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Eastmond C, Subedi A, De S, Intes X. Deep learning in fNIRS: a review. NEUROPHOTONICS 2022; 9:041411. [PMID: 35874933 PMCID: PMC9301871 DOI: 10.1117/1.nph.9.4.041411] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 06/22/2022] [Indexed: 05/28/2023]
Abstract
Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional near-infrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields. Aim: We aim to review the emerging DL applications in fNIRS studies. Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including brain-computer interface, neuro-impairment diagnosis, and neuroscience discovery. Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions: The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.
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Affiliation(s)
- Condell Eastmond
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
| | - Aseem Subedi
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
| | - Suvranu De
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
| | - Xavier Intes
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
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A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders. BIOLOGY 2022; 11:biology11030469. [PMID: 35336842 PMCID: PMC8945195 DOI: 10.3390/biology11030469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 01/19/2023]
Abstract
Simple Summary This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection. Abstract Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.
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Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Panahiazar M, Khozeimeh F, Zare A, Hosseini-Nejad H, Khosravi A, Atiya AF, Aminshahidi D, Hussain S, Rouhani M, Nahavandi S, Acharya UR. Epileptic Seizures Detection Using Deep Learning Techniques: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5780. [PMID: 34072232 PMCID: PMC8199071 DOI: 10.3390/ijerph18115780] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 02/06/2023]
Abstract
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
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Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | | | - Navid Ghassemi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran;
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, School of Medicine, University of California, San Francisco, CA 94143, USA;
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Hossein Hosseini-Nejad
- Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran;
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Amir F. Atiya
- Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo 12613, Egypt;
| | - Diba Aminshahidi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Sadiq Hussain
- System Administrator at Dibrugarh University, Assam 786004, India;
| | - Modjtaba Rouhani
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; (D.A.); (M.R.)
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia; (R.A.); (F.K.); (A.K.); (S.N.)
| | - Udyavara Rajendra Acharya
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Taichung City 41354, Taiwan
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Beheshti M, Mottaghy FM. Special Issue: Emerging Technologies for Medical Imaging Diagnostics, Monitoring and Therapy of Cancers. J Clin Med 2021; 10:jcm10061327. [PMID: 33806986 PMCID: PMC8005165 DOI: 10.3390/jcm10061327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 03/20/2021] [Indexed: 11/16/2022] Open
Abstract
Molecular imaging and therapy play an increasingly important role in the field of "precision medicine" as an emergent prospect for management of the cancerous disease [...].
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Affiliation(s)
- Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, University Hospital, Paracelsus Medical University, 5020 Salzburg, Austria
- Correspondence: ; Tel.: +43-(0)5-7255-26602; Fax: +43-(0)5-7255-26699
| | - Felix M. Mottaghy
- Department of Nuclear Medicine, University Hospital, RWTH University, 52074 Aachen, Germany;
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, 6229 Maastricht, The Netherlands
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