Abstract:We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sour… Show more
“…The source activity representing an artificial spike was multiplied by the lead-field matrix to generate the forward solution at sensor space. Lastly, Gaussian white noise of SNR = 5, 10, 20 dB ( Liu et al, 2002 , Lin et al, 2006 , Mattout et al, 2006 , Mäkelä et al, 2018 , Pantazis and Adler, 2021 , Sohrabpour et al, 2020 ) was added to the sensor space to simulate the noise-contaminated MEG signals. Fig.…”
Highlights
Evaluation of epilepsy source extent estimation from MEG measurements.
FAST-IRES gave robust location and extent estimation under different noise levels.
Epileptic sources were estimated from interictal discharges in drug-resistant epilepsy patients.
FAST-IRES outperformed LCMV in both simulation and patient data analysis.
“…The source activity representing an artificial spike was multiplied by the lead-field matrix to generate the forward solution at sensor space. Lastly, Gaussian white noise of SNR = 5, 10, 20 dB ( Liu et al, 2002 , Lin et al, 2006 , Mattout et al, 2006 , Mäkelä et al, 2018 , Pantazis and Adler, 2021 , Sohrabpour et al, 2020 ) was added to the sensor space to simulate the noise-contaminated MEG signals. Fig.…”
Highlights
Evaluation of epilepsy source extent estimation from MEG measurements.
FAST-IRES gave robust location and extent estimation under different noise levels.
Epileptic sources were estimated from interictal discharges in drug-resistant epilepsy patients.
FAST-IRES outperformed LCMV in both simulation and patient data analysis.
“…(https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset). The MRI [85] 2017 LS-SVM and FD EEG [68] 2017 LS-SVM EEG [15] 2017 SVM EEG [95] 2018 RF classifier EEG [52] 2019 Feature based techniques:LR, Linear SVM, FFNN, SCNN, Ra-SCNN MEG [54] 2019 KNN EEG [99] 2019 Gradient Boosting EEG [40] 2022 SVM EEG [1] 2022 KNN EEG [70] 2021 SVM with a radial basis function kernel MEG DL [25] 2019 ANN EEG [35] 2020 ANN EEG [106] 2017 Softmax Classifier EEG [101] 2018 DNN EEG [31] 2021 Hybrid DNN (CNN and LSTM) EEG [92] 2021 CNN-RNN EEG [80] 2021 DeepMEG-MLP MEG [66] D e e p M E G -C N N 2019 EEGNet-8, LF-CNN and VAR-CNN MEG [30] 2021…”
Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. Detection of disease at the initial stage, using various modalities, is one of the most important factors to decrease mortality rate occurring due to cancer and tumors. Modalities help radiologists and doctors to study the internal structure of the detected disease for retrieving the required features. ML has limitations with the present modalities due to large amounts of data, whereas DL works efficiently with any amount of data. Hence, DL is considered as the enhanced technique of ML where ML uses the learning techniques and DL acquires details on how machines should react around people. DL uses a multilayered neural network to get more information about the used datasets. This study aims to present a systematic literature review related to applications of ML and DL for the detection along with classification of multiple diseases. A detailed analysis of 40 primary studies acquired from the well-known journals and conferences between Jan 2014–2022 was done. It provides an overview of different approaches based on ML and DL for the detection along with the classification of multiple diseases, modalities for medical imaging, tools and techniques used for the evaluation, description of datasets. Further, experiments are performed using MRI dataset to provide a comparative analysis of ML classifiers and DL models. This study will assist the healthcare community by enabling medical practitioners and researchers to choose an appropriate diagnosis technique for a given disease with reduced time and high accuracy.
“…A novel class of inverse solvers arose in the past decade that utilize the recent advances in machine learning, predominantly artificial neural networks (ANNs) to solve M/EEG inverse problems. These approaches require training an ANN to produce a source estimate based on simulated pairs of source and M/EEG activity and achieve high accuracy compared to many conventional methods (Cui et al, 2019; Hecker, Rupprecht, van Elst, & Kornmeier, 2020, 2022; Pantazis & Adler, 2021).…”
Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but low spatial resolution. Locating the neural sources underlying the M/EEG poses an inverse problem, which is ill-posed. We developed a new method based on Recursive Application of Multiple Signal Classification (MUSIC). Our proposed method is able to recover not only the locations but, in contrast to other inverse solutions, also the extent of active brain regions flexibly (FLEX-MUSIC). This is achieved by allowing it to search not only for single dipoles but also dipole clusters of increasing extent to find the best fit during each recursion. FLEX-MUSIC achieved the highest accuracy for both single dipole and extended sources compared to all other methods tested. Remarkably, FLEX-MUSIC was capable to accurately estimate the level of sparsity in the source space (r = 0.82), whereas all other approaches tested failed to do so (r ≤ 0.18). The average computation time of FLEX-MUSIC was considerably lower compared to a popular Bayesian approach and comparable to that of another recursive MUSIC approach and eLORETA. FLEX-MUSIC produces only few errors and was capable to estimate the extent of sources reliably. The accuracy and low computation time of FLEX-MUSIC renders it an improved technique to solve M/EEG inverse problems both in neuroscience research and potentially in pre-surgery diagnostic in epilepsy.
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