123 related articles for article (PubMed ID: 10883337)
1. Intracranial pressure processing with artificial neural networks: classification of signal properties.
Mariak Z; Swiercz M; Krejza J; Lewko J; Lyson T
Acta Neurochir (Wien); 2000; 142(4):407-11; discussion 411-2. PubMed ID: 10883337
[TBL] [Abstract][Full Text] [Related]
2. [Analysis of intracranial pressure signals using artificial neural networks].
Mariak Z; Swiercz M; Krejza J; Lewko J; Lyson T
Neurol Neurochir Pol; 2000; 34(6):1209-23. PubMed ID: 11317497
[TBL] [Abstract][Full Text] [Related]
3. Intracranial pressure processing with artificial neural networks: prediction of ICP trends.
Swiercz M; Mariak Z; Krejza J; Lewko J; Szydlik P
Acta Neurochir (Wien); 2000; 142(4):401-6. PubMed ID: 10883336
[TBL] [Abstract][Full Text] [Related]
4. Neural network technique for detecting emergency states in neurosurgical patients.
Swiercz M; Mariak Z; Lewko J; Chojnacki K; Kozlowski A; Piekarski P
Med Biol Eng Comput; 1998 Nov; 36(6):717-22. PubMed ID: 10367462
[TBL] [Abstract][Full Text] [Related]
5. Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.
Yang J; Singh H; Hines EL; Schlaghecken F; Iliescu DD; Leeson MS; Stocks NG
Artif Intell Med; 2012 Jun; 55(2):117-26. PubMed ID: 22503644
[TBL] [Abstract][Full Text] [Related]
6. Classification of EEG signals using neural network and logistic regression.
Subasi A; Erçelebi E
Comput Methods Programs Biomed; 2005 May; 78(2):87-99. PubMed ID: 15848265
[TBL] [Abstract][Full Text] [Related]
7. A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics.
Bhatikar SR; DeGroff C; Mahajan RL
Artif Intell Med; 2005 Mar; 33(3):251-60. PubMed ID: 15811789
[TBL] [Abstract][Full Text] [Related]
8. NNERVE: neural network extraction of repetitive vectors for electromyography--Part I: Algorithm.
Hassoun MH; Wang C; Spitzer AR
IEEE Trans Biomed Eng; 1994 Nov; 41(11):1039-52. PubMed ID: 8001993
[TBL] [Abstract][Full Text] [Related]
9. Intracranial pressure wave morphological classification: automated analysis and clinical validation.
Nucci CG; De Bonis P; Mangiola A; Santini P; Sciandrone M; Risi A; Anile C
Acta Neurochir (Wien); 2016 Mar; 158(3):581-8; discussion 588. PubMed ID: 26743919
[TBL] [Abstract][Full Text] [Related]
10. Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers.
Mavroforakis ME; Georgiou HV; Dimitropoulos N; Cavouras D; Theodoridis S
Artif Intell Med; 2006 Jun; 37(2):145-62. PubMed ID: 16716579
[TBL] [Abstract][Full Text] [Related]
11. A new method for processing of continuous intracranial pressure signals.
Eide PK
Med Eng Phys; 2006 Jul; 28(6):579-87. PubMed ID: 16275153
[TBL] [Abstract][Full Text] [Related]
12. Feature-based classification of myoelectric signals using artificial neural networks.
Gallant PJ; Morin EL; Peppard LE
Med Biol Eng Comput; 1998 Jul; 36(4):485-9. PubMed ID: 10198534
[TBL] [Abstract][Full Text] [Related]
13. Artificial neural network based intracranial pressure mean forecast algorithm for medical decision support.
Zhang F; Feng M; Pan SJ; Loy LY; Guo W; Zhang Z; Chin PL; Guan C; King NK; Ang BT
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():7111-4. PubMed ID: 22255977
[TBL] [Abstract][Full Text] [Related]
14. Lung sound classification using cepstral-based statistical features.
Sengupta N; Sahidullah M; Saha G
Comput Biol Med; 2016 Aug; 75():118-29. PubMed ID: 27286184
[TBL] [Abstract][Full Text] [Related]
15. k-Shape clustering for extracting macro-patterns in intracranial pressure signals.
Martinez-Tejada I; Riedel CS; Juhler M; Andresen M; Wilhjelm JE
Fluids Barriers CNS; 2022 Feb; 19(1):12. PubMed ID: 35123535
[TBL] [Abstract][Full Text] [Related]
16. What can artificial neural networks teach us about neurodegenerative disorders with extrapyramidal features?
Litvan I; DeLeo JM; Hauw JJ; Daniel SE; Jellinger K; McKee A; Dickson D; Horoupian DS; Lantos PL; Tabaton M
Brain; 1996 Jun; 119 ( Pt 3)():831-9. PubMed ID: 8673495
[TBL] [Abstract][Full Text] [Related]
17. An effective approach to classify epileptic EEG signal using local neighbor gradient pattern transformation methods.
Sairamya NJ; Thomas George S; Balakrishnan R; Subathra MSP
Australas Phys Eng Sci Med; 2018 Dec; 41(4):1029-1046. PubMed ID: 30374770
[TBL] [Abstract][Full Text] [Related]
18. Using neural networks for processing biologic signals.
Sabbatini RM
MD Comput; 1996; 13(2):165-72. PubMed ID: 8684280
[TBL] [Abstract][Full Text] [Related]
19. Computer vision-based method for classification of wheat grains using artificial neural network.
Sabanci K; Kayabasi A; Toktas A
J Sci Food Agric; 2017 Jun; 97(8):2588-2593. PubMed ID: 27718230
[TBL] [Abstract][Full Text] [Related]
20. A neural-network-based detection of epilepsy.
Nigam VP; Graupe D
Neurol Res; 2004 Jan; 26(1):55-60. PubMed ID: 14977058
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]