Intraoperative neurophysiological monitoring for deep brain stimulation : principles, practice and cases / / Erwin B. Montgomery

• Record Nr.: UNINA9910809533503321
• Autore: Montgomery Erwin B.
• Pubbl/distr/stampa: Oxford, England : , : Oxford University Press, , 2014; ©2014
• ISBN: 0-19-935102-3; 0-19-938938-1; 0-19-935101-5
• Descrizione fisica: 1 online resource (417 p.)
• Disciplina: 616.8/0475
• Soggetti: Neurophysiologic monitoring
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Cover; Intraoperative Neurophysiological Monitoring for Deep Brain Stimulation; Copyright; Dedication; Contents; Preface; 1 Importance of intraoperative neurophysiological monitoring; 2 Preparations for intraoperative neurophysiological monitoring; 3 Basic concepts of electricity and electronics; 4 Electrode recordings: Neurophysiology; 5 Microelectrode and semi-microelectrode recordings: Electronics; 6 Noise and artifact; 7 Microelectrode recordings: Neuronal characteristics and behavioral correlations; 8 Microstimulation and macrostimulation; 9 The subthalamic nucleus; 10 The globus pallidus interna nucleus11 The ventral intermediate nucleus of the thalamus; 12 Clinical assessments during intraoperative neurophysiological monitoring; 13 Cases; 14 Future intraoperative neurophysiological monitoring; Appendix A Subthalamic nucleus deep brain stimulation algorithm; Appendix B Ventral intermediate thalamic deep brain stimulation algorithm; Appendix C Globus pallidus interna deep brain stimulation algorithm; Appendix D Microelectrode recording form for subthalamic nucleus deep brain stimulation; Appendix E Microelectrode recording form for globus pallidus internaAppendix F Microelectrode recording form for ventral intermediate thalamus; Appendix G Intraoperative macrostimulation for ; clinical effect in Parkinson's disease; Appendix H Intraoperative macrostimulation for clinical effect in tremor disorders; Appendix I Intraoperative macrostimulation for clinical effect on dystonia; Appendix J Intraoperative macrostimulation for clinical effect on tics; Appendix K Intraoperative macrostimulation for clinical effect on dyskinesia; Index
• Sommario/riassunto: Thorough understanding of electricity, electronics, biophysics, neurophysiology, and neuroanatomy renders more tractable otherwise complex electrophysiologically-based targeting. The textbook integrates these subjects in a single resource. Ultimately, electrophysiological monitoring required controlling the movement of electrons in electronic circuits. Thus, the textbook begins with fundamental discussions of electrons, the forces moving electrons, and the electrical circuits controlling these forces. The forces that allow recording and analysis also permeate the environment producing interfer

Pattern Recognition : 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part IV / / edited by Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal

• Record Nr.: UNISA996630870703316
• Autore: Antonacopoulos Apostolos
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
• ISBN: 9783031781285; 3031781287
• Edizione: [1st ed. 2025.]
• Descrizione fisica: 1 online resource (0 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 15304
• Altri autori (Persone): ChaudhuriSubhasis; ChellappaRama; LiuCheng-Lin; BhattacharyaSaumik; PalUmapada
• Disciplina: 006.37
• Soggetti: Computer vision; Machine learning; Computer Vision; Machine Learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: DeepEMD: A Transformer-based Fast Estimation of the Earth Mover’s Distance -- Equivariant Neural Networks for TEM Virus Images Improves Data Efficiency -- AI Based Story Generation -- Deep learning models for inference on compressed signals with known or unknown measurement matrix -- Training point-based deep learning networks for forest segmentation with synthetic data -- Brain Age Estimation using Self-attention based Convolutional Neural Network -- IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence -- Interpretable Deep Graph-level Clustering: A Prototype-based Approach -- A Saliency-Aware NR-IQA Method by Fusing Distortion Class Information -- A Guided Input Sampling-based Perturbative Approach for Explainable AI in Image-based Application -- Multi-target Attention Dispersion Adversarial Attack against Aerial Object Detector -- Mask-TS Net: Mask Temperature Scaling Uncertainty Calibration for Polyp Segmentation -- Label-expanded Feature Debiasing for Single Domain Generalization -- Infrared and Visible Image Fusion Based on CNN and Transformer Cross-Interaction with Semantic Modulations -- Mining Long Short-Term Evolution Patterns for Temporal Knowledge Graph Reasoning -- Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging -- A Weighted Discrete Wavelet Transform-based Capsule Network for Malware Classification -- Data-driven Spatiotemporal Aware Graph Hybrid-hop Transformer Network for Traffic Flow Forecasting -- Automatic Diagnosis Model of Gastrointestinal Diseases Based on Tongue Images -- TinyConv-PVT: A Deeper Fusion Model of CNN and Transformer for Tiny Dataset -- SCAD-Net: Spatial-Channel Attention and Depth-map Analysis Network for Face Anti-Spoofing -- Next Generation Loss Function for Image Classification -- NAOL: NeRF-Assisted Omnidirectional Localization -- EdgeConvFormer: an unsupervised anomaly detection method for multivariate time series -- Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels -- Hand over face gesture classification with feature driven vision transformer and supervised contrastive learning -- TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering -- GraFix: A Graph Transformer with Fixed Attention based on the WL Kernel -- Multi-Modal Deep Emotion-Cause Pair Extraction for Video Corpus.
• Sommario/riassunto: The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics.