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Chen Q, Dong Y, Gai Y. Tactile Location Perception Encoded by Gamma-Band Power. Bioengineering (Basel) 2024; 11:377. [PMID: 38671798 PMCID: PMC11048554 DOI: 10.3390/bioengineering11040377] [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: 03/12/2024] [Revised: 03/31/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The perception of tactile-stimulation locations is an important function of the human somatosensory system during body movements and its interactions with the surroundings. Previous psychophysical and neurophysiological studies have focused on spatial location perception of the upper body. In this study, we recorded single-trial electroencephalography (EEG) responses evoked by four vibrotactile stimulators placed on the buttocks and thighs while the human subject was sitting in a chair with a cushion. METHODS Briefly, 14 human subjects were instructed to sit in a chair for a duration of 1 h or 1 h and 45 min. Two types of cushions were tested with each subject: a foam cushion and an air-cell-based cushion dedicated for wheelchair users to alleviate tissue stress. Vibrotactile stimulations were applied to the sitting interface at the beginning and end of the sitting period. Somatosensory-evoked potentials were obtained using a 32-channel EEG. An artificial neural net was used to predict the tactile locations based on the evoked EEG power. RESULTS We found that single-trial beta (13-30 Hz) and gamma (30-50 Hz) waves can best predict the tactor locations with an accuracy of up to 65%. Female subjects showed the highest performances, while males' sensitivity tended to degrade after the sitting period. A three-way ANOVA analysis indicated that the air-cell cushion maintained location sensitivity better than the foam cushion. CONCLUSION Our finding shows that tactile location information is encoded in EEG responses and provides insights on the fundamental mechanisms of the tactile system, as well as applications in brain-computer interfaces that rely on tactile stimulation.
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Affiliation(s)
| | | | - Yan Gai
- Biomedical Engineering, School of Science and Engineering, Saint Louis University, 3507 Lindell Blvd, St. Louis, MO 63103, USA; (Q.C.); (Y.D.)
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Amiri M, Raimondo F, Fisher PM, Cacic Hribljan M, Sidaros A, Othman MH, Zibrandtsen I, Bergdal O, Fabritius ML, Hansen AE, Hassager C, Højgaard JLS, Jensen HR, Knudsen NV, Laursen EL, Møller JE, Nersesjan V, Nicolic M, Sigurdsson ST, Sitt JD, Sølling C, Welling KL, Willumsen LM, Hauerberg J, Larsen VA, Fabricius ME, Knudsen GM, Kjærgaard J, Møller K, Kondziella D. Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness. Neurocrit Care 2024; 40:718-733. [PMID: 37697124 PMCID: PMC10959792 DOI: 10.1007/s12028-023-01816-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 07/21/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations. METHODS We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome. RESULTS Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]). CONCLUSIONS Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.
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Affiliation(s)
- Moshgan Amiri
- Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Federico Raimondo
- Brain and Behaviour, Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Patrick M Fisher
- Neurobiology Research Unit, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Melita Cacic Hribljan
- Department of Neurophysiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Annette Sidaros
- Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
- Department of Neurophysiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Marwan H Othman
- Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Ivan Zibrandtsen
- Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
- Department of Neurophysiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Ove Bergdal
- Department of Neurosurgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Maria Louise Fabritius
- Department of Neuroanaesthesiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Radiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Hassager
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Joan Lilja S Højgaard
- Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Helene Ravnholt Jensen
- Department of Neuroanaesthesiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Niels Vendelbo Knudsen
- Department of Neuroanaesthesiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Emilie Lund Laursen
- Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Jacob E Møller
- Department of Cardiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Vardan Nersesjan
- Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Copenhagen University Hospital, Copenhagen, Denmark
| | - Miki Nicolic
- Department of Neurophysiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Sigurdur Thor Sigurdsson
- Department of Neuroanaesthesiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Jacobo D Sitt
- Institut du Cerveau - Paris Brain Institute, Inserm, Centre nationl de la recherche scientifique, Assistance Publique - Hôpitaux de Paris, Sorbonne Université, Hôpital de La Pitié Salpêtrière, Paris, France
| | - Christine Sølling
- Department of Neuroanaesthesiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Karen Lise Welling
- Department of Neuroanaesthesiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Lisette M Willumsen
- Department of Neurosurgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - John Hauerberg
- Department of Neurosurgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Vibeke Andrée Larsen
- Department of Radiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Martin Ejler Fabricius
- Department of Neurophysiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jesper Kjærgaard
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Kirsten Møller
- Department of Neuroanaesthesiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniel Kondziella
- Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Ming Z, Chen D, Gao T, Tang Y, Tu W, Chen J. V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges. Neural Netw 2024; 172:106136. [PMID: 38266472 DOI: 10.1016/j.neunet.2024.106136] [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: 05/06/2023] [Revised: 11/20/2023] [Accepted: 01/16/2024] [Indexed: 01/26/2024]
Abstract
Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.
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Affiliation(s)
- Zhekai Ming
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Dan Chen
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China.
| | - Tengfei Gao
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Yunbo Tang
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
| | - Weiping Tu
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Jingying Chen
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
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Portell Penadés E, Alvarez V. A Comprehensive Review and Practical Guide of the Applications of Evoked Potentials in Neuroprognostication After Cardiac Arrest. Cureus 2024; 16:e57014. [PMID: 38681279 PMCID: PMC11046378 DOI: 10.7759/cureus.57014] [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] [Accepted: 03/24/2024] [Indexed: 05/01/2024] Open
Abstract
Cardiorespiratory arrest is a very common cause of morbidity and mortality nowadays, and many therapeutic strategies, such as induced coma or targeted temperature management, are used to reduce patient sequelae. However, these procedures can alter a patient's neurological status, making it difficult to obtain useful clinical information for the reliable estimation of neurological prognosis. Therefore, complementary investigations are conducted in the early stages after a cardiac arrest to clarify functional prognosis in comatose cardiac arrest survivors in the first few hours or days. Current practice relies on a multimodal approach, which shows its greatest potential in predicting poor functional prognosis, whereas the data and tools to identify patients with good functional prognosis remain relatively limited in comparison. Therefore, there is considerable interest in investigating alternative biological parameters and advanced imaging technique studies. Among these, somatosensory evoked potentials (SSEPs) remain one of the simplest and most reliable tools. In this article, we discuss the technical principles, advantages, limitations, and prognostic implications of SSEPs in detail. We will also review other types of evoked potentials that can provide useful information but are less commonly used in clinical practice (e.g., visual evoked potentials; short-, medium-, and long-latency auditory evoked potentials; and event-related evoked potentials, such as mismatch negativity or P300).
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Yan J, Zha F, Zhou J, Zhou J, Zhao J, Zhang Q, Long J, Hou D, Song Z, Wang Y. Combining HD-tDCS with music stimulation for patients with prolonged disorders of consciousness: Study protocol for an RCT trial. NeuroRehabilitation 2024; 54:495-504. [PMID: 38457160 PMCID: PMC11091638 DOI: 10.3233/nre-230282] [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: 11/06/2023] [Accepted: 01/27/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Patients with prolonged disorders of consciousness (pDOC) pose significant challenges to healthcare workers due to their severe motor impairments and limited interaction with the environment. Non-invasive brain stimulation such as high-definition transcranial direct current stimulation (HD-tDCS) and music stimulation show promise in awakening this population. OBJECTIVE In this study, we present a protocol aiming at investigating the efficacy of combined HD-tDCS and music stimulation in awakening patients with pDOC through a single-blind, randomized controlled trial. METHODS Ninety patients with pDOC will be randomly divided into three groups: active HD-tDCS with music stimulation, active HD-tDCS, and sham HD-tDCS. All participants will receive 20 treatment sessions over a period of 10 days and the Coma Recovery Scale-Revised, Glasgow Outcome Scale and electroencephalogram will be used as assessment measures to evaluate their level of consciousness throughout the study. Adverse events and complications will be recorded during treatment. Within-group pre-post comparisons and between-group efficacy comparisons will be conducted to identify the most effective intervention approach. Statistical analysis will be performed using SPSS software with a significance level set at P < 0.05. CONCLUSION The pursuit of awakening therapy for patients with pDOC remains a clinical research challenge. This study protocol is designed with the aim of introducing an innovative non-pharmacological approach which combined HD-tDCS and music stimulation to facilitate the reinstatement of consciousness in patients with pDOC.
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Affiliation(s)
- Jie Yan
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- Department of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Fubing Zha
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Juan Zhou
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Jing Zhou
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Jingpu Zhao
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Qingfang Zhang
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
- Department of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jianjun Long
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Dianrui Hou
- Department of Rehabilitation, Nan’ao People’s Hospital of Shenzhen, Dapeng New District, Shenzhen, China
| | - Zhenhua Song
- Department of Rehabilitation Medicine, Haikou Hospital Affiliated to Xiangya Medical College of Central South University, Haikou, China
| | - Yulong Wang
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
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Wang X, Wang X, Wang C, Wang Z, Liu X, Lv X, Tang Y. A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms. eNeuro 2023; 10:ENEURO.0111-23.2023. [PMID: 37914407 PMCID: PMC10668214 DOI: 10.1523/eneuro.0111-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/22/2023] [Accepted: 09/15/2023] [Indexed: 11/03/2023] Open
Abstract
The objective of this work was to develop a deep learning-based automatic system with reliable performance in detecting interictal epileptiform discharges (IEDs) from scalp electroencephalograms (EEGs). For the present study, 484 raw scalp EEG recordings were included, standardized, and split into 406 for training and 78 for testing. Two neurophysiologists individually annotated the recordings for training in channel-wise manner. Annotations were divided into segments, on which nine deep neural networks (DNNs) were trained for the multiclassification of IED, artifact, and background. The fitted IED detectors were then evaluated on 78 EEG recordings with IED events fully annotated by three experts independently (majority agreement). A two montage-based decision mechanism (TMDM) was designed to determine whether an IED event occurred at a single time instant. Area under the precision-recall curve (AUPRC), as well as false-positive rates, F1 scores, and kappa agreement scores for sensitivity = 0.8 were estimated. In multitype classification, five DNNs provided one-versus-rest AUPRC mean value >0.993 using fivefold cross-validation. In IED detection, the system that had integrated the temporal convolutional network (TCN)-based IED detector and the TMDM rule achieved an AUPRC of 0.811. The false positive was 0.194/min (11.64/h), and the F1 score was 0.745. The agreement score between the system and the experts was 0.905. The proposed framework provides a TCN-based IED detector and a novel two montage-based determining mechanism that combined to make an automatic IED detection system. The system would be useful in aiding clinic EEG interpretation.
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Affiliation(s)
- Xiaoyun Wang
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Xing Wang
- Department of Signal Processing Research, Beijing Solar Electronic Technologies Company Ltd, Beijing 100044, People's Republic of China
| | - Chong Wang
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Zhongyuan Wang
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Xiangyu Liu
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Xiaoling Lv
- Geriatrics Research Institute of Zhejiang Province, Zhejiang Provincial Key Lab of Geriatrics, Zhejiang Hospital, Hangzhou 310013, People's Republic of China
| | - Ying Tang
- Geriatrics Research Institute of Zhejiang Province, Zhejiang Provincial Key Lab of Geriatrics, Zhejiang Hospital, Hangzhou 310013, People's Republic of China
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Wang Y, Cui W, Yu T, Li X, Liao X, Li Y. Dynamic Multi-Graph Convolution-Based Channel-Weighted Transformer Feature Fusion Network for Epileptic Seizure Prediction. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4266-4277. [PMID: 37782584 DOI: 10.1109/tnsre.2023.3321414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Electroencephalogram (EEG) based seizure prediction plays an important role in the closed-loop neuromodulation system. However, most existing seizure prediction methods based on graph convolution network only focused on constructing the static graph, ignoring multi-domain dynamic changes in deep graph structure. Moreover, the existing feature fusion strategies generally concatenated coarse-grained epileptic EEG features directly, leading to the suboptimal seizure prediction performance. To address these issues, we propose a novel multi-branch dynamic multi-graph convolution based channel-weighted transformer feature fusion network (MB-dMGC-CWTFFNet) for the patient-specific seizure prediction with the superior performance. Specifically, a multi-branch (MB) feature extractor is first applied to capture the temporal, spatial and spectral representations fromthe epileptic EEG jointly. Then, we design a point-wise dynamic multi-graph convolution network (dMGCN) to dynamically learn deep graph structures, which can effectively extract high-level features from the multi-domain graph. Finally, by integrating the local and global channel-weighted strategies with the multi-head self-attention mechanism, a channel-weighted transformer feature fusion network (CWTFFNet) is adopted to efficiently fuse the multi-domain graph features. The proposed MB-dMGC-CWTFFNet is evaluated on the public CHB-MIT EEG and a private intracranial sEEG datasets, and the experimental results demonstrate that our proposed method achieves outstanding prediction performance compared with the state-of-the-art methods, indicating an effective tool for patient-specific seizure warning. Our code will be available at: https://github.com/Rockingsnow/MB-dMGC-CWTFFNet.
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Mormando M, Galiè E, Bianchini M, Lauretta R, Puliani G, Tanzilli A, Anceschi U, Simone G, Petreri G, Graziano G, Pace A, Appetecchia M. Neurological adverse events of mitotane in adrenocortical carcinoma: results of a pilot study. Front Oncol 2023; 13:1222002. [PMID: 37645432 PMCID: PMC10461107 DOI: 10.3389/fonc.2023.1222002] [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: 05/13/2023] [Accepted: 07/25/2023] [Indexed: 08/31/2023] Open
Abstract
Introduction Mitotane, the only drug approved by the Food and Drug Administration (FDA) for the treatment of adrenocortical carcinoma, is associated with several side effects including neurotoxicity. The aim of our study is to investigate the relationship between mitotane plasma levels and neurological toxicity. Methods We have considered five patients affected by adrenocortical carcinoma treated with mitotane. The neurological assessment included a neurological examination, an electroencephalogram, event-related potentials (P300), and a neuropsychological assessment. All of the patients were first considered at the onset of symptoms of neurotoxicity or when mitotanemia levels were above 18 mg/L, for the second time at mitotanemia normalization and subsequently at its further increase, or in case of persistent neurological abnormalities, some months after normalization. Results At the first neurotoxicity, four patients showed impaired neurological examination, electroencephalogram, and P300; three patients had impaired neuropsychological assessment; one patient, only P300. At mitotanemia normalization, the neurological examination became normal in all patients and electroencephalogram normalized in one patient, improved in another one, continuing to be altered in the other three. P300 latency and neuropsychological assessment normalized in two patients and persisted altered in the patient experiencing long-term mitotane toxicity. At the third evaluation, in the patient with prolonged mitotane toxicity, the normal mitotanemia in the previous 9 months restored P300 and improved the electroencephalogram but not the neuropsychological assessment. In the two patients experiencing a further rise of mitotanemia, neurological examination was normal but P300 and electroencephalogram were altered. Conclusion The results of our study highlighted the presence of neurophysiological and neuropsychological abnormalities associated with mitotane values above 18 mg/L.
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Affiliation(s)
- Marilda Mormando
- Oncological Endocrinology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Edvina Galiè
- Neuro-oncology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Marta Bianchini
- Oncological Endocrinology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Rosa Lauretta
- Oncological Endocrinology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Giulia Puliani
- Oncological Endocrinology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Antonio Tanzilli
- Neuro-oncology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Umberto Anceschi
- Department of Urology, IRCCS, Regina Elena National Cancer Institute, Rome, Italy
| | - Giuseppe Simone
- Department of Urology, IRCCS, Regina Elena National Cancer Institute, Rome, Italy
| | - Gianluca Petreri
- Neuro-oncology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Giuliana Graziano
- Neuro-oncology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Andrea Pace
- Neuro-oncology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Marialuisa Appetecchia
- Oncological Endocrinology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
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Shivaraja TR, Remli R, Kamal N, Wan Zaidi WA, Chellappan K. Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:3654. [PMID: 37050713 PMCID: PMC10098757 DOI: 10.3390/s23073654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.
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Affiliation(s)
- Theeban Raj Shivaraja
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Rabani Remli
- Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
- Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
| | - Noorfazila Kamal
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Wan Asyraf Wan Zaidi
- Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
- Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Cheras 56000, Malaysia
| | - Kalaivani Chellappan
- Department of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
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10
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Luján MÁ, Mateo Sotos J, Torres A, Santos JL, Quevedo O, Borja AL. Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions. J Med Biol Eng 2022; 42:853-859. [DOI: 10.1007/s40846-022-00758-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022]
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11
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Amiri M, Fisher PM, Raimondo F, Sidaros A, Cacic Hribljan M, Othman MH, Zibrandtsen I, Albrechtsen SS, Bergdal O, Hansen AE, Hassager C, Højgaard JLS, Jakobsen EW, Jensen HR, Møller J, Nersesjan V, Nikolic M, Olsen MH, Sigurdsson ST, Sitt JD, Sølling C, Welling KL, Willumsen LM, Hauerberg J, Larsen VA, Fabricius M, Knudsen GM, Kjaergaard J, Møller K, Kondziella D. Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study. Brain 2022; 146:50-64. [PMID: 36097353 PMCID: PMC9825454 DOI: 10.1093/brain/awac335] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/25/2022] [Accepted: 08/14/2022] [Indexed: 01/15/2023] Open
Abstract
Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.
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Affiliation(s)
| | | | | | - Annette Sidaros
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melita Cacic Hribljan
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Marwan H Othman
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Ivan Zibrandtsen
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Simon S Albrechtsen
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Ove Bergdal
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Hassager
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark,Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Joan Lilja S Højgaard
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Helene Ravnholt Jensen
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jacob Møller
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Vardan Nersesjan
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Copenhagen University Hospital, Copenhagen, Denmark
| | - Miki Nikolic
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Markus Harboe Olsen
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Sigurdur Thor Sigurdsson
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jacobo D Sitt
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Christine Sølling
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Karen Lise Welling
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Lisette M Willumsen
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - John Hauerberg
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Vibeke Andrée Larsen
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Martin Fabricius
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jesper Kjaergaard
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark,Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Kirsten Møller
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark,Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Daniel Kondziella
- Correspondence to: Daniel Kondziella, MD, MSc, PhD FEBN Department of Neurology Copenhagen University Hospital, Rigshospitalet Blegdamsvej 9, DK-2100 Copenhagen E-mail:
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12
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qEEG Analysis in the Diagnosis of Alzheimer’s Disease: A Comparison of Functional Connectivity and Spectral Analysis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both quantitative methods were applied on an EEG dataset including 20 diagnosed AD patients and 20 age-matched healthy controls (HC). The obtained results showed an advantage of the functional connectivity analysis when compared to the spectral analysis; while the latter could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting a ‘phase-locked’ state in AD-affected brains. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized to the centro-parietal and centro-temporal areas in the theta frequency band (4–8 Hz). This study applies a neural metric for Alzheimer’s detection from a data science perspective rather than from a neuroscience one and shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.
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13
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6343. [PMID: 34640663 PMCID: PMC8512967 DOI: 10.3390/s21196343] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022]
Abstract
As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
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Affiliation(s)
- Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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14
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Hasan TF, Tatum WO. When should we obtain a routine EEG while managing people with epilepsy? Epilepsy Behav Rep 2021; 16:100454. [PMID: 34041475 PMCID: PMC8141667 DOI: 10.1016/j.ebr.2021.100454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/24/2021] [Accepted: 04/22/2021] [Indexed: 11/30/2022] Open
Abstract
More than eight decades after its discovery, routine electroencephalogram (EEG) remains a safe, noninvasive, inexpensive, bedside test of neurological function. Knowing when a routine EEG should be obtained while managing people with epilepsy is a critical aspect of optimal care. Despite advances in neuroimaging techniques that aid diagnosis of structural lesions in the central nervous system, EEG continues to provide critical diagnostic evidence with implications on treatment. A routine EEG performed after a first unprovoked seizure can support a clinical diagnosis of epilepsy and differentiate those without epilepsy, classify an epilepsy syndrome to impart prognosis, and characterize seizures for antiseizure management. Despite a current viral pandemic, EEG services continue, and the value of routine EEG is unchanged.
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Affiliation(s)
- Tasneem F. Hasan
- Department of Neurology, Ochsner Louisiana State University Health Sciences Center, Shreveport, LA, United States
| | - William O. Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
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15
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Li Q, Gao J, Huang Q, Wu Y, Xu B. Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent. Front Bioeng Biotechnol 2020; 8:1006. [PMID: 33015003 PMCID: PMC7506120 DOI: 10.3389/fbioe.2020.01006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/31/2020] [Indexed: 11/23/2022] Open
Abstract
Epileptiform discharges are of fundamental importance in understanding the physiology of epilepsy. To aid in the clinical diagnosis, classification, prognosis, and treatment of epilepsy, it is important to develop automated computer programs to distinguish epileptiform discharges from normal electroencephalogram (EEG). This is a challenging task as clinically used scalp EEG often contains a lot of noise and motion artifacts. The challenge is even greater if one wishes to develop explainable rather than black-box based approaches. To take on this challenge, we propose to use a multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE). We analyzed 640 multi-channel EEG segments, each 4 s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We found that features from SDLE were very effective in distinguishing epileptiform discharges from normal EEG. Using Random Forest Classifier (RF) and Support Vector Machines (SVM), the proposed approach with different features from SDLE robustly achieves an accuracy exceeding 99% in distinguishing epileptiform discharges from normal control ones. A single parameter, which is the ratio of the spectral energy of EEG signals and the SDLE and quantifies the regularity or predictability of the EEG signals, is introduced to better understand the high accuracy in the classification. It is found that this regularity is considerably greater for epileptiform discharges than for normal controls. Robustly having high accuracy in distinguishing epileptiform discharges from normal controls irrespective of which classification scheme being used, the proposed approach has the potential to be used widely in a clinical setting.
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Affiliation(s)
- Qiong Li
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jianbo Gao
- Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China.,Institute of Automation, Chinese Academy of Sciences, Beijing, China.,International College, Guangxi University, Nanning, China
| | - Qi Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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16
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Li Q, Gao J, Zhang Z, Huang Q, Wu Y, Xu B. Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective. Front Physiol 2020; 11:828. [PMID: 32903770 PMCID: PMC7438848 DOI: 10.3389/fphys.2020.00828] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 06/22/2020] [Indexed: 01/03/2023] Open
Abstract
Epilepsy is one of the most common disorders of the brain. Clinically, to corroborate an epileptic seizure-like symptom and to find the seizure localization, electroencephalogram (EEG) data are often visually examined by a clinical doctor to detect the presence of epileptiform discharges. Epileptiform discharges are transient waveforms lasting for several tens to hundreds of milliseconds and are mainly divided into seven types. It is important to develop systematic approaches to accurately distinguish these waveforms from normal control ones. This is a difficult task if one wishes to develop first principle rather than black-box based approaches, since clinically used scalp EEGs usually contain a lot of noise and artifacts. To solve this problem, we analyzed 640 multi-channel EEG segments, each 4s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We have proposed two approaches for distinguishing epileptiform discharges from normal EEGs. The first method is based on Signal Range and EEGs' long range correlation properties characterized by the Hurst parameter H extracted by applying adaptive fractal analysis (AFA), which can also maximally suppress the effects of noise and various kinds of artifacts. Our second method is based on networks constructed from three aspects of the scalp EEG signals, the Signal Range, the energy of the alpha wave component, and EEG's long range correlation properties. The networks are further analyzed using singular value decomposition (SVD). The square of the first singular value from SVD is used to construct features to distinguish epileptiform discharges from normal controls. Using Random Forest Classifier (RF), our approaches can achieve very high accuracy in distinguishing epileptiform discharges from normal control ones, and thus are very promising to be used clinically. The network-based approach is also used to infer the localizations of each type of epileptiform discharges, and it is found that the sub-networks representing the most likely location of each type of epileptiform discharges are different among the seven types of epileptiform discharges.
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Affiliation(s)
- Qiong Li
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jianbo Gao
- Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- International College, Guangxi University, Nanning, Guangxi, China
| | - Ziwen Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Qi Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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17
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Acharya VJ, Acharya JN. Localization with high-density EEG: Complexity of analysis versus accuracy. Clin Neurophysiol Pract 2019; 5:10-11. [PMID: 31889731 PMCID: PMC6931101 DOI: 10.1016/j.cnp.2019.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 11/08/2019] [Indexed: 11/21/2022] Open
Affiliation(s)
| | - Jayant N. Acharya
- Department of Neurology, Penn State University Hershey Medical Center, Hershey, PA USA
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