1
|
Dou S, Liu X, Deng Y, Chen Y, Song P, Wen T, Han B. Lightweight and wearable magnetoencephalography system based on spatially-grid constrained coils and compact magnetically shielded room. Neuroimage 2024; 300:120842. [PMID: 39304094 DOI: 10.1016/j.neuroimage.2024.120842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 09/22/2024] Open
Abstract
Magnetoencephalography based on optically pumped magnetometers can passively detect the ultra-weak brain magnetic field signals, which has significant clinical application prospects for the diagnosis and treatment of cerebral disorders. This paper proposes a brain magnetic signal measurement method on the basis of the active-passive coupling magnetic shielding strategy and helmet-mounted detection array, which has lower cost and comparable performance over the existing ones. We first utilized the spatially-grid constrained coils and biplanar coils with proportion-integration-differentiation controller with tracking differentiator to ensure a near-zero and stable magnetic field environment with large uniform region. Subsequently, we implemented the brain magnetic signal measurement with the subject randomly moving fingers through tapping a keyboard and with the condition of opening and closing the eyes. Effectively induced brain magnetic signals were detected at the motor functional area and occipital lobe area in the two experiments, respectively. The proposed method will contribute to the development of functional brain imaging.
Collapse
Affiliation(s)
- Shuai Dou
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China; Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing 100191, China
| | - Xikai Liu
- Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Zhejiang Engineering Research Center of Precision Electromagnetic Control Technology and Equipment, Ningbo 315800, China.
| | - Ya Deng
- Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Zhejiang Engineering Research Center of Precision Electromagnetic Control Technology and Equipment, Ningbo 315800, China.
| | - Yimin Chen
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China
| | - Pengfei Song
- Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Zhejiang Engineering Research Center of Precision Electromagnetic Control Technology and Equipment, Ningbo 315800, China
| | - Tong Wen
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China; Ningbo Institute of Technology, Beihang University, Ningbo 315800, China
| | - Bangcheng Han
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing 100191, China
| |
Collapse
|
2
|
Bastola S, Jahromi S, Chikara R, Stufflebeam SM, Ottensmeyer MP, De Novi G, Papadelis C, Alexandrakis G. Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study. Bioengineering (Basel) 2024; 11:897. [PMID: 39329639 PMCID: PMC11428344 DOI: 10.3390/bioengineering11090897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2024] [Revised: 09/03/2024] [Accepted: 09/04/2024] [Indexed: 09/28/2024] Open
Abstract
Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.
Collapse
Affiliation(s)
- Subrat Bastola
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
| | - Saeed Jahromi
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Rupesh Chikara
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA;
| | - Mark P. Ottensmeyer
- Medical Device & Simulation Laboratory, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA 02139, USA; (M.P.O.); (G.D.N.)
| | - Gianluca De Novi
- Medical Device & Simulation Laboratory, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA 02139, USA; (M.P.O.); (G.D.N.)
| | - Christos Papadelis
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - George Alexandrakis
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
| |
Collapse
|
3
|
Fonseca N, Bowerman J, Askari P, Proskovec AL, Feltrin FS, Veltkamp D, Early H, Wagner BC, Davenport EM, Maldjian JA. Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing. J Imaging 2024; 10:80. [PMID: 38667978 PMCID: PMC11051542 DOI: 10.3390/jimaging10040080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/19/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.
Collapse
Affiliation(s)
- N.C.d. Fonseca
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jason Bowerman
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Pegah Askari
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Arlington, Arlington, TX 76019, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Amy L. Proskovec
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Fabricio Stewan Feltrin
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Daniel Veltkamp
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Heather Early
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ben C. Wagner
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Elizabeth M. Davenport
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Joseph A. Maldjian
- MEG Center of Excellence, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (P.A.); (A.L.P.); (F.S.F.); (D.V.); (H.E.); (E.M.D.); (J.A.M.)
- Advanced Neuroscience Imaging Research (ANSIR) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; (J.B.); (B.C.W.)
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| |
Collapse
|
4
|
Chou HY, Lin YC, Hsieh SY, Chou HH, Lai CS, Wang B, Tsai YS. Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01050-9. [PMID: 38499706 DOI: 10.1007/s10278-024-01050-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/05/2023] [Accepted: 12/18/2023] [Indexed: 03/20/2024]
Abstract
Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in preterm infants in a timely and efficient manner. This retrospective study involves two datasets: a lung region segmentation dataset comprising 1491 chest radiographs of infants, and a BPD prediction dataset comprising 1021 chest radiographs of preterm infants. Transfer learning of a pre-trained machine learning model was employed for lung region segmentation and image fusion for BPD prediction to enhance the performance of the AI model. The lung segmentation model uses transfer learning to achieve a dice score of 0.960 for preterm infants with ≤ 168 h postnatal age. The BPD prediction model exhibited superior diagnostic performance compared to that of experts and demonstrated consistent performance for chest radiographs obtained at ≤ 24 h postnatal age, and those obtained at 25 to 168 h postnatal age. This study is the first to use deep learning on preterm chest radiographs for lung segmentation to develop a BPD prediction model with an early detection time of less than 24 h. Additionally, this study compared the model's performance according to both NICHD and Jensen criteria for BPD. Results demonstrate that the AI model surpasses the diagnostic accuracy of experts in predicting lung development in preterm infants.
Collapse
Affiliation(s)
- Hao-Yang Chou
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Yung-Chieh Lin
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, 704, Taiwan
| | - Sun-Yuan Hsieh
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 70101, Taiwan
- Institution of Medical Informatics, National Cheng Kung University, Tainan, 70101, Taiwan
- Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, 70101, Taiwan
- Department of Computer Science and Information Engineering, National Chi Nan University, Nantou, 54561, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan
- Research Center for Information Technology Innovation, Academia Sinica, Taipei, 115, Taiwan
| | - Hsin-Hung Chou
- Department of Computer Science and Information Engineering, National Chi Nan University, Nantou, 54561, Taiwan.
| | - Cheng-Shih Lai
- Department of Medical Imaging, National Cheng Kung University Hospital, Tainan, 701401, Taiwan
| | - Bow Wang
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, 704, Taiwan
| | - Yi-Shan Tsai
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, 704, Taiwan.
| |
Collapse
|
5
|
Zheng L, Liao P, Wu X, Cao M, Cui W, Lu L, Xu H, Zhu L, Lyu B, Wang X, Teng P, Wang J, Vogrin S, Plummer C, Luan G, Gao JH. An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography. J Neural Eng 2023; 20:046036. [PMID: 37615416 DOI: 10.1088/1741-2552/acef92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023]
Abstract
Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.
Collapse
Affiliation(s)
- Li Zheng
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Pan Liao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Xiuwen Wu
- Changping Laboratory, Beijing, People's Republic of China
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Miao Cao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Wei Cui
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Lingxi Lu
- Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, People's Republic of China
| | - Hui Xu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Linlin Zhu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Bingjiang Lyu
- Changping Laboratory, Beijing, People's Republic of China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Pengfei Teng
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jing Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Simon Vogrin
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Chris Plummer
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Jia-Hong Gao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
- McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China
- National Biomedical Imaging Center, Peking University, Beijing, People's Republic of China
| |
Collapse
|
6
|
Pan R, Yang C, Li Z, Ren J, Duan Y. Magnetoencephalography-based approaches to epilepsy classification. Front Neurosci 2023; 17:1183391. [PMID: 37502686 PMCID: PMC10368885 DOI: 10.3389/fnins.2023.1183391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/12/2023] [Indexed: 07/29/2023] Open
Abstract
Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.
Collapse
Affiliation(s)
- Ruoyao Pan
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Ying Duan
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
| |
Collapse
|
7
|
Doborjeh M, Liu X, Doborjeh Z, Shen Y, Searchfield G, Sanders P, Wang GY, Sumich A, Yan WQ. Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:902. [PMID: 36679693 PMCID: PMC9861477 DOI: 10.3390/s23020902] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/21/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients' responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients' EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients' outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%-100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.
Collapse
Affiliation(s)
- Maryam Doborjeh
- Knowledge Engineering and Discovery Research Institute (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
| | - Xiaoxu Liu
- Knowledge Engineering and Discovery Research Institute (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
- Centre for Robotics & Vision (CeRV), Auckland University of Technology, Auckland 1010, New Zealand
| | - Zohreh Doborjeh
- Eisdell Moore Centre, Audiology, School of Population Health, The University of Auckland, Auckland 1010, New Zealand
- School of Psychology, The University of Waikato, Hamilton 3216, New Zealand
| | - Yuanyuan Shen
- Knowledge Engineering and Discovery Research Institute (KEDRI), School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
| | - Grant Searchfield
- Eisdell Moore Centre, Audiology, School of Population Health, The University of Auckland, Auckland 1010, New Zealand
| | - Philip Sanders
- Eisdell Moore Centre, Audiology, School of Population Health, The University of Auckland, Auckland 1010, New Zealand
| | - Grace Y. Wang
- School of Psychology and Wellbeing, University of Southern Queensland, Darling Heights, QLD 4350, Australia
- Centre for Health Research, University of Southern Queensland, Darling Heights, QLD 4350, Australia
| | - Alexander Sumich
- NTU Psychology, Nottingham Trent University, Nottingham NG1 4FQ, UK
| | - Wei Qi Yan
- Centre for Robotics & Vision (CeRV), Auckland University of Technology, Auckland 1010, New Zealand
| |
Collapse
|
8
|
Cheng C, Liu Y, You B, Zhou Y, Gao F, Yang L, Dai Y. Multilevel Feature Learning Method for Accurate Interictal Epileptiform Spike Detection. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2506-2516. [PMID: 35877795 DOI: 10.1109/tnsre.2022.3193666] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Interictal epileptiform spike (referred to as spike) detected from electroencephalograms lasting only 20- to 200-ms can provide a reliable evidence-based indicator for clinical seizure type diagnosis. Recent feature representation approaches focus either on the concrete-level or on abstract-level information mining of the spike, thus demonstrating suboptimal detection performance. Additionally, existing abstract-level information mining methods of the spike based deep learning networks have not realized the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity, which affects detection performance. Thus, a multilevel feature learning method for accurate spike detection was proposed in this study. Specifically, the spatio-temporal-frequency multidomain information in concrete-level first are inferred the common mimetic properties of the spike using the multidomain feature extractors. Then, the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity is suitably captured using the temporal convolutional network. Finally, the spatio-temporal-frequency multidomain long-term dependent feature representation of spike is calculated using the element-wise manner to fuse the feature representation in concrete- and abstract-levels. The experimental results indicate that the proposed method can achieve an accuracy of 90.62±1.38%, sensitivity of 90.38±1.52%, specificity of 91.00±1.60%, precision of 90.33±4.71%, and the false detection rate per minute is 0.148±0.020m-1, which are higher than when using the feature representation in the concrete- or abstract-level alone. Additionally, the detection results indicate that the proposed method avoids the subjectivity and inefficiency of visual inspection, and it enables a highly accurate detection of the spike.
Collapse
|