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Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k-nearest neighbor, Naïve Bayes and multi-class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non-infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing.", "publicationTitle": "Microscopy Research and Technique", "publisher": "", "place": "", "date": "Mar 2019", "volume": "82", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "283-295", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Microsc. Res. Tech.", "DOI": "10.1002/jemt.23170", "citationKey": "", "url": "http://www.ncbi.nlm.nih.gov/pubmed/30575213", "accessDate": "", "PMID": "30575213", "PMCID": "", "ISSN": "1097-0029", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Automation", "type": 1 }, { "tag": "Blood Specimen Collection", "type": 1 }, { "tag": "Erythrocytes", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Image Processing, Computer-Assisted", "type": 1 }, { "tag": "Life Cycle Stages", "type": 1 }, { "tag": "Malaria", "type": 1 }, { "tag": "Parasite Load", "type": 1 }, { "tag": "Parasitemia", "type": 1 }, { "tag": "Plasmodium", "type": 1 }, { "tag": "hybrid classifiers", "type": 1 }, { "tag": "malaria parasitaemia", "type": 1 }, { "tag": "malaria parasitaemia quantification and grading", "type": 1 } ], "collections": [ "NILZP7J2" ], "relations": {}, "dateAdded": "2019-10-21T14:50:30Z", 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539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/PPSJJYY4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/PPSJJYY4", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Yousaf et al.", "parsedDate": "2019", "numChildren": 1 }, "data": { "key": "PPSJJYY4", "version": 137, "itemType": "journalArticle", "title": "A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD)", "creators": [ { "creatorType": "author", "firstName": "Kanwal", "lastName": "Yousaf" }, { "creatorType": "author", "firstName": "Zahid", "lastName": "Mehmood" }, { "creatorType": "author", "firstName": "Israr Ahmad", "lastName": "Awan" }, { "creatorType": "author", "firstName": "Tanzila", "lastName": "Saba" }, { "creatorType": "author", "firstName": "Riad", "lastName": "Alharbey" }, { "creatorType": "author", "firstName": "Talal", "lastName": "Qadah" }, { "creatorType": "author", "firstName": "Mayda Abdullateef", "lastName": "Alrige" } ], "abstractNote": "", "publicationTitle": "Health Care Management Science", "publisher": "", "place": "", "date": "2019", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1–23", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "NILZP7J2" ], "relations": {}, "dateAdded": "2019-10-21T14:51:51Z", "dateModified": "2019-10-21T14:51:51Z" } }, { "key": "KTIKNY2T", "version": 135, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/KTIKNY2T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/KTIKNY2T", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/ERLYQ6RK", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "KTIKNY2T", "version": 135, "parentItem": "ERLYQ6RK", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text", "accessDate": "2019-10-21T14:51:31Z", "url": 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al.", "parsedDate": "2019", "numChildren": 1 }, "data": { "key": "ERLYQ6RK", "version": 135, "itemType": "journalArticle", "title": "Mobile-health applications for the efficient delivery of health care facility to people with dementia (PwD) and support to their carers: a survey", "creators": [ { "creatorType": "author", "firstName": "Kanwal", "lastName": "Yousaf" }, { "creatorType": "author", "firstName": "Zahid", "lastName": "Mehmood" }, { "creatorType": "author", "firstName": "Tanzila", "lastName": "Saba" }, { "creatorType": "author", "firstName": "Amjad", "lastName": "Rehman" }, { "creatorType": "author", "firstName": "Asmaa Mahdi", "lastName": "Munshi" }, { "creatorType": "author", "firstName": "Riad", "lastName": "Alharbey" }, { "creatorType": "author", "firstName": "Muhammad", "lastName": "Rashid" } ], "abstractNote": "", "publicationTitle": "BioMed research international", "publisher": "", "place": "", "date": "2019", "volume": "2019", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Mobile-health applications for the efficient delivery of health care facility to people with dementia (PwD) and support to their carers", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "NILZP7J2" ], "relations": {}, "dateAdded": "2019-10-21T14:51:28Z", "dateModified": "2019-10-21T14:51:28Z" } }, { "key": "2HAWZ2VS", "version": 135, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/2HAWZ2VS", "type": "application/json" }, "alternate": { "href": 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"firstName": "Toqeer", "lastName": "Mahmood" } ], "abstractNote": "", "publicationTitle": "Microscopy research and technique", "publisher": "", "place": "", "date": "2019", "volume": "82", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "803–811", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "NILZP7J2" ], "relations": {}, "dateAdded": "2019-10-21T14:51:18Z", "dateModified": "2019-10-21T14:51:18Z" } }, { "key": "ZBL47QVR", "version": 135, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": 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"2019-10-21T14:49:40Z", "dateModified": "2019-10-21T14:49:40Z" } }, { "key": "AYP2B2ZL", "version": 135, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/AYP2B2ZL", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/AYP2B2ZL", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/5MZ79TUQ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "AYP2B2ZL", "version": 135, "parentItem": "5MZ79TUQ", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2019-10-21T14:49:32Z", "url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jemt.23281", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Iqbal et al. - 2019 - Deep learning model integrating features and novel.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2019-10-21T14:49:32Z", "dateModified": "2019-10-21T14:49:32Z" } }, { "key": "U5GKC9YQ", "version": 135, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/U5GKC9YQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/U5GKC9YQ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Ullah et al.", "parsedDate": "2019", "numChildren": 1 }, "data": { "key": "U5GKC9YQ", "version": 135, "itemType": "journalArticle", "title": "An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection", "creators": [ { "creatorType": "author", "firstName": "Hidayat", "lastName": "Ullah" }, { "creatorType": "author", "firstName": "Tanzila", "lastName": "Saba" }, { "creatorType": "author", "firstName": "Naveed", "lastName": "Islam" }, { "creatorType": "author", "firstName": "Naveed", "lastName": "Abbas" }, { "creatorType": "author", "firstName": "Amjad", "lastName": "Rehman" }, { "creatorType": "author", "firstName": "Zahid", "lastName": "Mehmood" }, { "creatorType": "author", "firstName": "Adeel", "lastName": "Anjum" } ], "abstractNote": "Atomic recognition of the Exudates (EXs), the major symbol of diabetic retinopathy is essential for automated retinal images analysis. In this article, we proposed a novel machine learning technique for early detection and classification of EXs in color fundus images. The major challenge observed in the classification technique is the selection of optimal features to reduce computational time and space complexity and to provide a high degree of classification accuracy. To address these challenges, this article proposed an evolutionary algorithm based solution for optimal feature selection, which accelerates the classification process and reduces computational complexity. Similarly, three well-known classifiers that is, Naïve Bayes classifier, Support Vector Machine, and Artificial Neural Network are used for the classification of EXs. Moreover, an ensemble-based classifier is used for the selection of best classifier on the basis of majority voting technique. Experiments are performed on three well-known benchmark datasets and a real dataset developed at local Hospital. It has been observed that the proposed technique achieved an accuracy of 98% in the detection and classification of EXs in color fundus images.", "publicationTitle": "Microscopy Research and Technique", "publisher": "", "place": "", "date": "2019", "volume": "82", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "361-372", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/jemt.23178", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/jemt.23178", "accessDate": "2019-10-21T14:49:31Z", "PMID": "", "PMCID": "", "ISSN": "1097-0029", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2019 Wiley Periodicals, Inc.", "extra": "", "tags": [ { "tag": "diabetic retinopathy", "type": 1 }, { "tag": "exudates", "type": 1 }, { "tag": "fovea", "type": 1 }, { "tag": "macula", "type": 1 }, { "tag": "optic disc", "type": 1 } ], "collections": [ "NILZP7J2" ], "relations": {}, "dateAdded": "2019-10-21T14:49:31Z", "dateModified": "2019-10-21T14:49:31Z" } }, { "key": "5MZ79TUQ", "version": 135, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/5MZ79TUQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/5MZ79TUQ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Iqbal et al.", "parsedDate": "2019", "numChildren": 1 }, "data": { "key": "5MZ79TUQ", "version": 135, "itemType": "journalArticle", "title": "Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation", "creators": [ { "creatorType": "author", "firstName": "Sajid", "lastName": "Iqbal" }, { "creatorType": "author", "firstName": "Muhammad U. Ghani", "lastName": "Khan" }, { "creatorType": "author", "firstName": "Tanzila", "lastName": "Saba" }, { "creatorType": "author", "firstName": "Zahid", "lastName": "Mehmood" }, { "creatorType": "author", "firstName": "Nadeem", "lastName": "Javaid" }, { "creatorType": "author", "firstName": "Amjad", "lastName": "Rehman" }, { "creatorType": "author", "firstName": "Rashid", "lastName": "Abbasi" } ], "abstractNote": "Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.", "publicationTitle": "Microscopy Research and Technique", "publisher": "", "place": "", "date": "2019", "volume": "82", "issue": "8", "section": "", "partNumber": "", "partTitle": "", "pages": "1302-1315", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/jemt.23281", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/jemt.23281", "accessDate": "2019-10-21T14:49:14Z", "PMID": "", "PMCID": "", "ISSN": "1097-0029", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2019 Wiley Periodicals, Inc.", "extra": "", "tags": [ { "tag": "LSTM", "type": 1 }, { "tag": "brain tumor segmentation", "type": 1 }, { "tag": "convolutional neural networks", "type": 1 }, { "tag": "ensemble neural networks", "type": 1 } ], "collections": [ "NILZP7J2" ], "relations": {}, "dateAdded": "2019-10-21T14:49:14Z", "dateModified": "2019-10-21T14:49:14Z" } }, { "key": "H2P5NGYR", "version": 133, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/H2P5NGYR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/H2P5NGYR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Assi et al.", "parsedDate": "2018-10-19", "numChildren": 1 }, "data": { "key": "H2P5NGYR", "version": 133, "itemType": "journalArticle", "title": "Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction", "creators": [ { "creatorType": "author", "firstName": "Elie Bou", "lastName": "Assi" }, { "creatorType": "author", "firstName": "Laura", "lastName": "Gagliano" }, { "creatorType": "author", "firstName": "Sandy", "lastName": "Rihana" }, { "creatorType": "author", "firstName": "Dang K.", "lastName": "Nguyen" }, { "creatorType": "author", "firstName": "Mohamad", "lastName": "Sawan" } ], "abstractNote": "The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%.", "publicationTitle": "Scientific Reports", "publisher": "", "place": "", "date": "2018-10-19", "volume": "8", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "15491", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1038/s41598-018-33969-9", "citationKey": "", "url": "https://www.nature.com/articles/s41598-018-33969-9", "accessDate": "2018-10-23T02:01:47Z", "PMID": "", "PMCID": "", "ISSN": "2045-2322", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "En", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2018 The Author(s)", "extra": "", "tags": [ { "tag": "Artificial neural network" }, { "tag": "Entropy" }, { "tag": "Epilepsy" }, { "tag": "MLP" }, { "tag": "Machine learning" }, { "tag": "Neural Networks (Computer)" }, { "tag": "Seizure prediction" } ], "collections": [], "relations": {}, "dateAdded": "2018-10-23T02:01:47Z", "dateModified": "2018-10-23T02:03:48Z" } }, { "key": "NYFIFJWB", "version": 143, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/NYFIFJWB", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/NYFIFJWB", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/H2P5NGYR", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "NYFIFJWB", "version": 143, "parentItem": "H2P5NGYR", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2018-10-23T02:01:49Z", "url": "https://www.nature.com/articles/s41598-018-33969-9.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Assi et al. - 2018 - Bispectrum Features and Multilayer Perceptron Clas.pdf", "md5": "2786b6774d4967aeace3a394624544c8", "mtime": 1540260109529, "tags": [], "relations": {}, "dateAdded": "2018-10-23T02:01:49Z", "dateModified": "2018-10-23T02:01:49Z" } }, { "key": "FGUG5B5E", "version": 144, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/FGUG5B5E", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/FGUG5B5E", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Hawco et al.", "parsedDate": "2007-05-01", "numChildren": 1 }, "data": { "key": "FGUG5B5E", "version": 144, "itemType": "journalArticle", "title": "BOLD changes occur prior to epileptic spikes seen on scalp EEG", "creators": [ { "creatorType": "author", "firstName": "Colin S.", "lastName": "Hawco" }, { "creatorType": "author", "firstName": "Andrew P.", "lastName": "Bagshaw" }, { "creatorType": "author", "firstName": "Yingli", "lastName": "Lu" }, { "creatorType": "author", "firstName": "François", "lastName": "Dubeau" }, { "creatorType": "author", "firstName": "Jean", "lastName": "Gotman" } ], "abstractNote": "This study examined BOLD changes prior to interictal discharges in the EEG of patients with epilepsy. From a database of 143 EEG-fMRI studies, we selected the 16 data sets that showed both strong fMRI activation in the original analysis and only a single spike type in the EEG. Scans were then analyzed using seven model HRFs, peaking 3 or 1 s before the event, or 1, 3, 5, 7, or 9 s after it. An HRF was calculated using a deconvolution method for all activations seen in each analysis. The results showed that seven data sets had HRFs that peaked 1 s after the event or earlier, indicating a BOLD change starting prior to the spike seen on the scalp EEG. This is surprising since the BOLD change is expected to result from the spike. For most of the data sets with early peaking HRFs, the maximum activation in all of the statistical maps was when the model HRF peaked 1 s after the event, suggesting that the early activation was at least as important as any later activation. We suggest that this early activity is the result of neuronal changes occurring several seconds prior to a surface EEG event, but that these changes are not visible on the scalp. This is the first report of a BOLD response occurring several seconds prior to an interictal event seen on the scalp and could have important implications for our understanding of the generation of epileptic discharges.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "May 01, 2007", "volume": "35", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "1450-1458", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Neuroimage", "DOI": "10.1016/j.neuroimage.2006.12.042", "citationKey": "", "url": "", "accessDate": "", "PMID": "17399999", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Adult", "type": 1 }, { "tag": "Aged", "type": 1 }, { "tag": "Brain Mapping", "type": 1 }, { "tag": "Cerebrovascular Circulation", "type": 1 }, { "tag": "Data Interpretation, Statistical", "type": 1 }, { "tag": "Electroencephalography", "type": 1 }, { "tag": "Epilepsy", "type": 1 }, { "tag": "Female", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Magnetic Resonance Imaging", "type": 1 }, { "tag": "Male", "type": 1 }, { "tag": "Middle Aged", "type": 1 }, { "tag": "Oxygen", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2017-11-03T16:20:52Z", "dateModified": "2017-11-03T16:20:52Z" } }, { "key": "EEA744UW", "version": 126, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/EEA744UW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/EEA744UW", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/FGUG5B5E", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } } }, "data": { "key": "EEA744UW", "version": 126, "parentItem": "FGUG5B5E", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2017-11-03T16:20:52Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/17399999", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2017-11-03T16:20:52Z", "dateModified": "2017-11-03T16:20:52Z" } }, { "key": "HNET9JZM", "version": 125, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/HNET9JZM", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/HNET9JZM", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/CIIXMCIM", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "HNET9JZM", "version": 125, "parentItem": "CIIXMCIM", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Full Text PDF", "accessDate": "2017-05-10T18:35:08Z", "url": "http://www.sciencedirect.com/science/article/pii/S0165027015002277/pdfft?md5=890fb2b1710dcbf12e9974e3587107fd&pid=1-s2.0-S0165027015002277-main.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Gadhoumi et al. - 2016 - Seizure prediction for therapeutic devices A revi.pdf", "md5": "598b4175873de25ec423af23977ffa94", "mtime": 1494441310552, "tags": [], "relations": {}, "dateAdded": "2017-05-10T18:35:08Z", "dateModified": "2017-05-10T18:35:10Z" } }, { "key": "CIIXMCIM", "version": 124, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/CIIXMCIM", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/CIIXMCIM", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Gadhoumi et al.", "parsedDate": "2016-02-15", "numChildren": 1 }, "data": { "key": "CIIXMCIM", "version": 124, "itemType": "journalArticle", "title": "Seizure prediction for therapeutic devices: A review", "creators": [ { "creatorType": "author", "firstName": "Kais", "lastName": "Gadhoumi" }, { "creatorType": "author", "firstName": "Jean-Marc", "lastName": "Lina" }, { "creatorType": "author", "firstName": "Florian", "lastName": "Mormann" }, { "creatorType": "author", "firstName": "Jean", "lastName": "Gotman" } ], "abstractNote": "Research in seizure prediction has come a long way since its debut almost 4 decades ago. Early studies suffered methodological caveats leading to overoptimistic results and lack of statistical significance. The publication of guidelines addressing mainly the question of performance evaluation and statistical validation in seizure prediction helped revising the status of the field. While many studies failed to prove that above chance prediction is possible by applying these guidelines, other studies were successful. Methods based on EEG analysis using linear and nonlinear measures were reportedly successful in detecting preictal changes and using them to predict seizures above chance. In this review, we present a selection of studies in seizure prediction published in the last decade. The studies were selected based on the validity of the methods and the statistical significance of performance results. These results varied between studies and many showed acceptable levels of sensitivity and specificity that could be appealing for therapeutic devices. The relatively large prediction horizon and early preictal changes reported in most studies suggest that seizure prediction may work better in closed loop seizure control devices rather than as seizure advisory devices. The emergence of a large database of annotated long-term EEG recordings should help prospective assessment of prediction methods. Some questions remain to be addressed before large clinical trials involving seizure prediction can be carried out.", "publicationTitle": "Journal of Neuroscience Methods", "publisher": "", "place": "", "date": "February 15, 2016", "volume": "260", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "270-282", "series": "Methods and Models in Epilepsy Research", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Journal of Neuroscience Methods", "DOI": "10.1016/j.jneumeth.2015.06.010", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0165027015002277", "accessDate": "2017-05-10T18:35:08Z", "PMID": "", "PMCID": "", "ISSN": "0165-0270", "archive": "", "archiveLocation": "", "shortTitle": "Seizure prediction for therapeutic devices", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Focal epilepsy", "type": 1 }, { "tag": "Intracerebral EEG", "type": 1 }, { "tag": "Seizure prediction", "type": 1 }, { "tag": "Statistical validation", "type": 1 }, { "tag": "Therapeutic devices", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2017-05-10T18:35:08Z", "dateModified": "2017-05-10T18:35:08Z" } }, { "key": "K4U8QEMX", "version": 122, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/K4U8QEMX", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/K4U8QEMX", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/HQWWCQ5N", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "K4U8QEMX", "version": 122, "parentItem": "HQWWCQ5N", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Full Text PDF", "accessDate": "2017-04-04T18:08:12Z", "url": "http://www.sciencedirect.com/science/article/pii/S1746809417300277/pdfft?md5=73cabaff244257810caf68bc6fc453b8&pid=1-s2.0-S1746809417300277-main.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Bou Assi et al. - 2017 - Towards accurate prediction of epileptic seizures.pdf", "md5": "06b4d3b8b04450902090521579393f40", "mtime": 1491329294369, "tags": [], "relations": {}, "dateAdded": "2017-04-04T18:08:12Z", "dateModified": "2017-04-04T18:08:14Z" } }, { "key": "HQWWCQ5N", "version": 123, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/HQWWCQ5N", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/HQWWCQ5N", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Bou Assi et al.", "parsedDate": "2017-04", "numChildren": 1 }, "data": { "key": "HQWWCQ5N", "version": 123, "itemType": "journalArticle", "title": "Towards accurate prediction of epileptic seizures: A review", "creators": [ { "creatorType": "author", "firstName": "Elie", "lastName": "Bou Assi" }, { "creatorType": "author", "firstName": "Dang K.", "lastName": "Nguyen" }, { "creatorType": "author", "firstName": "Sandy", "lastName": "Rihana" }, { "creatorType": "author", "firstName": "Mohamad", "lastName": "Sawan" } ], "abstractNote": "Recent research has investigated the possibility of predicting epileptic seizures. Intervention before the onset of seizure manifestations could be envisioned with accurate seizure forecasting. Although efforts for better prediction have been made, the translation of current approaches to clinical applications is still not possible. While early findings have been optimistic, the absence of statistical validation and reproducibility has raised doubts about the existence of a preictal state. Analysis and algorithmic studies are providing evidence that transition to the ictal state is not random, with build-up leading to seizures. We have reviewed the general framework of reliable algorithmic seizure prediction studies, discussing each component of the whole block diagram. We have explored steps along the pathway, from signal acquisition to adequate performance evaluation that should be taken into account in the design of an efficient seizure advisory/intervention system. The present review has established that there is potential for improvement and optimization in the seizure prediction framework. New databases, higher sampling frequencies, adequate preprocessing, electrode selection, and machine-learning considerations are all elements of the prediction scheme that should be assessed to achieve more realistic, better-than-chance performances.", "publicationTitle": "Biomedical Signal Processing and Control", "publisher": "", "place": "", "date": "April 2017", "volume": "34", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "144-157", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Biomedical Signal Processing and Control", "DOI": "10.1016/j.bspc.2017.02.001", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1746809417300277", "accessDate": "2017-04-04T18:08:12Z", "PMID": "", "PMCID": "", "ISSN": "1746-8094", "archive": "", "archiveLocation": "", "shortTitle": "Towards accurate prediction of epileptic seizures", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Classification", "type": 1 }, { "tag": "Epilepsy", "type": 1 }, { "tag": "Epileptic seizure" }, { "tag": "Preictal state", "type": 1 }, { "tag": "Seizure detection" }, { "tag": "Seizure forecasting", "type": 1 }, { "tag": "Signal processing", "type": 1 }, { "tag": "feature extraction", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2017-04-04T18:08:12Z", "dateModified": "2017-04-04T18:08:12Z" } }, { "key": "EPVAA92X", "version": 144, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/EPVAA92X", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/EPVAA92X", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Sweeney et al.", "parsedDate": "2013-01", "numChildren": 1 }, "data": { "key": "EPVAA92X", "version": 144, "itemType": "journalArticle", "title": "The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique", "creators": [ { "creatorType": "author", "firstName": "Kevin T.", "lastName": "Sweeney" }, { "creatorType": "author", "firstName": "Seán F.", "lastName": "McLoone" }, { "creatorType": "author", "firstName": "Tomás E.", "lastName": "Ward" } ], "abstractNote": "Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results.", "publicationTitle": "IEEE transactions on bio-medical engineering", "publisher": "", "place": "", "date": "Jan 2013", "volume": "60", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "97-105", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "IEEE Trans Biomed Eng", "DOI": "10.1109/TBME.2012.2225427", "citationKey": "", "url": "", "accessDate": "", "PMID": "23086501", "PMCID": "", "ISSN": "1558-2531", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Adult", "type": 1 }, { "tag": "Algorithms", "type": 1 }, { "tag": "Artifacts", "type": 1 }, { "tag": "Electroencephalography", "type": 1 }, { "tag": "Female", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Male", "type": 1 }, { "tag": "Models, Theoretical", "type": 1 }, { "tag": "Motion Artifact Contaminated fNIRS and EEG Data" }, { "tag": "Signal Processing, Computer-Assisted", "type": 1 }, { "tag": "Signal-To-Noise Ratio", "type": 1 }, { "tag": "Spectroscopy, Near-Infrared", "type": 1 } ], "collections": [], "relations": { "dc:relation": [ "http://zotero.org/groups/539783/items/GVSCU3H7", "http://zotero.org/groups/539783/items/7K7VR6DU" ] }, "dateAdded": "2017-03-22T02:09:26Z", "dateModified": "2017-03-22T02:09:50Z" } }, { "key": "RSP7KFAE", "version": 117, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/RSP7KFAE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/RSP7KFAE", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/EPVAA92X", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } } }, "data": { "key": "RSP7KFAE", "version": 117, "parentItem": "EPVAA92X", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2017-03-22T02:09:26Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/23086501", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2017-03-22T02:09:26Z", "dateModified": "2017-03-22T02:09:26Z" } }, { "key": "7K7VR6DU", "version": 144, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/7K7VR6DU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/7K7VR6DU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Goldberger et al.", "parsedDate": "2000-06-13", "numChildren": 1 }, "data": { "key": "7K7VR6DU", "version": 144, "itemType": "journalArticle", "title": "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals", "creators": [ { "creatorType": "author", "firstName": "A. L.", "lastName": "Goldberger" }, { "creatorType": "author", "firstName": "L. A.", "lastName": "Amaral" }, { "creatorType": "author", "firstName": "L.", "lastName": "Glass" }, { "creatorType": "author", "firstName": "J. M.", "lastName": "Hausdorff" }, { "creatorType": "author", "firstName": "P. C.", "lastName": "Ivanov" }, { "creatorType": "author", "firstName": "R. G.", "lastName": "Mark" }, { "creatorType": "author", "firstName": "J. E.", "lastName": "Mietus" }, { "creatorType": "author", "firstName": "G. B.", "lastName": "Moody" }, { "creatorType": "author", "firstName": "C. K.", "lastName": "Peng" }, { "creatorType": "author", "firstName": "H. E.", "lastName": "Stanley" } ], "abstractNote": "The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.", "publicationTitle": "Circulation", "publisher": "", "place": "", "date": "Jun 13, 2000", "volume": "101", "issue": "23", "section": "", "partNumber": "", "partTitle": "", "pages": "E215-220", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Circulation", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "10851218", "PMCID": "", "ISSN": "1524-4539", "archive": "", "archiveLocation": "", "shortTitle": "PhysioBank, PhysioToolkit, and PhysioNet", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Databases as Topic", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Internet", "type": 1 }, { "tag": "Motion Artifact Contaminated fNIRS and EEG Data" }, { "tag": "Non-programmatic", "type": 1 }, { "tag": "Physiology", "type": 1 }, { "tag": "Research", "type": 1 }, { "tag": "Software", "type": 1 } ], "collections": [], "relations": { "dc:relation": [ "http://zotero.org/groups/539783/items/GVSCU3H7", "http://zotero.org/groups/539783/items/EPVAA92X" ] }, "dateAdded": "2017-03-22T02:08:37Z", "dateModified": "2017-03-22T02:08:48Z" } }, { "key": "2KKCHJJ6", "version": 116, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/2KKCHJJ6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/2KKCHJJ6", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/539783/items/7K7VR6DU", "type": "application/json" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } } }, "data": { "key": "2KKCHJJ6", "version": 116, "parentItem": "7K7VR6DU", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2017-03-22T02:08:37Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/10851218", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2017-03-22T02:08:37Z", "dateModified": "2017-03-22T02:08:37Z" } }, { "key": "GVSCU3H7", "version": 144, "library": { "type": "group", "id": 539783, "name": "epiNIRSeeg", "links": { "alternate": { "href": "https://www.zotero.org/groups/epinirseeg", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/539783/items/GVSCU3H7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/epinirseeg/items/GVSCU3H7", "type": "text/html" } }, "meta": { "createdByUser": { "id": 72579, "username": "codina", "name": "Edgar Guevara", "links": { "alternate": { "href": "https://www.zotero.org/codina", "type": "text/html" } } }, "creatorSummary": "Sweeney et al.", "parsedDate": "2011", "numChildren": 1 }, "data": { "key": "GVSCU3H7", "version": 144, "itemType": "journalArticle", "title": "A methodology for validating artifact removal techniques for fNIRS", "creators": [ { "creatorType": "author", "firstName": "Kevin T.", "lastName": "Sweeney" }, { "creatorType": "author", "firstName": "Hasan", "lastName": "Ayaz" }, { "creatorType": "author", "firstName": "Tomás E.", "lastName": "Ward" }, { "creatorType": "author", "firstName": "Meltem", "lastName": "Izzetoglu" }, { "creatorType": "author", "firstName": "Seán F.", "lastName": "McLoone" }, { "creatorType": "author", "firstName": "Banu", "lastName": "Onaral" } ], "abstractNote": "fNIRS recordings are increasingly utilized to monitor brain activity in both clinical and connected health settings. These optical recordings provide a convenient measurement of cerebral hemodynamic changes which can be linked to motor and cognitive performance. Such measurements are of clinical utility in a broad range of conditions ranging from dementia to movement rehabilitation therapy. For such applications fNIRS is increasingly deployed outside the clinic for patient monitoring in the home. However, such a measurement environment is poorly controlled and motion, in particular, is a major source of artifacts in the signal, leading to poor signal quality for subsequent clinical interpretation. Artifact removal techniques are increasingly being employed with an aim of reducing the effect of the noise in the desired signal. Currently no methodology is available to accurately determine the efficacy of a given artifact removal technique due to the lack of a true reference for the uncontaminated signal. In this paper we propose a novel methodology for fNIRS data collection allowing for effective validation of artifact removal techniques. This methodology describes the use of two fNIRS channels in close proximity allowing them to sample the same measurement location; allowing for the introducing of motion artifact to only one channel while having the other free of contamination. Through use of this methodology, for each motion artifact epoch, a true reference for the uncontaminated signal becomes available for use in the development and performance evaluation of signal processing strategies. The advantage of the described methodology is demonstrated using a simple artifact removal technique with an accelerometer based reference.", "publicationTitle": "Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference", "publisher": "", "place": "", "date": "2011", "volume": "2011", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "4943-4946", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Conf Proc IEEE Eng Med Biol Soc", "DOI": "10.1109/IEMBS.2011.6091225", "citationKey": "", "url": "", "accessDate": "", "PMID": "22255447", "PMCID": "", "ISSN": "1557-170X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Artifacts", "type": 1 }, { "tag": "Brain", "type": 1 }, { "tag": "Diagnosis, Computer-Assisted", "type": 1 }, { "tag": "Functional Neuroimaging", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Motion Artifact Contaminated fNIRS and EEG Data" }, { "tag": "Movement", "type": 1 }, { "tag": "Reproducibility of Results", "type": 1 }, { "tag": "Sensitivity and specificity", "type": 1 }, { "tag": "Spectroscopy, Near-Infrared", "type": 1 } ], "collections": [], "relations": { "dc:relation": [ "http://zotero.org/groups/539783/items/7K7VR6DU", "http://zotero.org/groups/539783/items/EPVAA92X" ] }, "dateAdded": "2017-03-22T02:04:00Z", "dateModified": "2017-03-22T02:04:00Z" } } ]