journals.bib

@comment{{This file has been generated by bib2bib 1.98}}
@comment{{Command line: bib2bib -ob journals.bib -c "author : 'Sameni'" -c "$type = 'ARTICLE'" References.bib}}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
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