Clinical EEG and Neuroscience最新文献

{"title":"Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia.","authors":"Alexandra Papakonstantinou, Jannis Klemming, Martin Haberecht, Dieter Kunz, Frederik Bes","doi":"10.1177/15500594231175320","DOIUrl":"10.1177/15500594231175320","url":null,"abstract":"<p><p><b><i>Study Objectives.</i></b> To present and evaluate an automatic scoring algorithm for quantification of REM-sleep without atonia (RWA) in patients with REM-sleep behaviour disorder (RBD) based on a generally accepted, well-validated visual scoring method, (\"Montreal\" phasic and tonic) and a recently developed, concise scoring method (Ikelos-RWA). <b><i>Methods.</i></b> Video-polysomnographies of 20 RBD patients (68.2 ± 7.2 years) and 20 control patients with periodic limb movement disorder (65.9 ± 6.7 years) were retrospectively analysed. RWA was estimated from chin electromyogram during REM-sleep. Visual and automated RWA scorings were correlated, and agreement (<i>a</i>) and Cohen's Kappa (<i>k</i>) calculated for 1735 minutes of REM-sleep of the RBD patients. Discrimination performance was evaluated with receiver operating characteristic (ROC) analysis. The algorithm was then applied on the polysomnographies of a cohort of 232 RBD patients (total analysed REM-sleep: 17,219 minutes) and evaluated, while correlating the different output parameters. <b><i>Results.</i></b> Visual and computer-derived RWA scorings correlated significantly (tonic Montreal: <i>r</i>_TM = 0.77; phasic Montreal: r_PM = 0.78; Ikelos-RWA: r_I = 0.97; all <i>p</i> < 0.001) and showed good to excellent Kappa coefficients (<i>k</i>_TM = 0.71; <i>k</i>_PM = 0.79; <i>k</i>_I = 0.77). The ROC analysis showed high sensitivities (95%-100%) and specificities (84%-95%) at the optimal operation points, with area under the curve (AUC) of 0.98, indicating high discriminating capacity. The automatic RWA scorings of 232 patients correlated significantly (<i>r</i>_TM{I} = 0.95; <i>r</i>_PM{I} = 0.91, <i>p</i> < 0.0001). <b><i>Conclusions.</i></b> The presented algorithm is an easy-to-use and valid tool for automatic RWA scoring in patients with RBD and may prove effective for general use being publicly available.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9481335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Age-dependent Electroencephalogram Characteristics During Different Levels of Anesthetic Depth.","authors":"Feixiang Li, Yaoyao Dang, Xuan Zhang, Huimin Chen, Yuechun Lu, Yonghao Yu","doi":"10.1177/15500594221142680","DOIUrl":"10.1177/15500594221142680","url":null,"abstract":"<p><p><b>Objective</b> The monitoring of anesthetic depth based on electroencephalogram derivation is not currently adjusted for age. Here we analyze the influence of age factors on electroencephalogram characteristics. <b>Methods</b> Frontal electroencephalogram recordings were obtained from 80 adults during routine clinical anesthesia. The characteristics of electroencephalogram with age and anesthesia were observed during four kinds of anesthesia. <b>Results</b> The slow wave power, δ power, Bispectral Index (BIS) and approximate entropy can be used to distinguish different states of anesthesia (P < 0.05). In the deep and very deep anesthesia states, δ power decreased with age (P < 0.0001). In the very deep anesthesia state, θ power decreased with age (P < 0.05). In the deep and very deep anesthesia states, α power decreased with age (P = 0.0002). In the light and deep anesthesia states, β power decreased with age (P = 0.003). In the deep anesthesia state, γ power decreased with age (P = 0.002). In the very deep anesthesia state, permutation entropy increased significantly with age (P = 0.0001). In the very deep anesthesia state, BIS value increased with age (P = 0.006). The slow wave power, approximate entropy, and sample entropy did not show age-dependent changes. <b>Conclusions</b> The influence of age should be considered when using BIS and δ power to monitor the depth of anesthesia, while the influence of age should not be considered when using slow wave power and approximate entropy to monitor the depth of anesthesia.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10764811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Clinical Utility of Finding Unexpected Subclinical Spikes Detected by High-Density EEG During Neurodiagnostic Investigations","authors":"Michael March, Omri Bar, Madeline Chadehumbe, Kim Catterall, Mark Mintz","doi":"10.1177/15500594241284090","DOIUrl":"https://doi.org/10.1177/15500594241284090","url":null,"abstract":"This study aimed to analyze the frequency of unexpected subclinical spikes (USCS) in pediatric patients who underwent high-density electroencephalogram (HD-EEG). Of the 4481 successful HD-EEG studies, 18.5% (829) were abnormal, and 49.7% of these abnormal studies showed SCS, of which 64.1% were USCS. USCS were found to be correlated with attention/concentration deficits and executive dysfunction, often accompanied by the dual psychiatric diagnosis of ADHD. MRI revealed abnormal findings in 32.6% of the subjects with USCS, such as abnormal signal or signal hyperintensity in brain parenchyma, temporal or arachnoid cysts, and vascular malformations. Moreover, the USCS group who received neuropsychiatric testing scored lower than the population mean on Full-Scale Intelligence Quotient, Working Memory Index, and Processing Speed Index. This study highlights the potential of USCS as biomarkers that can lead to changes in clinical management and outcomes, provide valuable information about pathophysiological mechanisms, and suggest potential treatment pathways.","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of LORETA Z Score Neurofeedback and Cognitive Rehabilitation on Quality of Life and Response Inhibition in Individuals with Opioid Addiction","authors":"Alireza Faridi, Farhad Taremian, Robert W. Thatcher","doi":"10.1177/15500594241283069","DOIUrl":"https://doi.org/10.1177/15500594241283069","url":null,"abstract":"Background. Previous studies has shown that conventional neurofeedback and cognitive rehabilitation can improve psychological outcomes in people with opioid use disorders. However, the effectiveness of LORETA Z-score neurofeedback (LZNFB) and attention bias modification training on quality of life and inhibitory control of these people has not been investigated yet. LZNFB targets deeper brain structures with higher precision, compared to conventional neurofeedback that typically focuses on surface EEG activity. The present study aims to compare the effect of these two methods on quality of life and response inhibition in men with opioid use disorders under methadone maintenance therapy (MMT). Methods. In this randomized controlled clinical trial with a pre-test, post-test, follow-up design, 30 men with opioid use disorders under MMT were randomly assigned into three groups of LZNFB, attention bias modification training, and control (MMT alone). The LZNFB and Cognitive Rehabilitation groups received 20 and 15 sessions of treatment, respectively. The Persian versions WHO Quality of Life-BREEF questionnaire and the Go/No-Go test were completed by the participants before, immediately after, and one month after interventions. The collected data were analyzed in SPSS v.22 software. Results. Both intervention groups showed a significant improvement in quality-of-life score and a significant reduction in response time at the post-test phase ( P &lt; .05), where LZNFB group showed more improvement in quality of life and more reduction in response inhibition. After one month, the increase in quality of life continued in both groups, while the decrease in response time continued only in the LZNFB group. Conclusion. Both LZNFB and attention bias modification training are effective in improving quality of life and response inhibition of men with OUD under MMT, however, LZNFB is more effective.","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy","authors":"Sinem Zeynep Metin, Çağlar Uyulan, Shams Farhad, Türker Tekin Ergüzel, Ömer Türk, Barış Metin, Önder Çerezci, Nevzat Tarhan","doi":"10.1177/15500594241273181","DOIUrl":"https://doi.org/10.1177/15500594241273181","url":null,"abstract":"Background: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Brain Stimulator (DBS) Artifact in the EEG of a Pediatric Patient.","authors":"Jennifer V Gettings, Robert C Stowe","doi":"10.1177/15500594231194958","DOIUrl":"10.1177/15500594231194958","url":null,"abstract":"<p><p>We report the first case of deep brain stimulator (DBS) artifact in the EEG of a pediatric patient. Our case is a 7-year-old male with bilateral globus pallidus interna (GPi) DBS for whom the EEG recorded a rhythmic 7.5 Hz theta activity on EEG related to DBS artifact. This artifact was also appreciated as a monochromatic invariable frequency band over 7.5 Hz on density spectral array (DSA). This rhythmic artifact may mimic an ictal pattern and should be recognized as artifact in order to avoid unnecessary treatment with anti-seizure medications (ASM).</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10055775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.","authors":"Amira Echtioui, Wassim Zouch, Mohamed Ghorbel, Chokri Mhiri, Habib Hamam","doi":"10.1177/15500594221148285","DOIUrl":"10.1177/15500594221148285","url":null,"abstract":"<p><p>Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with the device by tackling a sequence of motor imagery tasks. However, the extracting user-specific features and increasing the accuracy of the classifier remain as difficult tasks in MI-based BCI. In this work, we propose a new method using artificial neural network (ANN) enhancing the performance of the motor imagery classification. Feature extraction techniques, like time domain parameters, band power features, signal power features, and wavelet packet decomposition (WPD), are studied and compared. Four classification algorithms are implemented which are Quadratic Discriminant Analysis, k-Nearest Neighbors, Linear Discriminant Analysis, and proposed ANN architecture. We added Batch Normalization layers to the proposed ANN architecture to improve the learning time and accuracy of the neural network. These layers also alleviate the effect of weight initialization and the addition of a regularization effect on the network. Our proposed method using ANN architecture achieves 0.5545 of kappa and 58.42% of accuracy on the BCI Competition IV-2a dataset. Our results show that the modified ANN method, with frequency and spatial features extracted by WPD and Common Spatial Pattern, respectively, offers a better classification compared to other current methods.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10489778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Assessment of Smoking cue Reactivity During a Smoking Cue Exposure Task.","authors":"A M Kroczek, B Schröder, D Rosenbaum, A Mühleck, J Diemer, A Mühlberger, A J Fallgatter, A Batra, A-C Ehlis","doi":"10.1177/15500594221138273","DOIUrl":"10.1177/15500594221138273","url":null,"abstract":"<p><p><i>Background.</i> Cue-reactivity as a characteristic symptom of substance use disorders (SUD) is highly context dependent. Paradigms with high context validity need to be established for the investigation of underlying neurobiological mechanisms. While craving can be assessed by self-report as one aspect of cue-reactivity (CR), the assessment of biological measures such as the autonomous response and EEG promises a holistic perspective including CR at an automatized level. In a multimodal approach, smoking cue exposure (CE) effects on heart rate variability (HRV), EEG frequency power, and craving as well as their interrelation were assessed. This pilot study focused on the validity of CR measurements in a naturalistic CE paradigm. <i>Methods.</i> EEG frequency power, HRV, and craving were assessed during resting state (RS) and smoking CE in smokers (n = 14) and nonsmoking controls (n = 10) to investigate the psychophysiological and subjective reactions to CE. <i>Results.</i> Increased beta power was found only in smokers during CE compared to the control condition. There was an inverse correlation of beta power and maximum craving. Likewise, HRV correlated negatively with maximum smoking urges in smokers immediately after the measurements, without differentiation between CE and control condition. <i>Conclusion.</i> The increased beta power in smokers during CE is discussed as increased inhibitory control related to reduced craving in smokers. Furthermore, increased craving during CE seems to be associated to decreased vagal activity. The multimodal measurements during the CE showed ecological validity to be fundamental for CE assessment in clinical populations to evaluate its predictive value.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11020128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40485241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The EEG Features of Four Cases of Subacute Sclerosing Panencephalitis in North China: SSPE EEG Features.","authors":"Jiang-Tao Wang, Jie Han, Ming-Guang Wang, Xiao-Hua Hou, Zai-Fen Gao, Yan-Feng Zhang, Jian-Min Liang","doi":"10.1177/15500594221137050","DOIUrl":"10.1177/15500594221137050","url":null,"abstract":"<p><p><i>Objective</i>. To analyze the EEG features of four subacute sclerosing panencephalitis cases in North China. <i>Methods</i>. We retrospectively analyzed the EEG features in four patients with subacute sclerosing panencephalitis and 12 patients in control group from North China. <i>Results.</i> The periodic long-interval diffuse discharges were found in all of the four cases with subacute sclerosing panencephalitis. The morphology and component of periodic complexes were varied in different patients and different wakefulness states. Some EEG parameter settings help to identify periodic long-interval diffuse discharges including the slowed sweep speed, decreased sensitivity and reduced number of montages. In each patient with subacute sclerosing panencephalitis, the periodic long-interval diffuse discharges associated with two types of brief episodes (1:1) during awake period were found and none of the patients in the control group had this EEG pattern. The score system based on the periodic discharges and brief episodes also shows that all the patients with SSPE reached score 5 while none of the patients in the control group has a score greater than 3, which suggests that this EEG pattern may have diagnostic value. <i>Conclusions.</i> In subacute sclerosing panencephalitis, the morphology and component of periodic long-interval diffuse discharges were varied in different patients and different wakefulness states. Specific EEG parameter settings help to identify periodic long-interval diffuse discharges. Periodic long-interval diffuse discharges associated with two types of brief episodes (1:1) during awake period may strongly suggest the diagnosis of subacute sclerosing panencephalitis.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40471979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Source Localization and Spectrum Analyzing of EEG in Stuttering State upon Dysfluent Utterances.","authors":"Masoumeh Bayat, Reza Boostani, Malihe Sabeti, Fariba Yadegari, Mohammadreza Pirmoradi, K S Rao, Mohammad Nami","doi":"10.1177/15500594221150638","DOIUrl":"10.1177/15500594221150638","url":null,"abstract":"<p><p><b>Purpose:</b> The present study which addressed adults who stutter (AWS) attempted to investigate power spectral dynamics in the stuttering state by answering the questions using quantitative electroencephalography (qEEG). <b>Method:</b> A 64-channel electroencephalography (EEG) setup was used for data acquisition at 20 AWS. Since the speech, especially stuttering, causes significant noise in the EEG, 2 conditions of speech preparation (SP) and imagined speech (IS) were considered. EEG signals were decomposed into 6 bands. The corresponding sources were localized using the standard low-resolution electromagnetic tomography (sLORETA) tool in both fluent and dysfluent states. <b>Results:</b> Significant differences were noted after analyzing the time-locked EEG signals in fluent and dysfluent utterances. Consistent with previous studies, poor alpha and beta suppression in SP and IS conditions were localized in the left frontotemporal areas in a dysfluent state. This was partly true for the right frontal regions. In the theta range, disfluency was concurrence with increased activation in the left and right motor areas. Increased delta power in the left and right motor areas as well as increased beta2 power over left parietal regions was notable EEG features upon fluent speech. <b>Conclusion:</b> Based on the present findings and those of earlier studies, explaining the neural circuitries involved in stuttering probably requires an examination of the entire frequency spectrum involved in speech.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10520211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}