PERFORM Publications

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Title		Authors	PubMed ID
1	Impact of COVID-19 on incidence and trends of adverse events among hospitalised patients in Calgary, Canada: a retrospective chart review study	Wu G; Eastwood CA; Cheligeer C; Southern DA; Zeng Y; Ghali WA; Bakal JA; Boussat B; Flemons W; Forster A; Xu Y; Quan H;	41592994 CONCORDIA
2	Reliability of Comprehensive Facial Soft Tissue Landmark Detection and Analysis Using Frontal View Photographs	Hassanzadeh-Samani S; Pirayesh Z; Motie P; Ghorbanimehr MS; Farzan A; Mohammad-Rahimi H; Behnaz M; Motamedian SR;	40975629 ENCS
3	Microfluidic Liquid Biopsy Minimally Invasive Cancer Diagnosis by Nano-Plasmonic Label-Free Detection of Extracellular Vesicles: Review	Neriya Hegade KP; Bhat RB; Packirisamy M;	40650129 ENCS
4	Real-time motion detection using dynamic mode decomposition	Mignacca M; Brugiapaglia S; Bramburger JJ;	40421310 MATHSTATS
5	All-Inclusive Sensing Tablet with Integrated Passive Mixer for Ultraviscous Solutions	Safiabadi Tali SH; Al-Kassawneh M; Mansouri M; Sadiq Z; Jahanshahi-Anbuhi S;	40327804 ENCS
6	Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism	Cheligeer C; Southern DA; Yan J; Wu G; Pan J; Lee S; Martin EA; Jafarpour H; Eastwood CA; Zeng Y; Quan H;	40105654 ENCS
7	Face Boundary Formulation for Harmonic Models: Face Image Resembling	Huang HT; Li ZC; Wei Y; Suen CY;	39852327 CONCORDIA
8	Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection	Gao Z; Chen X; Xu J; Yu R; Zhang H; Yang J;	39771685 ENCS
9	Non-invasive paper-based sensors containing rare-earth-doped nanoparticles for the detection of D-glucose	López-Peña G; Ortiz-Mansilla E; Arranz A; Bogdan N; Manso-Silván M; Martín Rodríguez E;	38729020 CHEMBIOCHEM
10	Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions	Sun Y; Wang C;	38493601 ENCS
11	Tailoring plasmonic sensing strategies for the rapid and sensitive detection of hypochlorite in swimming water samples	Sadiq Z; Al-Kassawneh M; Safiabadi Tali SH; Jahanshahi-Anbuhi S;	38451315 ENCS
12	Deep learning approach to security enforcement in cloud workflow orchestration	El-Kassabi HT; Serhani MA; Masud MM; Shuaib K; Khalil K;	36691661 ENCS
13	Unique Photoactivated Time-Resolved Response in 2D GeS for Selective Detection of Volatile Organic Compounds	Mohammadzadeh MR; Hasani A; Jaferzadeh K; Fawzy M; De Silva T; Abnavi A; Ahmadi R; Ghanbari H; Askar A; Kabir F; Rajapakse RKND; Adachi MM;	36658730 PHYSICS
14	Gold Nanoparticles-Based Colorimetric Assays for Environmental Monitoring and Food Safety Evaluation	Sadiq Z; Safiabadi Tali SH; Hajimiri H; Al-Kassawneh M; Jahanshahi-Anbuhi S;	36629748 ENCS
15	Stable Cavitation-Mediated Delivery of miR-126 to Endothelial Cells	He S; Singh D; Yusefi H; Helfield B;	36559150 BIOLOGY
16	Novelty detection in rover-based planetary surface images using autoencoders	Stefanuk B; Skonieczny K;	36313243 ENCS
17	A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies	Bettauer V; Costa ACBP; Omran RP; Massahi S; Kirbizakis E; Simpson S; Dumeaux V; Law C; Whiteway M; Hallett MT;	35972285 BIOLOGY
18	Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection	Rjoub G; Wahab OA; Bentahar J; Cohen R; Bataineh AS;	35875592 ENCS
19	Microfluidic Platforms for the Isolation and Detection of Exosomes: A Brief Review	Raju D; Bathini S; Badilescu S; Ghosh A; Packirisamy M;	35630197 ENCS
20	Knowledge distillation approach towards melanoma detection	Khan MS; Alam KN; Dhruba AR; Zunair H; Mohammed N;	35594685 CONCORDIA
21	Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging	Shaikh MS; Jaferzadeh K; Thörnberg B;	35270968 ENCS
22	Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images	Bourouis S; Alharbi A; Bouguila N;	34460578 ENCS
23	X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech	Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H;	33679361 PERFORM
24	A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset.	Rahman A, Zunair H, Reme TR, Rahman MS, Mahdy MRC	33465520 ENCS
25	Bound detergent molecules in bacterial reaction centers facilitate detection of tetryl explosive.	Modafferi D, Zazubovich V, Kálmán L	32632533 PHYSICS
26	Two-stage ultrasound image segmentation using U-Net and test time augmentation.	Amiri M; Brooks R; Behboodi B; Rivaz H;	32350786 IMAGING
27	How do landscape context and fences influence roadkill locations of small and medium-sized mammals?	Plante J, Jaeger JAG, Desrochers A	30711836 GEOGRAPHY
28	Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video.	Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO	29407997 IMAGING
29	Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment.	Dansereau CL, Bellec P, Lee K, Pittau F, Gotman J, Grova C	25565949 PERFORM
30	Detection and Magnetic Source Imaging of Fast Oscillations (40-160 Hz) Recorded with Magnetoencephalography in Focal Epilepsy Patients.	von Ellenrieder N, Pellegrino G, Hedrich T, Gotman J, Lina JM, Grova C, Kobayashi E	26830767 PERFORM

Title:	Detection and Magnetic Source Imaging of Fast Oscillations (40-160 Hz) Recorded with Magnetoencephalography in Focal Epilepsy Patients.
Authors:	von Ellenrieder N, Pellegrino G, Hedrich T, Gotman J, Lina JM, Grova C, Kobayashi E
Link:	https://www.ncbi.nlm.nih.gov/pubmed/26830767?dopt=Abstract
DOI:	10.1007/s10548-016-0471-9
Publication:	Brain topography
Keywords:	Automatic detection; Epilepsy; High frequency activity; Human; Source localization;
PMID:	26830767	Category:	Brain Topogr	Date Added:	2019-06-04
Dept Affiliation:	PERFORM 1 Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada. 2 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, 3775 University Street, Montreal, QC, H3A 2B4, Canada. 3 LEICI, CONICET - Universidad Nacional de La Plata, Calle 116 y 48, 1900, La Plata, Argentina. 4 Département de Génie Électrique, École de Technologie Supérieure, 1100 Notre-Dame Street West, Montreal, QC, H3C 1K3, Canada. 5 Centre de Recherches Mathematiques, Univeristé de Montréal, 2920 Chemin de la tour, Montreal, QC, H3T 1J4, Canada. 6 Center for Advanced Research on Sleep Medecine, Centre de Rech. de l'Hôpital du Sacré-Cœur de Montréal, 5400 W Gouin Blvd, Montreal, QC, H4J 1J5, Canada. 7 Physics Department and PERFORM Centre, Concordia University, 7141 Sherbrooke Street West, Montreal, QC, H4B 1R6, Canada. 8 Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada. eliane.kobayashi@mcgill.ca.

Description:

Detection and Magnetic Source Imaging of Fast Oscillations (40-160 Hz) Recorded with Magnetoencephalography in Focal Epilepsy Patients.

Brain Topogr. 2016 Mar;29(2):218-31

Authors: von Ellenrieder N, Pellegrino G, Hedrich T, Gotman J, Lina JM, Grova C, Kobayashi E

Abstract

We present a framework to detect fast oscillations (FOs) in magnetoencephalography (MEG) and to perform magnetic source imaging (MSI) to determine the location and extent of their generators in the cortex. FOs can be of physiologic origin associated to sensory processing and memory consolidation. In epilepsy, FOs are of pathologic origin and biomarkers of the epileptogenic zone. Seventeen patients with focal epilepsy previously confirmed with identified FOs in scalp electroencephalography (EEG) were evaluated. To handle data deriving from large number of sensors (275 axial gradiometers) we used an automatic detector with high sensitivity. False positives were discarded by two human experts. MSI of the FOs was performed with the wavelet based maximum entropy on the mean method. We found FOs in 11/17 patients, in only one patient the channel with highest FO rate was not concordant with the epileptogenic region and might correspond to physiologic oscillations. MEG FOs rates were very low: 0.02-4.55 per minute. Compared to scalp EEG, detection sensitivity was lower, but the specificity higher in MEG. MSI of FOs showed concordance or partial concordance with proven generators of seizures and epileptiform activity in 10/11 patients. We have validated the proposed framework for the non-invasive study of FOs with MEG. The excellent overall concordance with other clinical gold standard evaluation tools indicates that MEG FOs can provide relevant information to guide implantation for intracranial EEG pre-surgical evaluation and for surgical treatment, and demonstrates the important added value of choosing appropriate FOs detection and source localization methods.

PMID: 26830767 [PubMed - indexed for MEDLINE]

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