Auswahl der wissenschaftlichen Literatur zum Thema „Epileptic seizures detection“
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Zeitschriftenartikel zum Thema "Epileptic seizures detection"
Sharmila, Ashok, und Purusothaman Geethanjali. „A review on the pattern detection methods for epilepsy seizure detection from EEG signals“. Biomedical Engineering / Biomedizinische Technik 64, Nr. 5 (25.09.2019): 507–17. http://dx.doi.org/10.1515/bmt-2017-0233.
Der volle Inhalt der QuelleShoeibi, Afshin, Marjane Khodatars, Navid Ghassemi, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Maryam Panahiazar et al. „Epileptic Seizures Detection Using Deep Learning Techniques: A Review“. International Journal of Environmental Research and Public Health 18, Nr. 11 (27.05.2021): 5780. http://dx.doi.org/10.3390/ijerph18115780.
Der volle Inhalt der QuelleHashem Attia, Atef, und Ashraf Mahroos Said. „Brain seizures detection using machine learning classifiers based on electroencephalography signals: a comparative study“. Indonesian Journal of Electrical Engineering and Computer Science 27, Nr. 2 (01.08.2022): 803. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp803-810.
Der volle Inhalt der QuelleDhar, Puja, Vijay Kumar Garg und Mohammad Anisur Rahman. „Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals“. Journal of Healthcare Engineering 2022 (16.03.2022): 1–14. http://dx.doi.org/10.1155/2022/3491828.
Der volle Inhalt der QuelleSaranya, D., und A. Bharathi. „Automatic detection of epileptic seizure using machine learning-based IANFIS-LightGBM system“. Journal of Intelligent & Fuzzy Systems 46, Nr. 1 (10.01.2024): 2463–82. http://dx.doi.org/10.3233/jifs-233430.
Der volle Inhalt der QuellePrasanna, J., M. S. P. Subathra, Mazin Abed Mohammed, Robertas Damaševičius, Nanjappan Jothiraj Sairamya und S. Thomas George. „Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey“. Journal of Personalized Medicine 11, Nr. 10 (15.10.2021): 1028. http://dx.doi.org/10.3390/jpm11101028.
Der volle Inhalt der QuelleMansouri, Amirsalar, Sanjay P. Singh und Khalid Sayood. „Online EEG Seizure Detection and Localization“. Algorithms 12, Nr. 9 (23.08.2019): 176. http://dx.doi.org/10.3390/a12090176.
Der volle Inhalt der QuelleCogan, Diana, Javad Birjandtalab, Mehrdad Nourani, Jay Harvey und Venkatesh Nagaraddi. „Multi-Biosignal Analysis for Epileptic Seizure Monitoring“. International Journal of Neural Systems 27, Nr. 01 (08.11.2016): 1650031. http://dx.doi.org/10.1142/s0129065716500313.
Der volle Inhalt der QuelleVijay Kakade, Meenal, Chandrakant J. Gaikwad und Vijay R. Dahake. „Epileptic Seizure Detection Using Artifact Reduction and HOS Features of WPD“. ITM Web of Conferences 32 (2020): 02008. http://dx.doi.org/10.1051/itmconf/20203202008.
Der volle Inhalt der QuelleEt. al., Nazia Parveen,. „Higher-Order Phase-Space Reconstruction for Detection of Epileptic Electroencephalogram“. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, Nr. 2 (10.04.2021): 2533–39. http://dx.doi.org/10.17762/turcomat.v12i2.2202.
Der volle Inhalt der QuelleDissertationen zum Thema "Epileptic seizures detection"
McGroggan, N. „Neutral network detection of epileptic seizures in the electroencephalogram“. Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249426.
Der volle Inhalt der QuelleValko, Andras, und Antoine Homsi. „Predictive detection of epileptic seizures in EEG for reactive care“. Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15078.
Der volle Inhalt der QuellePISANO, BARBARA. „Machine Learning Techniques for Detection of Nocturnal Epileptic Seizures from Electroencephalographic Signals“. Doctoral thesis, Università degli Studi di Cagliari, 2018. http://hdl.handle.net/11584/255953.
Der volle Inhalt der QuelleFan, Xiaoya. „Dynamics underlying epileptic seizures: insights from a neural mass model“. Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/279546/6/contratXF.pdf.
Der volle Inhalt der QuelleDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Shahidi, Zandi Ali. „Scalp EEG quantitative analysis : automated real-time detection and prediction of epileptic seizures“. Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42748.
Der volle Inhalt der QuelleGheryani, Mostafa. „Epileptic seizure and anomaly detection in internet of medical things“. Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5211.
Der volle Inhalt der QuelleThe goal of my PhD is to investigate the characteristics of inertial and physiological signals via IoMT systems generated by epileptic seizure and to develop an algorithm to detect the seizure. The focus of the algorithms lies in nocturnal seizures where the risk of SUDEP is high because the patients are unsupervised while sleeping. In chapter III analysis we propose an IoMT platform for seizure detection. The proposed framework approach starts by deriving the RMS for ACM and Gyro, followed by the normalization of whole signals (ACM, Gyro and EMG) in the same range, and aggregate all into one signal. The chart’s control with its upper and lower limits are derived in the training phase and used to detect abnormal seizures and to raise an alarm. In chapter IV Our proposed algorithm is based on deriving instantaneous power measurements in a sliding window containing 3D ACM or 3D Gyro or EMG. The residual between forecasted and measured power is used as input for the detection algorithm based on Shewhart Control Chart (SCC). When the difference between forecasted and derived power exceeds chart limits [lower, upper] for several consecutive slots, an alarm is raised. Our proposed approach provides low FAR (4%) and sensitivity of 97%. In Chapter V our proposed method starts by reducing the dimension of collected data using RMS to derive one signal from 3D ACM and one signal from 3D Gyro. With the derived 3 collected signals (ACM, Gyro and EMG), we apply VTP to derive one signal used as input for anomaly detection mechanism. The robust version of z-score is applied on the resulting product signal to detect deviations associated with seizures before raising an alarm. Our experimental results show that our proposed approach is robust against nocturnal movements and achieves a high level of detection accuracy with low false alarm rate. Afterward, we compare the performance of our approach with the zero-crossings method calculated from sEMG. Our approach shows that the detection accuracy using VTP outperforms zero-crossing count over an overlapping sliding window of 1 second. In chapter VI, we propose an approach using the IoMT devices to acquire EMG, ACM and Gyro data and to transmit the measurements to a LPU for processing. When the LPU detects abnormal changes in the measurements, it raises an alarm for assistant. Our proposed approach uses SVM with reject option to distinguish seizures from normal daily life activity. Features presenting physiological changes of muscular activity and inertial data were extracted in LPU and are used as input for the detection algorithm. The reject option in SVM is used to enhance the reliability of the monitoring system and to reduce FAR, where the user is notified and can discard the alarm in his smartphone in the absence of seizure. The conducted experiments proved that our proposed approach could achieve a good accuracy with only 4% of false alarm rate. Finally, since we are using IoMT sensors, which are susceptible to data security issues. We proposed a solution to prevent Man in the Middle (MitM) attack, which can identify healthcare emergencies of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this chapter, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from Received Signal Strength Indication (RSSI). Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%
McNally, Kelly A. „Application of Signal Detection Theory to Verbal Memory Testing for the Differential Diagnosis of Psychogenic Nonepileptic and Epileptic Seizures“. University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178883120.
Der volle Inhalt der QuelleTruong, Nhan Duy. „Epileptic Seizure Detection and Forecasting Ecosystems“. Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21932.
Der volle Inhalt der QuelleRamachandran, Ganesan. „Comparison of algorithms for epileptic seizure detection“. [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000597.
Der volle Inhalt der QuelleLiu, Hui. „Online automatic epileptic seizure detection from electroencephalogram (EEG)“. [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
Der volle Inhalt der QuelleBücher zum Thema "Epileptic seizures detection"
1959-, Mareels Iven, und Cook Mark 1960-, Hrsg. Epileptic seizures and the EEG: Measurement, models, detection, and prediction. Boca Raton: Taylor & Francis, 2010.
Den vollen Inhalt der Quelle findenVarsavsky, Andrea. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis, 2011.
Den vollen Inhalt der Quelle findenCook, Mark, Iven Mareels und Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.
Den vollen Inhalt der Quelle findenCook, Mark, Iven Mareels und Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.
Den vollen Inhalt der Quelle findenCook, Mark, Iven Mareels und Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.
Den vollen Inhalt der Quelle findenCook, Mark, Iven Mareels und Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.
Den vollen Inhalt der Quelle findenEEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier, 2019. http://dx.doi.org/10.1016/c2018-0-01888-5.
Der volle Inhalt der QuelleDehuri, Satchidananda, Alok Kumar Jagadev, Shruti Mishra und Sandeep Kumar Satapathy. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier Science & Technology, 2019.
Den vollen Inhalt der Quelle findenDehuri, Satchidananda, Alok Kumar Jagadev, Shruti Mishra und Sandeep Kumar Satapathy. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier Science & Technology Books, 2019.
Den vollen Inhalt der Quelle findenVespa, Paul M. Electroencephalogram monitoring in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0221.
Der volle Inhalt der QuelleBuchteile zum Thema "Epileptic seizures detection"
Sharma, Ayushi, und Sandeep Paul. „Epileptic Seizures Detection“. In Lecture Notes in Mechanical Engineering, 309–14. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8025-3_31.
Der volle Inhalt der QuelleHolonec, R., S. Vlad und L. Rapolti. „Application for Detection of Epileptic Seizures“. In 6th International Conference on Advancements of Medicine and Health Care through Technology; 17–20 October 2018, Cluj-Napoca, Romania, 91–96. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6207-1_15.
Der volle Inhalt der QuelleCarrión, Salvador, Álvaro López-Chilet, Javier Martínez-Bernia, Joan Coll-Alonso, Daniel Chorro-Juan und Jon Ander Gómez. „Automatic Detection of Epileptic Seizures with Recurrent and Convolutional Neural Networks“. In Lecture Notes in Computer Science, 522–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_46.
Der volle Inhalt der Quellede Bruijne, G. R., P. C. W. Sommen und R. M. Aarts. „Detection of Epileptic Seizures Through Audio Classification“. In IFMBE Proceedings, 1450–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_344.
Der volle Inhalt der QuellePanigrahi, Narayan, und Saraju P. Mohanty. „Detection of Epileptic Seizures from EEG Data“. In Brain Computer Interface, 175–85. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003241386-12.
Der volle Inhalt der QuelleMoldovan, Dorin. „Scalable Hypothesis Tests for Detection of Epileptic Seizures“. In Computational Statistics and Mathematical Modeling Methods in Intelligent Systems, 157–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31362-3_16.
Der volle Inhalt der QuelleNageshwar, V., P. Venkateswara Rao, C. Sarika, K. Manusha und Y. Deepthi. „Detection of Epileptic Seizures from Logistic Model Trees“. In Atlantis Highlights in Computer Sciences, 371–82. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-314-6_36.
Der volle Inhalt der QuelleMehta, Deval, Shobi Sivathamboo, Hugh Simpson, Patrick Kwan, Terence O’Brien und Zongyuan Ge. „Privacy-Preserving Early Detection of Epileptic Seizures in Videos“. In Lecture Notes in Computer Science, 210–19. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43904-9_21.
Der volle Inhalt der QuelleSiuly, Siuly, Yan Li und Yanchun Zhang. „A Novel Clustering Technique for the Detection of Epileptic Seizures“. In Health Information Science, 83–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47653-7_5.
Der volle Inhalt der QuellePatan, Krzysztof, und Grzegorz Rutkowski. „Detection of Epileptic Seizures via Deep Long Short-Term Memory“. In Advances in Intelligent Systems and Computing, 166–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29885-2_15.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Epileptic seizures detection"
Mirzaei, Ahmad, Ahmad Ayatollahi, Parisa Gifani und Leili Salehi. „Spectral Entropy for Epileptic Seizures Detection“. In 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2010). IEEE, 2010. http://dx.doi.org/10.1109/cicsyn.2010.84.
Der volle Inhalt der QuelleGupta, Sarthak, Siddhant Bagga, Vikas Maheshkar und M. P. S. Bhatia. „Detection of Epileptic Seizures using EEG Signals“. In 2020 International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2020. http://dx.doi.org/10.1109/aisp48273.2020.9073157.
Der volle Inhalt der QuelleCuppens, Kris, Bart Vanrumste, Berten Ceulemans, Lieven Lagae und Sabine Van Huffel. „Detection of Epileptic Seizures Using Video Data“. In 2010 6th International Conference on Intelligent Environments (IE). IEEE, 2010. http://dx.doi.org/10.1109/ie.2010.77.
Der volle Inhalt der QuelleSharma, Swati, und Arjun Arora. „Detection of Epileptic Seizures using Machine Learning“. In 2022 5th International Conference on Advances in Science and Technology (ICAST). IEEE, 2022. http://dx.doi.org/10.1109/icast55766.2022.10039516.
Der volle Inhalt der QuelleKusmakar, Shitanshu, Chandan K. Karmakar, Bernard Yan, Terence J. O'Brien, Ramanathan Muthuganapathy und Marimuthu Palaniswami. „Onset Detection of Epileptic Seizures From Accelerometry Signal“. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8513669.
Der volle Inhalt der QuellePadma, Tatiparti, und Ch Usha Kumari. „Sudden Fall Detection and Protection for Epileptic Seizures“. In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE). IEEE, 2018. http://dx.doi.org/10.1109/icrieece44171.2018.9009317.
Der volle Inhalt der QuelleGupta, Surbhi, Mustafa Sameer und Neeraj Mohan. „Detection of Epileptic Seizures using Convolutional Neural Network“. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2021. http://dx.doi.org/10.1109/esci50559.2021.9396983.
Der volle Inhalt der QuelleAldana, Yissel Rodriguez, Borbala Hunyadi, Enrique J. Maranon Reyes, Valia Rodriguez Rodriguez und Sabine Van Huffel. „Nonconvulsive epileptic seizures detection using multiway data analysis“. In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081629.
Der volle Inhalt der QuelleNaser, Zaman Gheni, und Raid Luaibi Lafta. „EEG and Fractal Dimension for Epileptic Seizures Detection“. In 2023 Al-Sadiq International Conference on Communication and Information Technology (AICCIT). IEEE, 2023. http://dx.doi.org/10.1109/aiccit57614.2023.10218011.
Der volle Inhalt der QuelleReyes, C. F., T. J. Contreras, B. Tovar, L. I. Garay und M. A. Silva. „Detection of absence epileptic seizures using support vector machine“. In 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2013. http://dx.doi.org/10.1109/iceee.2013.6676057.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Epileptic seizures detection"
Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor und Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), Dezember 2021. http://dx.doi.org/10.21079/11681/42562.
Der volle Inhalt der QuelleElarton, J., und K. Koepsel. Epileptic Seizure Detection & Warning Device. Office of Scientific and Technical Information (OSTI), Juni 1999. http://dx.doi.org/10.2172/7856.
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