Prediction of parkinson's disease tremor onset using a radial basis function neural network based on particle swarm optimization

International Journal of Neural Systems

Volumn 20, Issue 2, 2010, Pages 109-116

  Wu, Defeng ^a,b   Warwick, Kevin ^b   Ma, Zi ^a   Gasson, Mark N ^b   Burgess, Jonathan G ^b   Pan, Song ^b   Aziz, Tipu Z ^c  

^a DALIAN MARITIME UNIVERSITY   (China)

^b UNIVERSITY OF READING   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Deep brain stimulation; Parkinson's disease; Particle swarm optimization; Radial basis function neural network

Indexed keywords

BRAIN AREAS; BRAIN ELECTRODES; COMPUTATIONAL OVERHEADS; DEEP BRAIN STIMULATION; DETECTION ACCURACY; ELECTRICAL ACTIVITIES; ELECTROMYOGRAPHIC SIGNAL; HUMAN SUBJECTS; IMPLANTED BATTERY; LIFE SPAN; LOCAL FIELD POTENTIALS; ON-DEMAND; PARKINSON'S DISEASE; PERFORMANCE COMPARISON; POWER DRAW; RADIAL BASIS FUNCTION NEURAL NETWORKS; STIMULATION ELECTRODES; SUBTHALAMIC NUCLEUS;

ATTITUDE CONTROL; BRAIN; DISEASES; ELECTRODES; FORECASTING; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS;

PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN DEPTH STIMULATION; ELECTROMYOGRAPHY; EVOKED MUSCLE RESPONSE; FOREARM; FUZZY LOGIC; HUMAN; INNERVATION; METHODOLOGY; PARKINSON DISEASE; PERCEPTIVE DISCRIMINATION; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; SPECTROSCOPY; SUBTHALAMIC NUCLEUS; TREMOR;

ALGORITHMS; DEEP BRAIN STIMULATION; ELECTROMYOGRAPHY; EVOKED POTENTIALS, MOTOR; FOREARM; FUZZY LOGIC; HUMANS; NEURAL NETWORKS (COMPUTER); PARKINSON DISEASE; PRINCIPAL COMPONENT ANALYSIS; SIGNAL DETECTION, PSYCHOLOGICAL; SPECTRUM ANALYSIS; SUBTHALAMIC NUCLEUS; TREMOR;

EID: 77951553244     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065710002292     Document Type: Article

References (19)

1. C. Hamani, D. Andrade, M. Hodaie, R. Wennberg, and A. Lozano, Deep brain stimulation for the treatment of epilepsy, International Journal of Neural Systems 19 (3) (2009) 213-226.
2. S. Pan, K. Warwick, J. Burgess, M. N. Gasson, S. Y. Wang, T. Z. Aziz and J. F. Stein, Prediction of Parkinson's disease tremor onset with artificial neural networks, Proc. 5th IASTED International Conference on Biomedical Engineering (2007), pp. 341-345.
3. M. N. Gasson, T. Aziz, K. Warwick, S. Wang and J. Stein, Towards a demand driven deep-brain stimulator for the treatment of movement disorders, Proc. 3rd IEE International Seminar on Medical Applications of Signal Processing, London (2005), pp. 83-86.
4. F. Takeda, Y. Shiraishi and T. Sanechika, Alarm sound classification system of oxygen concentrator by using neural network, International Journal of Innovative Computing, Information and Control 3 (1) (2007) 211-222.
5. B. Abbasi, A neural network applied to estimate process capability of non-normal processes, Expert Systems with Applications 36 (2) Part 2 (2009) 3093-3100.
6. A. Işeri and B. Karlik, An artificial neural networks approach on automobile pricing Expert Systems with Applications 36 (2) Part 1 (2009) 2155-2160.
7. P. Dhanalakshmi, S. Palanivel and V. Ramalingam, Classification of audio signals using SVM and RBFNN, Expert Systems with Applications 36 (3) Part 2 (2009) 6069-6075.
8. S. Mehrabi, M. Maghsoudloo, H. Arabalibeik, R. Noormand and Y. Nozari, Application of multilayer perceptron and radial basis function neural networks in differentiating between chronic obstructive pulmonary and congestive heart failure diseases, Expert Systems with Applications 36 (3) Part 2 (2009) 6956-6959.
9. T. S. Subashini, V. Ramalingam and S. Palanivel, Breast mass classification based on cytological patterns using RBFNN and SVM, Expert Systems with Applications 36 (3) Part 1 (2009) 5284-5290.
10. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection, IEEE Transactions on Biomedical Engineering 55 (2) (2008) 512-518.
11. H. Adeli and A. Karim, Fuzzy-wavelet RBFNN model for freeway incident detection, Journal of Transportation Engineering 126 (6) (2000) 464-471.
12. A. Karim and H. Adeli, Comparison of the fuzzy - wavelet RBFNN freeway incident detection model with the California Algorithm, Journal of Transportation Engineering 128 (1) (2002) 21-30. (Pubitemid 34096524)
13. A. Karim and H. Adeli, Radial basis function neural network for work zone capacity and queue estimation, Journal of Transportation Engineering 129 (5) (2003) 494-503. (Pubitemid 37164326)
14. R. V. Mayorga and J. Carrera, A radial basis function network approach for the computational of inverse continuous time variant functions, International Journal of Neural Systems 17 (3) (2007) 149-160. (Pubitemid 47117606)
15. P. Anand, B. V. N. Siva Prasad and C. Venkateswarlu, Modeling and optimization of a pharmaceutical formulation system using radial basis function network, International Journal of Neural Systems 19 (2) (2009) 127-136.
16. R. Savitha, S. Suresh and N. Sundararajan, A fully complex-valued radial basis function network and its learning algorithm, International Journal of Neural Systems 19 (4) (2009) 253-267.
17. P. A. Meehan and P. A. Bellette, Chaotic signal analysis of Parkinson's disease STN brain signals, CHAOS 2008 International Conference (Chania, Crete, Greece, 3-6 June 2008), pp. 232-241.
18. D. S. Broomhead and D. Lowe, Multi-variable functional interpolation and adaptive networks, Complex Systems 2 (3) (1988) 269-303.
19. J. Kennedy and R. Eberhart, Particle swarm optimization, IEEE International Conference on Neural Networks (1995) 1942-1948.