method A robust model-based Kalman filtering scheme is proposed for tracking the poles of a second order time-varying autoregressive model fitted over the EEG acquired during different wake/sleep stages. The pole angle/phase is regarded as the dominant frequency of the EEG spectrum (known as the instantaneous frequency in literature). The frequency resolution is improved by splitting the wide frequency band to subbands corresponding to well-known brain rhythms. Using recent findings in field of EEG phase/frequency tracking, the instantaneous envelope of the narrow-band signal{\textquoteright}s analytic form is also tracked as a complementary feature.Results The minimal set of instantaneous frequency and envelope features is employed in three classification schemes, using training labels from R\&k and AASM sleep scoring standards. The LDA classifier resulted in the highest performance using the proposed feature set.Comparison with Existing Methods The proposed method resulted in a higher mean decoding accuracy and a lower standard deviation on the entire dataset, as compared with state-of-the-art techniques.Conclusions The accurate tracking of the instantaneous frequency and envelope are highly informative for sleep stage scoring. The proposed method is shown to have additional applications, including the prediction of wake-sleep transition, which can be used for drowsiness detection from the EEG.Competing Interest StatementThe authors have declared no competing interest.},
journal = {bioRxiv}
}
@article{KazemnejadGordanySameni2021,
author = {Kazemnejad, Arsalan and Gordany, Peiman and Sameni, Reza},
title = {An Open-Access Simultaneous Electrocardiogram and Phonocardiogram Database},
elocation-id = {2021.05.17.444563},
year = {2021},
url = {https://doi.org/10.1101/2021.05.17.444563},
publisher = {Cold Spring Harbor Laboratory},
journal = {bioRxiv}
}
@inproceedings{RazavipourCINC2013,
title = {{Fetal QRS Complex Detection using Semi-Blind Source Separation Framework}},
author = {Fatemeh Razavipour and Masoumeh Haghpanahi and Reza Sameni},
booktitle = {Proceedings of the 40th Annual International Conference on Computers in Cardiology},
year = {2013},
address = {Zaragoza, Spain},
month = {September 22-25},
pages = {181--184},
vol = {40}
}
@article{RazavipourSameni2015,
title = {{A Study of Event Related Potential Frequency Domain Coherency using Multichannel Electroencephalogram Subspace Analysis}},
author = {Fatemeh Razavipour and Reza Sameni},
journal = {Journal of Neuroscience Methods},
year = {2015},
month = {July},
pages = {22--28},
volume = {249},
owner = {sameni},
url = {http://dx.doi.org/10.1016/j.jneumeth.2015.03.037}
}
@article{Razavipour2013,
title = {{A General Framework for Extracting Fetal Magnetoencephalogram and Audio-Evoked Responses}},
author = {Fatemeh Razavipour and Reza Sameni},
journal = {Journal of Neuroscience Methods},
year = {2013},
month = {January},
number = {2},
pages = {283--296},
volume = {212},
owner = {sameni},
timestamp = {2013.01.15},
url = {http://dx.doi.org/10.1016/j.jneumeth.2012.10.021}
}
@article{Kheirati20131453,
author = {Ebadollah Kheirati Roonizi and Reza Sameni},
journal = {Computers in Biology and Medicine},
title = {Morphological modeling of cardiac signals based on signal decomposition},
year = {2013},
issn = {0010-4825},
month = {October},
number = {10},
pages = {1453--1461},
volume = {43},
abstract = {Abstract In this paper a general framework is presented for morphological modeling of cardiac signals from a signal decomposition perspective. General properties of a desired morphological model are presented and special cases of the model are studied in detail. The presented approach is studied for modeling the morphology of electrocardiogram (ECG) signals. Specifically, three types of \{ECG\} modeling techniques, including polynomial spline models, sinusoidal model and a model previously presented by McSharry et al., are studied within this framework. The proposed method is applied to datasets from the PhysioNet \{ECG\} database for compression and modeling of normal and abnormal \{ECG\} signals. Quantitative and qualitative results of these applications are also presented and discussed.},
url = {http://dx.doi.org/10.1016/j.compbiomed.2013.06.017}
}
@manual{OSET3.14,
title = {{The Open-Source Electrophysiological Toolbox (OSET), version 3.14}},
author = {Reza Sameni},
year = {2018},
url = {http://www.oset.ir}
}
@article{SameniOnlineFiltering2016,
title = {Online filtering using piecewise smoothness priors: Application to normal and abnormal electrocardiogram denoising},
author = {Reza Sameni},
journal = {Signal Processing},
year = {2017},
month = {April},
number = {4},
pages = {52 - 63},
volume = {133},
abstract = {Abstract In this work, a block-wise extension of Tikhonov regularization is proposed for denoising smooth signals contaminated by wide-band noise. The proposed method is derived from a constrained least squares problem in two forms: 1) a block-wise fixed-lag smoother with smooth inter-block transitions applied in matrix form, and 2) a fixed-interval smoother applied as a forward-backward zero-phase filter. The filter response is maximally flat and monotonically decreasing, without any ripples in its pass-band. The method is also extended to smoothness of multiple smoothness orders, and its relationship with Lipschitz regularity and block-wise Wiener smoothing is also studied. The denoising of normal and abnormal electrocardiogram (ECG) signals in different stationary and non-stationary noise levels is studied as case study. While most ECG denoising techniques benefit from the pseudo-periodicity of the ECG, the developed technique is merely based on the smoothness assumption, which makes it a powerful method for both normal and abnormal ECG. The performance of the method is assessed by Monte-Carlo simulations over three standard normal and abnormal ECG databases of different sampling rates, in comparison with bandpass filtering, wavelet denoising with various parameters, and Savitzky-Golay filters using Stein's unbiased risk estimate shrinkage scheme.},
issn = {0165-1684},
keywords = {Tikhonov regularization, Forward-backward filtering, Electrocardiogram filtering, Wavelet denoising, Lipschitz regularity, Wiener smoothing},
url = {https://doi.org/10.1016/j.sigpro.2016.10.019}
}
@unpublished{sameni:hal-01382035,
title = {{Spatio-Temporal Source Separation using Temporal Priors with Parameterized Uncertainties}},
author = {Sameni, Reza},
note = {working paper or preprint},
month = oct,
year = {2016},
file = {Paper.pdf:https\://hal.archives-ouvertes.fr/hal-01382035/file/Paper.pdf:PDF},
hal_id = {hal-01382035},
hal_version = {v1},
url = {https://hal.archives-ouvertes.fr/hal-01382035}
}
@unpublished{sameni:hal-01382076,
title = {{Towards Distributed Component Analysis}},
author = {Sameni, Reza},
note = {working paper or preprint},
month = oct,
year = {2015},
file = {paper.pdf:https\://hal.archives-ouvertes.fr/hal-01382076/file/paper.pdf:PDF},
hal_id = {hal-01382076},
hal_version = {v1},
url = {https://hal.archives-ouvertes.fr/hal-01382076}
}
@manual{SameniResearchPool,
title = {My Open-Research Pool},
author = {Reza Sameni},
year = {2015},
url = {http://home.cse.shirazu.ac.ir/~sameni/research.html}
}
@inproceedings{Sameni2012,
title = {{A Linear Kalman Notch Filter for Power-Line Interference Cancellation}},
author = {Reza Sameni},
booktitle = {{Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)}},
year = {2012},
address = {Shiraz, Iran},
month = {2-3 May 2012},
pages = {604--610},
owner = {sameni},
timestamp = {2012.10.23},
url = {https://doi.org/10.1109/AISP.2012.6313817}
}
@inbook{Sameni2021,
author = {Sameni, Reza},
editor = {Pani, Danilo
and Rabotti, Chiara
and Signorini, Maria Gabriella
and Burattini, Laura},
title = {Noninvasive Fetal Electrocardiography: Models, Technologies, and Algorithms},
booktitle = {Innovative Technologies and Signal Processing in Perinatal Medicine: Volume 1},
year = {2021},
publisher = {Springer International Publishing},
address = {Cham},
pages = {99--146},
abstract = {The fetal electrocardiogram (fECG) was first recorded from the maternal abdominal surface in the early 1900s. During the past 50 years, the most advanced electronics technologies and signal processing algorithms have been used to convert noninvasive fetal electrocardiography into a reliable technology for fetal cardiac monitoring. In this chapter, the major signal processing techniques, which have been developed for the modeling, extraction, and analysis of the fECG from noninvasive maternal abdominal recordings, are reviewed and compared with one another in detail. The major topics of the chapter include (1) the electrophysiology of the fECG from the signal processing viewpoint, (2) the mathematical model of the maternal volume conduction media and the waveform models of the fECG acquired from body surface leads, (3) the signal acquisition requirements, (4) model-based techniques for fECG noise and interference cancellation, including adaptive filters and semi-blind source separation techniques, and (5) recent algorithmic advances for fetal motion tracking and online fECG extraction from few number of channels.},
isbn = {978-3-030-54403-4},
url = {https://doi.org/10.1007/978-3-030-54403-4\_5}
}
@manual{OSET2.1,
title = {{The Open-Source Electrophysiological Toolbox (OSET), version 2.1}},
author = {Reza Sameni},
year = {2010},
url = {http://www.oset.ir}
}
@manual{OSET,
title = {{Open-Source ECG Toolbox (OSET)}},
author = {R. Sameni},
note = {2nd version},
year = {2008},
url = {http://ecg.sharif.ir/}
}
@phdthesis{Sameni2008,
title = {{Extraction of Fetal Cardiac Signals from an Array of Maternal Abdominal Recordings}},
author = {Reza Sameni},
school = {Sharif University of Technology -- Institut National Polytechnique de Grenoble},
year = {2008},
month = {July},
owner = {sameni},
timestamp = {2008.05.08},
url = {http://www.sameni.info/Publications/Thesis/PhDThesis.pdf}
}
@unpublished{Sameni2007d,
title = {{Primary Results on Multichannel Fetal ECG Recordings}},
author = {R. Sameni},
note = {{Technical Report}},
month = {June},
year = {2007},
owner = {sameni},
timestamp = {2012.10.22}
}
@unpublished{Sameni2007e,
title = {{Multipole Expansion of Body Surface Potentials: An ICA Oriented Formulation (Part I)}},
author = {R. Sameni},
note = {{Technical Report}},
month = {November},
year = {2007},
owner = {sameni},
timestamp = {2012.10.22}
}
@techreport{Sameni2006,
title = {{Guidelines for writing efficient MATLAB codes}},
author = {Reza Sameni},
institution = {{Emory University}},
year = {2020},
url = {https://www.overleaf.com/read/dphnzzmnrjts},
owner = {sameni},
timestamp = {2010.03.17}
}
@unpublished{Sameni2006d,
title = {{Removing ECG Artifacts from EEG Recordings}},
author = {R. Sameni},
note = {{Technical Report}},
month = {May},
year = {2006},
owner = {sameni},
timestamp = {2012.10.22}
}
@unpublished{Sameni2006e,
title = {{Model-Based Multichannel ECG Filtering}},
author = {R. Sameni},
note = {{Technical Report}},
month = {December},
year = {2006},
owner = {sameni},
timestamp = {2012.10.22}
}
@article{Tomassini2020,
author = {Selene Tomassini and Agnese Sbrollini and Annachiara Strazza and Reza Sameni and Ilaria Marcantoni and Micaela Morettini and Laura Burattini},
journal = {Biomedical Signal Processing and Control},
title = {{AdvFPCG}-Delineator: Advanced delineator for fetal phonocardiography},
year = {2020},
month = {Aug},
pages = {102021},
volume = {61},
url = {https://doi.org/10.1016/j.bspc.2020.102021},
publisher = {Elsevier {BV}}
}
@unpublished{Sameni2006f,
title = {{Primary Results on Multichannel Magnetocardiograms}},
author = {R. Sameni},
note = {{Technical Report}},
month = {November},
year = {2006},
owner = {sameni},
timestamp = {2012.10.22}
}
@manual{Sameni2006g,
title = {{Open Source ECG Toolbox (OSET)}},
author = {R. Sameni},
year = {2006},
owner = {sameni},
timestamp = {2012.10.22},
url = {http://ecg.sharif.ir/}
}
@techreport{Sameni2004,
title = {{Discrimination of EEG Patterns during the Performance of Different Mental Activities}},
author = {Reza Sameni},
institution = {{Research Project Report, Sharif University of Technology}},
year = {2004},
owner = {sameni},
timestamp = {2010.03.17}
}
@techreport{SameniTermPaperDSPII,
title = {{Analysis of Iterative Approaches of Interpolation-Distortion Compensation}},
author = {R. Sameni},
institution = {GIPSA-LAB, INP-Grenoble},
year = {2004},
month = {March},
note = {{DSPII course term paper, Sharif University of Technology}}
}
@mastersthesis{Sameni2003,
title = {{Discrimination of EEG Signals during the Performance of Different Mental Tasks}},
author = {R. Sameni},
school = {Sharif University of Technology},
year = {2003},
owner = {sameni},
publisher = {M.Sc. dissertation, Sharif University of Technology},
timestamp = {2012.10.22}
}
@techreport{Sameni2001,
title = {{Design and Implementation of a Portable Hotwire Anemometer}},
author = {Reza Sameni},
institution = {{IROST}},
year = {2001},
month = {November},
owner = {sameni},
timestamp = {2010.03.17}
}
@article{SameniClifford2010,
title = {{A Review of Fetal ECG Signal Processing; Issues and Promising Directions}},
author = {Reza Sameni and Gari D. Clifford},
journal = {{The Open Pacing, Electrophysiology \& Therapy Journal (TOPETJ)}},
year = {2010},
month = {November},
pages = {4--20},
volume = {3},
url = {10.2174/1876536X01003010004},
owner = {sameni},
timestamp = {2010.04.10}
}
@article{SCJS06,
title = {{Multichannel ECG and Noise Modeling: Application to Maternal and Fetal ECG Signals}},
author = {R Sameni and G. D. Clifford and C. Jutten and M. B. Shamsollahi},
journal = {{EURASIP Journal on Advances in Signal Processing}},
year = {2007},
pages = {{Article ID 43407, 14 pages}},
volume = {2007},
url = {https://doi.org/10.1155/2007/43407}
}
@conference{SameniAJOG2009,
title = {{Accuracy of fetal heart rate acquired from sensors on the maternal abdomen compared to a fetal scalp electrode}},
author = {Reza Sameni and Gari D. Clifford and Jay Ward and Jim Robertson and Courtenay Pettigrew and Adam J. Wolfberg},
booktitle = {{American Journal of Obstetrics and Gynecology}},
year = {2009},
address = {Chicago, IL},
month = {December},
organization = {{Society for Maternal-Fetal Medicine}},
pages = {S241--S241},
volume = {201},
owner = {sameni},
timestamp = {2011.02.09},
url = {http://dx.doi.org/10.1016/j.ajog.2009.10.529}
}
@article{SameniGouyPailler2014,
author = {Reza Sameni and Cedric Gouy-Pailler},
journal = {Journal of Neuroscience Methods},
title = {{An Iterative Subspace Denoising Algorithm for Removing Electroencephalogram Ocular Artifacts}},
year = {2014},
month = {March},
number = {3},
pages = {97--105},
volume = {225},
address = {l},
booktitle = {Proc.},
owner = {sameni},
timestamp = {2014},
url = {http://dx.doi.org/10.1016/j.jneumeth.2014.01.024}
}
@inproceedings{Sameni2012b,
author = {R. Sameni and C. Gouy-Pailler},
booktitle = {Proc.},
title = {{An Iterative Subspace Denoising algorithm for Removing Electrocardiogram Ocular Artifact}},
year = {2012},
address = {l},
owner = {sameni},
timestamp = {2012}
}
@techreport{GouyPailler08,
title = {{Iterative Subspace Decomposition for Ocular Artifact Removal from EEG Recordings}},
author = {R. Sameni and C. Gouy-Pailler},
institution = {GIPSA-lab, INP-Grenoble},
year = {2008},
owner = {sameni},
timestamp = {2008.04.22}
}
@patent{sameni2015extraction,
title = {{Extraction of fetal cardiac signals}},
author = {Sameni, R. and Jutten, C. and Shamsollahi, M.B. and Clifford, G.D.},
month = sep # {~8},
note = {US Patent 9,125,577},
year = {2015},
publisher = {Google Patents},
url = {https://www.google.com/patents/US9125577}
}
@patent{patentSameni1_Published,
title = {{Extraction of Fetal Cardiac Signals}},
author = {R. Sameni and C. Jutten and M.B. Shamsollahi and G.D. Clifford},
month = {June},
year = {2010},
day = {3},
dayfiled = {3},
monthfiled = {June},
nationality = {U.S.},
number = {{US 2010/0137727 A1}},
owner = {sameni},
timestamp = {2009.09.12},
note = {Licensed to MindChild Medical Inc.},
yearfiled = {2010}
}
@article{Sameni2010,
title = {A deflation procedure for subspace decomposition},
author = {Sameni, Reza and Jutten, Christian and Shamsollahi, Mohammad B},
journal = {Signal Processing, IEEE Transactions on},
year = {2010},
number = {4},
pages = {2363--2374},
volume = {58},
owner = {sameni},
publisher = {IEEE},
timestamp = {2016.10.01},
url = {https://doi.org/10.1109/TSP.2009.2037353}
}
@article{Sameni2008a,
author = {R. Sameni and C. Jutten and M. B. Shamsollahi},
journal = {Biomedical Engineering, IEEE Transactions on},
title = {{Multichannel Electrocardiogram Decomposition using Periodic Component Analysis}},
year = {2008},
month = {Aug},
number = {8},
pages = {1935--1940},
volume = {55},
abstract = {In this letter, we propose the application of the generalized eigenvalue decomposition for the decomposition of multichannel electrocardiogram (ECG) recordings. The proposed method uses a modified version of a previously presented measure of periodicity and a phase-wrapping of the RR-interval, for extracting the ?most periodic? linear mixtures of a recorded dataset. It is shown that the method is an improved extension of conventional source separation techniques, specifically customized for ECG signals. The method is therefore of special interest for the decomposition and compression of multichannel ECG, and for the removal of maternal ECG artifacts from fetal ECG recordings.},
owner = {sameni},
timestamp = {2012.10.22},
url = {https://doi.org/10.1109/TBME.2008.919714}
}
@inproceedings{SJS06,
title = {{What ICA Provides for {ECG} Processing: Application to Noninvasive Fetal {ECG} Extraction}},
author = {R. Sameni and C. Jutten and M. B. Shamsollahi},
booktitle = {{Proc. of the International Symposium on Signal Processing and Information Technology (ISSPIT'06)}},
year = {2006},
address = {{Vancouver, Canada}},
month = {August},
pages = {656--661},
url = {https://doi.org/10.1109/ISSPIT.2006.270882}
}
@unpublished{Sameni00,
title = {{Designing a Training Board for the 8x51 Microcontroller Series}},
author = {Reza Sameni and Sharifoddin Mansouri},
note = {{Bachlor's final project, Shiraz University}},
month = {September},
year = {2000},
owner = {sameni},
timestamp = {2010.03.16}
}
@article{SameniSeraj2017,
title = {{A robust statistical framework for instantaneous electroencephalogram phase and frequency estimation and analysis}},
author = {Reza Sameni and Esmaeil Seraj},
journal = {Physiological Measurement},
year = {2017},
number = {12},
pages = {2141--2163},
volume = {38},
abstract = {Objective: The instantaneous phase (IP) and instantaneous frequency (IF) of the electroencephalogram (EEG) are considered as notable complements for the EEG spectrum. The calculation of these parameters commonly includes narrow-band filtering, followed by the calculation of the signal?s analytical form. The calculation of the IP and IF is highly susceptible to the filter parameters and background noise level, especially in low analytical signal amplitudes. The objective of this study is to propose a robust statistical framework for EEG IP/IF estimation and analysis. Approach: Herein, a Monte Carlo estimation scheme is proposed for the robust estimation of the EEG IP and IF. It is proposed that any EEG phase-related inference should be reported as an average with confidence intervals obtained by repeating the IP and IF estimation under infinitesimal variations (selected by an expert), in algorithmic parameters such as the filter?s bandwidth, center frequency and background noise level. In the second part of the paper, a stochastic model consisting of the superposition of narrow-band foreground and background EEG is used to derive analytically probability density functions of the instantaneous envelope (IE) and IP of EEG signals, which justify the proposed Monte Carlo scheme. Main results: The instantaneous analytical envelope of the EEG, which has been empirically used in previous studies, is shown to have a fundamental impact on the accuracy of the EEG phase contents. It is rigorously shown that the IP/IF estimation quality highly depends on the IE and any phase/frequency interpretations in low IE are statistically unreliable and require a hypothesis test. Significance: The impact of the proposed method on previous studies, including time-domain phase synchrony, phase resetting, phase locking value and phase amplitude coupling are studied with examples. The findings of this research can set forth new standards for EEG phase/frequency estimation and analysis techniques.},
url = {http://dx.doi.org/10.1088/1361-6579/aa93a1}
}
@inproceedings{SaSh03,
title = {{Discrimination of EEG Signals during the Performance of Different Mental Tasks}},
author = {R. Sameni and M.B Shamsollahi},
booktitle = {Proc. of the World Congress on Medical Physics and Biomedical Engineering},
year = {2003},
address = {Sydney, Australia},
month = {August 24-29},
note = {[CD-ROM] ISBN 1877040142, Poster Paper No. 4251}
}
@inproceedings{SSJ06,
title = {{Multi-Channel Electrocardiogram Denoising Using a Bayesian Filtering Framework}},
author = {R. Sameni and M.B Shamsollahi and C. Jutten},
booktitle = {Proc. of the 33rd Annual International Conference on Computers in Cardiology},
year = {2006},
address = {Valencia, Spain},
month = {September 17-20},
pages = {185--188},
url = {http://cinc.mit.edu/archives/2006/},
vol = {33}
}
@inproceedings{SSS04,
title = {{Processing Polysomnographic Signals, using Independent Component Analysis}},
author = {R. Sameni and M.B Shamsollahi and L. Senhadji},
booktitle = {Proc. Of the International Conference on Biomedical Engineering (BIOMED 2004)},
year = {2004},
address = {Innsbruck, Austria},
month = {February},
pages = {193-196}
}
@article{SSJ08,
title = {{Model-based Bayesian filtering of cardiac contaminants from biomedical recordings}},
author = {R. Sameni and M. B. Shamsollahi and C. Jutten},
journal = {Physiological Measurement},
year = {2008},
month = {May},
number = {5},
pages = {595--613},
volume = {29},
abstract = {Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.},
url = {https://doi.org/10.1088/0967-3334/29/5/006}
}
@inproceedings{SSJ05,
title = {{Filtering Electrocardiogram Signals Using the Extended Kalman Filter}},
author = {R. Sameni and M. B. Shamsollahi and C. Jutten},
booktitle = {Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)},
year = {2005},
address = {Shanghai, China},
month = {September 1-4},
pages = {5639--5642},
url = {https://doi.org/10.1109/IEMBS.2005.1615765}
}
@inproceedings{SSJB05,
title = {{Filtering Noisy {ECG} Signals Using the Extended {K}alman Filter Based on a Modified Dynamic {ECG} Model}},
author = {R. Sameni and M. B. Shamsollahi and C. Jutten and M. Babaie-Zadeh},
booktitle = {Proceedings of the 32nd Annual International Conference on Computers in Cardiology},
year = {2005},
address = {Lyon, France},
month = {September 25-28},
pages = {1017-1020},
vol = {32}
}
@article{SSJC06,
title = {A Nonlinear Bayesian Filtering Framework for {ECG} Denoising},
author = {R. Sameni and M. B. Shamsollahi and C. Jutten and G. D. Clifford},
journal = {Biomedical Engineering, IEEE Transactions on},
year = {2007},
month = {December},
number = {12},
pages = {2172--2185},
volume = {54},
no = {12},
url = {https://doi.org/10.1109/TBME.2007.897817},
vol = {54}
}
@inproceedings{SVPH06,
title = {{Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria}},
author = {R. Sameni and F. Vrins and F. Parmentier and C. H\'erail and V. Vigneron and M. Verleysen and C. Jutten and M.B Shamsollahi},
booktitle = {Proc. of the 26th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2006)},
year = {2006},
address = {CNRS, Paris, France},
month = {July 8-13},
pages = {97--104},
volume = {872},
url = {http://hdl.handle.net/2078.1/90753}
}
@manual{fetalPCGPhysionet,
title = {{Shiraz University Fetal Heart Sounds Database (SUFHSDB)}},
author = {Maryam Samieinasab and Reza Sameni},
year = {2016},
url = {https://physionet.org/physiobank/database/sufhsdb/}
}
@inproceedings{SamieinasabSameni2015,
title = {Fetal Phonocardiogram Extraction Using Single Channel Blind Source Separation},
author = {Samieinasab, M. and Sameni, R.},
booktitle = {Electrical Engineering (ICEE), 2015 23rd Iranian Conference on},
year = {2015},
month = {May},
url = {https://doi.org/10.1109/IranianCEE.2015.7146186}
}
@inproceedings{Sayadi2007,
title = {{ECG Denoising Using Parameters of ECG Dynamical Model as the States of an Extended Kalman Filter}},
author = {Sayadi, O. and Sameni, R. and Shamsollahi, M.B.},
booktitle = {Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE},
year = {2007},
month = {Aug.},
pages = {2548--2551},
abstract = {In this paper an efficient filtering procedure based on the extended Kalman filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. We have suggested simple dynamics as the governing equations for the model parameters. Since we have not any observation for these new state variables, they are considered as hidden states. Quantitative evaluation of the proposed algorithm on the MIT-BIH signals shows that an average SNR improvement of 12 dB is achieved for a signal of -5 dB. The results show improved output SNRs compared to the EKF outputs in the absence of these new dynamics.},
doi = {10.1109/IEMBS.2007.4352848},
issn = {1557-170X},
keywords = {Biomedical measurements;Electrocardiography;Equations;Filtering;Noise measurement;Noise reduction;Nonlinear dynamical systems;Nonlinear systems;Pollution measurement;Signal generators;Kalman filters;electrocardiography;medical signal processing;signal denoising;ECG denoising;ECG dynamical model;electrocardiogram;extended Kalman filter;hidden states;modified nonlinear dynamic model;ECG dynamical model;Extended Kalman filter;Hidden state variable;},
url = {https://doi.org/10.1109/IEMBS.2007.4352848}
}
@article{SerajSameni2017,
title = {Robust electroencephalogram phase estimation with applications in brain-computer interface systems},
author = {Esmaeil Seraj and Reza Sameni},
journal = {Physiological Measurement},
year = {2017},
number = {3},
pages = {501},
volume = {38},
abstract = {Objective: In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Based on recent theoretical findings in this area, it is shown that some of the phase variations?previously associated to the brain response?are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG. Approach: With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in the zero-pole loci of narrow-band filters, followed by phase estimation using the signal?s analytical form and ensemble averaging over the randomized ensembles to obtain a robust EEG phase and frequency. This Monte Carlo estimation method is shown to be very robust to noise and minor changes of the filter parameters and reduces the effect of fake EEG phase jumps, which do not have a cerebral origin. Main results: As proof of concept, the proposed method is used for extracting EEG phase features for a brain computer interface (BCI) application. The results show significant improvement in classification rates using rather simple phase-related features and a standard K-nearest neighbors and random forest classifiers, over a standard BCI dataset. Significance: The average performance was improved between 4?7% (in absence of additive noise) and 8?12% (in presence of additive noise). The significance of these improvements was statistically confirmed by a paired sample t-test , with 0.01 and 0.03 p-values, respectively. The proposed method for EEG phase calculation is very generic and may be applied to other EEG phase-based studies.},
url = {https://doi.org/10.1088/1361-6579/aa5bba}
}
@inproceedings{MoodyCINC2013,
title = {{Noninvasive Fetal ECG: The PhysioNet/Computing in Cardiology Challenge 2013}},
author = {Ikaro Silva and Joachim Behar and Reza Sameni and Tingting Zhu and Julien Oster and Gari D. Clifford and George B. Moody},
booktitle = {Proceedings of the 40th Annual International Conference on Computers in Cardiology},
year = {2013},
address = {Zaragoza, Spain},
month = {September 22-25},
pages = {149--152},
vol = {40}
}
@inproceedings{Tavakoli2006,
title = {{Audio Watermarking for Covert Communication through Telephone System}},
author = {Tavakoli, E. and Vahdat, B.V. and Shamsollahi, M.B. and Sameni, R.},
booktitle = {IEEE International Symposium on Signal Processing and Information Technology, 2006},
year = {2006},
month = {Aug.},
pages = {955--959},
doi = {10.1109/ISSPIT.2006.270935},
journal = {Signal Processing and Information Technology, 2006 IEEE International Symposium on}
}
@article{Tsalaile2008,
title = {{Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period}},
author = {Thato Tsalaile and Reza Sameni and Saeid~Sanei and Christian Jutten and Jonathon Chambers},
journal = {Biomedical Engineering, IEEE Transactions on},
year = {2009},
month = {March},
number = {3},
pages = {646--655},
volume = {56},
abstract = {A novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of a heart sound signal from real-world lung sound recordings. Separation results confirm the utility of the introduced approach, and listening tests are employed to further corroborate the results.},
url = {https://doi.org/10.1109/TBME.2008.2002141}
}
@conference{Vahabzadeh2012,
title = {{The Notion of Cardiac Phase and its Applications in Electrophysiological Studies}},
author = {Bahman Vahabzadeh and Reza Sameni},
booktitle = {Biomedical Engineering (BioMed 2012)},
year = {2012},
address = {Innsbruck, Austria},
month = {February 15--17},
abstract = {In this paper the cardiac phase is calculated using different methods based on time warping theory. The estimated phase is used for calculation of the heart rate (HR) signal. The results show that the estimated HR is similar to the HR calculated by using the RR-interval sequence. Unlike the RR-interval signal which is non-uniformly sampled, the proposed method for HR calculation is continuous in time. Therefore, conventional signal processing methods can be used to study the HR signal easily and without any preprocessing techniques like resampling and interpolations used in previous method.},
owner = {sameni},
timestamp = {2012.07.10},
url = {http://dx.doi.org/10.2316/P.2012.764-127}
}
@article{JamshidianSameni2018,
author = {Fahimeh Jamshidian-Tehrani and Reza Sameni},
title = {Fetal {ECG} extraction from time-varying and low-rank noninvasive maternal abdominal recordings},
journal = {Physiological Measurement},
year = {2018},
month = {Nov},
publisher = {{IOP} Publishing},
url = {http://dx.doi.org/10.1088/1361-6579/aaef5d}
}
@article{JamshidianSameniJutten2019,
author = {Fahimeh {Jamshidian-Tehrani} and Reza {Sameni} and Christian {Jutten}},
journal = {IEEE Transactions on Biomedical Engineering},
title = {{Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction}},
year = {2020},
volume = {67},
number = {5},
pages = {1377--1386},
url = {http://dx.doi.org/10.1109/TBME.2019.2936943}
}
@article{ZollanvariJamesSameni2019,
author = {Zollanvari, Amin
and James, Alex Pappachen
and Sameni, Reza},
title = {A Theoretical Analysis of the Peaking Phenomenon in Classification},
journal = {Journal of Classification},
year = {2019},
month = {Jul},
day = {11},
abstract = {In this work, we analytically study the peaking phenomenon in the context of linear discriminant analysis in the multivariate Gaussian model under the assumption of a common known covariance matrix. The focus is finite-sample setting where the sample size and observation dimension are comparable. Therefore, in order to study the phenomenon in such a setting, we use an asymptotic technique whereby the number of sample points is kept comparable in magnitude to the dimensionality of observations. The analysis provides a more thorough picture of the phenomenon. In particular, the analysis shows that as long as the Relative Cumulative Efficacy of an additional Feature set (RCEF) is greater (less) than the size of this set, the expected error of the classifier constructed using these additional features will be less (greater) than the expected error of the classifier constructed without them. Our result highlights underlying factors of the peaking phenomenon relative to the classifier used in this study and, at the same time, calls into question the classical wisdom around the peaking phenomenon.},
issn = {1432-1343},
url = {https://doi.org/10.1007/s00357-019-09327-3}
}
@article{sameni2020mathematical,
title = {{Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus}},
author = {Sameni, Reza},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/2003.11371},
year = {2020}
}
@article{sameni2021modelbased,
url = {https://doi.org/10.1109/jstsp.2021.3129118},
year = {2022},
month = feb,
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {16},
number = {2},
pages = {307--317},
author = {Reza Sameni},
title = {Model-Based Prediction and Optimal Control of Pandemics by Non-Pharmaceutical Interventions},
journal = {{IEEE} Journal of Selected Topics in Signal Processing}
}
@article{BahramiRad2021,
url = {https://doi.org/10.1371/journal.pone.0259916},
year = {2021},
month = nov,
publisher = {Public Library of Science ({PLoS})},
volume = {16},
number = {11},
pages = {e0259916},
author = {Ali Bahrami Rad and Conner Galloway and Daniel Treiman and Joel Xue and Qiao Li and Reza Sameni and Dave Albert and Gari D. Clifford},
editor = {Felix Albu},
title = {{Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach}},
journal = {{PLOS} {ONE}}
}
@article{Hegde2022,
url = {https://doi.org/10.1109/jstsp.2022.3145622},
year = {2022},
month = feb,
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {16},
number = {2},
pages = {289--299},
author = {Chaitra Hegde and Ali Bahrami Rad and Reza Sameni and Gari David Clifford},
title = {Modeling Social Distancing and Quantifying Epidemic Disease Exposure in a Built Environment},
journal = {{IEEE} Journal of Selected Topics in Signal Processing}
}
@article{Grooby2022,
doi = {10.1109/access.2022.3144355},
url = {https://doi.org/10.1109/access.2022.3144355},
year = {2022},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {10},
pages = {10934--10948},
author = {E. Grooby and C. Sitaula and D. Fattahi and R. Sameni and K. Tan and L. Zhou and A. King and A. Ramanathan and A. Malhotra and G. A. Dumont and F. Marzbanrad},
title = {{Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications}},
journal = {{IEEE} Access}
}
@article{Oliveira2021,
url = {https://doi.org/10.1109/jbhi.2021.3137048},
year = {2021},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
pages = {1--1},
author = {Jorge Henrique Oliveira and Francesco Renna and Paulo Costa and Diogo Nogueira and Cristina Oliveira and Carlos Ferreira and Alipio Jorge and Sandra Mattos and Thameni Hatem and Thiago Tavares and Andoni Elola and Ali Rad and Reza Sameni and Gari D. Clifford and Miguel Tavares Coimbra},
title = {The {CirCor} {DigiScope} Dataset: From Murmur Detection to Murmur Classification},
journal = {{IEEE} Journal of Biomedical and Health Informatics}
}
@misc{katebi2020deep,
title = {Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device},
author = {Nasim Katebi and Reza Sameni and Gari D. Clifford},
year = {2020},
eprint = {2012.00553},
archiveprefix = {arXiv},
primaryclass = {eess.SP}
}
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