Technologies for assessment of motor disorders in Parkinson’s Disease: A review

Sensors (Switzerland)

Volumn 15, Issue 9, 2015, Pages 21710-21745

  Oung, Qi Wei ^a   Muthusamy, Hariharan ^a   Lee, Hoi Leong ^a   Basah, Shafriza Nisha ^a   Yaacob, Sazali ^b   Sarillee, Mohamed ^a   Lee, Chia Hau ^a  

^a UNIVERSITY MALAYSIA PERLIS   (Malaysia)

^b UNIVERSITI KUALA LUMPUR   (Malaysia)

Author keywords

Audio sensor; Multimodal sensor; Parkinson disease (PD); Unified Parkinson disease rating scale (UPDRS); Wearable sensor

Indexed keywords

DIAGNOSIS; DISEASES; NEURODEGENERATIVE DISEASES;

AUDIO SENSORS; CENTRAL NERVOUS SYSTEMS; COMPARATIVE STUDIES; MULTIMODAL SENSOR; MULTIMODAL SENSOR INTEGRATIONS; MULTIPLE SENSOR FUSION; PARKINSON DISEASE; TECHNOLOGICAL TOOLS;

WEARABLE SENSORS;

ACCELEROMETRY; COMPLICATION; HUMAN; MEDICAL TECHNOLOGY; MOTOR DISORDERS; PARKINSON DISEASE; PATHOPHYSIOLOGY; PHYSIOLOGIC MONITORING; PROCEDURES;

ACCELEROMETRY; BIOMEDICAL TECHNOLOGY; HUMANS; MONITORING, PHYSIOLOGIC; MOTOR DISORDERS; PARKINSON DISEASE;

EID: 84940707766     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150921710     Document Type: Review

References (130)

1. Choudhury, T.; Consolvo, S.; Harrison, B.; Hightower, J.; LaMarca, A.; LeGrand, L.; Rahimi, A.; Rea, A.; Bordello, G.; Hemingway, B. The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Comput. 2008, 7, 32-41.
2. Casale, P.; Pujol, O.; Radeva, P. Human activity recognition from accelerometer data using a wearable device. In Pattern Recognition and Image Analysis; Springer: Berlin, Germany, 2011; pp. 289-296.
3. Kim, E.; Helal, S.; Cook, D. Human activity recognition and pattern discovery. IEEE Pervasive Comput. 2010, 9, 48-53.
4. Lee, H. L.; Shahriman, A. B.; Za'aba, S. K.; Khairunizam, W.; Roohi, S. A.; Zuradzman, M. R. Upper Extremity Vein Graft Monitoring Device after Surgery Procedure: A Preliminary Study. Adv. Mater. Res. 2014, doi: 10. 4028/www. scientific. net/AMR. 925. 656.
5. Lee, H. L.; Shahriman, A. B.; Sazali, Y.; Zuradzman, M. R.; Khairunizam, W.; Ahmad, W.; Zunaidi, I.; Cheng, E. M.; Khadijah, S.; Nisha, S. In vitro evaluation of fingerʼs hemodynamics for vein graft surveillance using electrical bio-impedance method. Aust. J. Basic Appl. Sci. 2014, 8, 350-359.
6. Sim, O. F. Ageing in Malaysia: National Policy and Future Direction; Faculty of Business and Accountancy, University of Malaya: Kuala Lumpur, Malaysian, 2001.
7. De Lau, L. M.; Breteler, M. M. Epidemiology of parkinsonʼs disease. Lancet Neurol. 2006, 5, 525-535.
8. Goodman, L. S. Goodman and Gilmanʼs the Pharmacological Basis of Therapeutics; McGraw-Hill: New York, NY, USA, 1996.
9. Patel, S.; Sherrill, D.; Hughes, R.; Hester, T.; Huggins, N.; Lie-Nemeth, T.; Standaert, D.; Bonato, P. Analysis of the Severity of Dyskinesia in Patients with Parkinsonʼs Disease via Wearable Sensors. In Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA, 3-5 April 2006.
10. Parkinson Centre-Malaysian Parkinson Disease Association. Why is a Parkinson Centre Needed? Available online: http: //www. mpda. org. my/helpparkinsonclub. php (accessed on 5 May 2015).
11. Aarli, J. A.; Dua, T.; Janca, A.; Muscetta, A. Neurological Disorders: Public Health Challenges. World Health Organization: Geneva, Switzerland, 2006.
12. Parkinsonʼs Disease: Hope through research. Available online: http: //www. ninds. nih. gov/disorders/ parkinsons_disease/detail_parkinsons_disease. htm (accessed on 5 May 2015).
13. Elbaz, A.; Bower, J. H.; Maraganore, D. M.; McDonnell, S. K.; Peterson, B. J.; Ahlskog, J. E.; Schaid, D. J.; Rocca, W. A. Risk tables for parkinsonism and parkinsonʼs disease. J. Clin. Epidemiol. 2002, 55, 25-31.
14. Sung, M.; Marci, C.; Pentland, A. Wearable feedback systems for rehabilitation. J. Neuroeng. Rehabil. 2005, 2, doi: 10. 1186/1743-0003-2-17.
15. Van den Eeden, S. K.; Tanner, C. M.; Bernstein, A. L.; Fross, R. D.; Leimpeter, A.; Bloch, D. A.; Nelson, L. M. Incidence of parkinson's disease: Variation by age, gender, and race/ethnicity. Am. J. Epidemiol. 2003, 157, 1015-1022.
16. Chase, T. N. Levodopa therapy consequences of the nonphysiologic replacement of dopamine. Neurology 1998, 50, S17-25.
17. Lang, A. E.; Lozano, A. M. Parkinsonʼs disease. New Engl. J. Med. 1998, 339, 1044-1053.
18. Obeso, J. A.; Olanow, C. W.; Nutt, J. G. Levodopa motor complications in parkinsonʼs disease. Trends Neurosci. 2000, 23, S2-S7.
19. Oung, Q. W.; Hariharan, M.; Basah, S.; Yaacob, S.; Sarillee, M.; Lee, H. L. Use of technological tools for parkinsonʼs disease early detection: A review. In Proceedings of the 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 28-30 November 2014; pp. 343-348.
20. Jankovic, J. Parkinson's disease: Clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 2008, 79, 368-376.
21. Keijsers, N. L.; Horstink, M. W.; Gielen, S. C. Online monitoring of dyskinesia in patients with parkinsonʼs disease. IEEE Eng. Med. Biol. Maga. 2003, 22, 96-103.
22. Patel, S.; Lorincz, K.; Hughes, R.; Huggins, N.; Growdon, J. H.; Welsh, M.; Bonato, P. Analysis of feature space for monitoring persons with parkinsonʼs disease with application to a wireless wearable sensor system. In Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 22-26 August 2007; pp. 6290-6293.
23. Group, P. S. Evaluation of dyskinesias in a pilot, randomized, placebo-controlled trial of remacemide in advanced parkinson disease. Arch. Neurol. 2001, 58, 1660.
24. Pastor-Sanz, L.; Cancela, J.; Waldmeyer, M. T. A.; Pansera, M.; Pastorino, M. Mobile Systems as a Challenge for Neurological Diseases Management-the Case of Parkinsonʼs; InTech Open Access Publisher: Morn Hill, Winchester, UK, 2011.
25. Patel, S.; Lorincz, K.; Hughes, R.; Huggins, N.; Growdon, J.; Standaert, D.; Dy, J.; Welsh, M.; Bonato, P. A body sensor network to monitor parkinsonian symptoms: Extracting features on the nodes. In Proceedings of the 5th International Workshop on Wearable Micro and Nanosystems for Personalised Health, Valencia, Spain, 21-23 May 2008; pp 21-23.
26. Goetz, C. G.; Fahn, S.; Martinez‐Martin, P.; Poewe, W.; Sampaio, C.; Stebbins, G. T.; Stern, M. B.; Tilley, B. C.; Dodel, R.; Dubois, B. Movement disorder society‐sponsored revision of the unified parkinsonʼs disease rating scale (MDS‐UPDRS): Process, format, and clinimetric testing plan. Mov. Disord. 2007, 22, 41-47.
27. Goetz, C. G.; Tilley, B. C.; Shaftman, S. R.; Stebbins, G. T.; Fahn, S.; Martinez‐Martin, P.; Poewe, W.; Sampaio, C.; Stern, M. B.; Dodel, R. Movement disorder society‐sponsored revision of the unified parkinsonʼs disease rating scale (MDS‐UPDRS): Scale presentation and clinimetric testing results. Mov. Disord. 2008, 23, 2129-2170.
28. Fattorini, L.; Felici, F.; Filligoi, G.; Traballesi, M.; Farina, D. Influence of high motor unit synchronization levels on non-linear and spectral variables of the surface EMG. J. Neurosci. Methods 2005, 143, 133-139.
29. Meigal, A. I.; Rissanen, S.; Tarvainen, M.; Karjalainen, P.; Iudina-Vassel, I.; Airaksinen, O.; Kankaanpää, M. Novel parameters of surface emg in patients with parkinson's disease and healthy young and old controls. J. Electromyogr. Kinesiol. 2009, 19, e206-e213.
30. Rissanen, S.; Kankaanpää, M.; Tarvainen, M. P.; Nuutinen, J.; Tarkka, I. M.; Airaksinen, O.; Karjalainen, P. A. Analysis of surface EMG signal morphology in parkinsonʼs disease. Physiol. Meas. 2007, 28, doi: 10. 1088/0967-3334/28/12/005.
31. Ruonala, V.; Meigal, A.; Rissanen, S.; Airaksinen, O.; Kankaanpaa, M.; Karjalainen, P. EMG signal morphology in essential tremor and parkinsonʼs disease. In Proceedings of the 35th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3-7 July 2013; pp. 5765-5768.
32. De Michele, G.; Sello, S.; Carboncini, M. C.; Rossi, B.; Strambi, S. K. Cross-correlation time-frequency analysis for multiple emg signals in parkinson's disease: A wavelet approach. Med. Eng. Phys. 2003, 25, 361-369.
33. Sturman, M. M.; Vaillancourt, D. E.; Metman, L. V.; Bakay, R. A.; Corcos, D. M. Effects of subthalamic nucleus stimulation and medication on resting and postural tremor in parkinsonʼs disease. Brain 2004, 127, 2131-2143.
34. Rissanen, S. M.; Kankaanpää, M.; Meigal, A.; Tarvainen, M. P.; Nuutinen, J.; Tarkka, I. M.; Airaksinen, O.; Karjalainen, P. A. Surface EMG and acceleration signals in parkinson's disease: Feature extraction and cluster analysis. Med. Biol. Eng. Comput. 2008, 46, 849-858.
35. Cole, B. T.; Roy, S. H.; Nawab, S. H. Detecting freezing-of-gait during unscripted and unconstrained activity. In Proceedings of the 2011 Annual International Conference of the Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August-3 September 2011; pp. 5649-5652.
36. Handojoseno, A. A.; Shine, J. M.; Nguyen, T. N.; Tran, Y.; Lewis, S. J.; Nguyen, H. T. The detection of freezing of gait in parkinsonʼs disease patients using eeg signals based on wavelet decomposition. In Proceedings of the 2012 Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August-1 September 2012; pp. 69-72.
37. Backer, J. H. The symptom experience of patients with parkinsonʼs disease. J. Neurosci. Nurs. 2006, 38, 51-57.
38. Nutt, J. G.; Bloem, B. R.; Giladi, N.; Hallett, M.; Horak, F. B.; Nieuwboer, A. Freezing of gait: Moving forward on a mysterious clinical phenomenon. Lancet Neurol. 2011, 10, 734-744.
39. Burrus, C. S.; Gopinath, R. A.; Guo, H. Introduction to Wavelets and Wavelet Transforms: A Primer; Prentice-Hall: Englewood Cliffs, NJ, USA, 1997.
40. Bhosale, M. P. G.; Patil, S. Classification of EMG signals using wavelet transform and hybrid classifier for parkinson's disease detection. Int. J. Eng. Res. Technol. 2012, 2, 106-112.
41. Long, D.; Wang, J.; Xuan, M.; Gu, Q.; Xu, X.; Kong, D.; Zhang, M. Automatic classification of early parkinsonʼs disease with multi-modal mr imaging. PLoS ONE 2012, 7, e47714.
42. Stawarz, M.; Polański, A.; Kwiek, S.; Boczarska-Jedynak, M.; Janik, L.; Przybyszewski, A.; Wojciechowski, K. A system for analysis of tremor in patients with parkinson's disease based on motion capture technique. In Computer Vision and Graphics; Springer: Berlin, Germany, 2012; pp. 618-625.
43. Andrade, L.; Manolakos, E. S. Signal background estimation and baseline correction algorithms for accurate DNA sequencing. J. VLSI Signal Process. Syst. Signal Image Video Technol. 2003, 35, 229-243.
44. Salarian, A.; Russmann, H.; Wider, C.; Burkhard, P. R.; Vingerhoets, F. J.; Aminian, K. Quantification of tremor and bradykinesia in parkinsonʼs disease using a novel ambulatory monitoring system. IEEE Trans. Biomed. Eng. 2007, 54, 313-322.
45. Salarian, A.; Russmann, H.; Vingerhoets, F. J.; Burkhard, P. R.; Aminian, K. Ambulatory monitoring of physical activities in patients with parkinsonʼs disease. IEEE Trans. Biomed. Eng. 2007, 54, 2296-2299.
46. Salarian, A.; Russmann, H.; Vingerhoets, F.; Burkhard, P.; Blanc, Y.; Dehollain, C.; Aminian, K. An ambulatory system to quantify bradykinesia and tremor in parkinsonʼs disease. In Proceedings of the 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, Birmingham, UK, 24-26 April 2003; pp. 35-38.
47. Salarian, A.; Russmann, H.; Vingerhoets, F. J.; Dehollaini, C.; Blanc, Y.; Burkhard, P. R.; Aminian, K. Gait assessment in parkinsonʼs disease: Toward an ambulatory system for long-term monitoring. IEEE Trans. Biomed. Eng. 2004, 51, 1434-1443.
48. Keijsers, N. L.; Horstink, M. W.; Gielen, S. C. Ambulatory motor assessment in parkinsonʼs disease. Mov. Disord. 2006, 21, 34-44.
49. Keijsers, N. L.; Horstink, M. W.; Gielen, S. C. Automatic assessment of levodopa‐induced dyskinesias in daily life by neural networks. Mov. Disord. 2003, 18, 70-80.
50. Keijsers, N. L.; Horstink, M. W.; Gielen, S. C. Movement parameters that distinguish between voluntary movements and levodopa-induced dyskinesia in parkinson's disease. Human Mov. Sci. 2003, 22, 67-89.
51. Keijsers, N.; Horstink, M.; van Hilten, J.; Hoff, J.; Gielen, C. Detection and assessment of the severity of levodopa‐induced dyskinesia in patients with parkinson's disease by neural networks. Mov. Disord. 2000, 15, 1104-1111.
52. Patel, S.; Lorincz, K.; Hughes, R.; Huggins, N.; Growdon, J.; Standaert, D.; Akay, M.; Dy, J.; Welsh, M.; Bonato, P. Monitoring motor fluctuations in patients with parkinsonʼs disease using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 2009, 13, 864-873.
53. Patel, S.; Chen, B. R.; Buckley, T.; Rednic, R.; McClure, D.; Tarsy, D.; Shih, L.; Dy, J.; Welsh, M.; Bonato, P. Home monitoring of patients with parkinsonʼs disease via wearable technology and a web-based application. In Proceedings of the 2010 Annual International Conference of the Engineering in Medicine and Biology Society, Buenos Aires, Argentina, 31 August-4 September 2010; pp. 4411-4414.
54. Chen, B. R.; Patel, S.; Buckley, T.; Rednic, R.; McClure, D. J.; Shih, L.; Tarsy, D.; Welsh, M.; Bonato, P. A web-based system for home monitoring of patients with parkinsonʼs disease using wearable sensors. IEEE Trans. Biomed. Eng. 2011, 58, 831-836.
55. Cancela, J.; Pastorino, M.; Arredondo, M.; Pansera, M.; Pastor-Sanz, L.; Villagra, F.; Pastor, M.; Gonzalez, A. Gait assessment in parkinsonʼs disease patients through a network of wearable accelerometers in unsupervised environments. In Proceedings of the 2011 Annual International Conference of the Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August-3 September 2011; pp. 2233-2236.
56. Das, S.; Amoedo, B.; de la Torre, F.; Hodgins, J. Detecting parkinsonsʼ symptoms in uncontrolled home environments: A multiple instance learning approach. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August-1 September 2012; pp 3688-3691.
57. Andrews, S.; Tsochantaridis, I.; Hofmann, T. Support vector machines for multiple-instance learning. In Proceedings of the Advances in neural information processing systems, Providence, RI, USA, 7 May 2002; pp. 561-568.
58. Lathropb, T. G. D. R. H. Solving the multiple-instance problem with axis-parallel rectangles. J. Artif. Intell. 1997, 89, 31-71.
59. Maron, O.; Lozano-Pérez, T. A framework for multiple-instance learning. In Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems, Denver, CO, USA, 29 November-4 December 1997; pp. 570-576.
60. Wang, J.; Zucker, J. D. Solving multiple-instance problem: A lazy learning approach. In Proceedings of the 17th International Conference on Machine Learning, Standord, CA, USA, 29 June-2 July 2000; pp. 1119-1126.
61. LeMoyne, R.; Mastroianni, T.; Grundfest, W. Wireless accelerometer configuration for monitoring parkinson's disease hand tremor. Sci. Res. 2013, 2, 62-67.
62. LeMoyne, R.; Coroian, C.; Mastroianni, T. Quantification of parkinsonʼs disease characteristics using wireless accelerometers. In Proceedings of the International Conference on Complex Medical Engineering, Tempe, AZ, USA, 9-11 April 2009; pp. 1-5.
63. Lemoyne, R.; Coroian, C.; Mastroianni, T.; Grundfest, W. Accelerometers for quantification of gait and movement disorders: A perspective review. J. Mech. Med. Biol. 2008, 8, 137-152.
64. LeMoyne, R.; Mastroianni, T.; Cozza, M.; Coroian, C.; Grundfest, W. Implementation of an iphone for characterizing parkinson's disease tremor through a wireless accelerometer application. In Proceedings of the Annual International Conference of the Engineering in Medicine and Biology Society, Buenos Aires, Argentina, 31 August-4 September 2010; pp. 4954 -4958.
65. Macht, M.; Kaussner, Y.; Möller, J. C.; Stiasny‐Kolster, K.; Eggert, K. M.; Krüger, H. P.; Ellgring, H. Predictors of freezing in parkinsonʼs disease: A survey of 6620 patients. Mov. Disord. 2007, 22, 953-956.
66. Bächlin, M.; Plotnik, M.; Roggen, D.; Giladi, N.; Hausdorff, J.; Tröster, G. A wearable system to assist walking of parkinson s disease patients. Methods Inf. Med. 2010, 49, 88-95.
67. Bächlin, M.; Plotnik, M.; Roggen, D.; Inbar, N.; Giladi, N.; Hausdorff, J.; Tröster, G. Parkinsons disease patients perspective on context aware wearable technology for auditive assistance. In Proceding of the 3rd International Conference on Pervasive Computing Technologies for Healthcare, London, UK, 1-3 April 2009; pp. 1-8.
68. Bächlin, M.; Plotnik, M.; Roggen, D.; Maidan, I.; Hausdorff, J. M.; Giladi, N.; Troster, G. Wearable assistant for parkinson's disease patients with the freezing of gait symptom. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 436-446.
69. Bächlin, M.; Roggen, D.; Tröster, G.; Plotnik, M.; Inbar, N.; Maidan, I.; Herman, T.; Brozgol, M.; Shaviv, E.; Giladi, N. Potentials of enhanced context awareness in wearable assistants for parkinsonʼs disease patients with the freezing of gait syndrome. In Proceedings of the International Symposium on Wearable Computers, Linz, Austria, 4-7 September 2009; pp. 123-130.
70. Bloem, B. R.; Hausdorff, J. M.; Visser, J. E.; Giladi, N. Falls and freezing of gait in parkinsonʼs disease: A review of two interconnected, episodic phenomena. Mov. Disord. 2004, 19, 871-884.
71. Giladi, N.; Treves, T.; Simon, E.; Shabtai, H.; Orlov, Y.; Kandinov, B.; Paleacu, D.; Korczyn, A. Freezing of gait in patients with advanced parkinsonʼs disease. J. Neural Transm. 2001, 108, 53-61.
72. Mazilu, S.; Calatroni, A.; Gazit, E.; Roggen, D.; Hausdorff, J. M.; Tröster, G. Feature learning for detection and prediction of freezing of gait in parkinson's disease. In Machine Learning and Data Mining in Pattern Recognition; Springer: Berlin, Germany, 2013; pp. 144-158.
73. Mazilu, S.; Hardegger, M.; Zhu, Z.; Roggen, D.; Troster, G.; Plotnik, M.; Hausdorff, J. M. Online detection of freezing of gait with smartphones and machine learning techniques. In Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), San Diego, CA, USA, 21-24 May 2012; pp. 123-130.
74. Moore, S. T.; MacDougall, H. G.; Ondo, W. G. Ambulatory monitoring of freezing of gait in parkinsonʼs disease. J. Neurosci. Methods 2008, 167, 340-348.
75. Zabaleta, H.; Keller, T.; Fimbel, E. Gait analysis in frequency domain for freezing detection in patients with parkinson's disease. Gerontechnology 2008, 7, 247.
76. Saad, A.; Zaarour, I.; Lefebvre, D.; Guerin, F.; Bejjani, P.; Ayache, M. About detection and diagnosis of freezing of gait. In Proceedings of the 2nd International Conference on Advances in Biomedical Engineering (ICABME), Tripoli, Libya, 11-13 September 2013; pp. 117-120.
77. Titze, I. R.; Martin, D. W. Principles of voice production. J. Acoust. Soc. Am. 1998, 104, 1148.
78. Baken, R. J.; Orlikoff, R. F. Clinical Measurement of Speech and Voice; Cengage Learning: Boston, MA, USA, 2000.
79. Rosen, K. M.; Duffy, J. R. Parametric quantitative acoustic analysis of conversation produced by speakers with dysarthria and healthy speakers. J. Speech Lang. Hear. Res. 2006, 49, 395-411.
80. Hartelius, L.; Svensson, P. Speech and swallowing symptoms associated with parkinson's disease and multiple sclerosis: A survey. Folia Phoniatrica et Logopaedica 1994, 46, 9-17.
81. Ho, A. K.; Iansek, R.; Marigliani, C.; Bradshaw, J. L.; Gates, S. Speech impairment in a large sample of patients with parkinson's disease. Behav. Neurol. 1999, 11, 131-137.
82. Sindhu, R.; Neoh, S. C.; Hariharan, M. A hybrid expert system for automatic detection of voice disorders. Int. J. Med. Eng. Inf. 2014, 6, 218-237.
83. Hariharan, M.; Polat, K.; Yaacob, S. A new feature constituting approach to detection of vocal fold pathology. Int. J. Syst. Sci. 2014, 45, 1622-1634.
84. Tsanas, A.; Little, M. A.; Fox, C.; Ramig, L. O. Objective automatic assessment of rehabilitative speech treatment in parkinsonʼs disease. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 22, 181-190.
85. Tsanas, A.; Little, M. A.; McSharry, P. E.; Spielman, J.; Ramig, L. O. Novel speech signal processing algorithms for high-accuracy classification of parkinson's disease. IEEE Trans. Biomed. Eng. 2012, 59, 1264-1271.
86. Little, M. A.; McSharry, P. E.; Hunter, E. J.; Spielman, J.; Ramig, L. O. Suitability of dysphonia measurements for telemonitoring of parkinson's disease. IEEE Trans. Biomed. Eng. 2009, 56, 1015-1022.
87. Hariharan, M.; Polat, K.; Sindhu, R. A new hybrid intelligent system for accurate detection of parkinsonʼs disease. Comput. Methods Progr. Biomed. 2014, 113, 904-913.
88. Tsanas, A. Accurate Telemonitoring of Parkinson's Disease Symptom Severity Using Nonlinear Speech Signal Processing and Statistical Machine Learning. Ph. D. Thesis, University of Oxford, Oxford, UK, 2012.
89. Little, M. A.; McSharry, P. E.; Roberts, S. J.; Costello, D. A.; Moroz, I. M. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed. Eng. OnLine 2007, 6, doi: 10. 1186/1475-925X-6-23.
90. Tsanas, A. New nonlinear markers and insights into speech signal degradation for effective tracking of parkinson's disease symptom severity. In Proceedings of the International Symposium on Nonlinear Theory and its Applications, Krakow, Poland, 5-8 September 2010; pp. 457-460.
91. Tsanas, A.; Little, M. A.; McSharry, P. E.; Ramig, L. O. Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average parkinsonʼs disease symptom severity. J. Royal Soc. Interface 2011, 8, 842-855.
92. Tsanas, A.; Little, M. A.; McSharry, P. E.; Ramig, L. O. Enhanced classical dysphonia measures and sparse regression for telemonitoring of parkinsonʼs disease progression. In Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing, Dallas, TX, USA, 14-19 March 2010; pp. 594-597.
93. Tsanas, A.; Little, M. A.; McSharry, P. E.; Ramig, L. O. Accurate telemonitoring of parkinsonʼs disease progression by noninvasive speech tests. IEEE Trans. Biomed. Eng. 2010, 57, 884-893.
94. Åström, F.; Koker, R. A parallel neural network approach to prediction of parkinson's disease. Expert Syst. Appl. 2011, 38, 12470-12474.
95. Shahbakhi, M.; Far, D. T.; Tahami, E. Speech analysis for diagnosis of parkinson's disease using genetic algorithm and support vector machine. J. Biomed. Sci. Eng. 2014, 7, 147-156.
96. Luukka, P. Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst. Appl. 2011, 38, 4600-4607.
97. Rustempasic, I.; Can, M. Diagnosis of parkinson's disease using fuzzy c-means clustering and pattern recognition. South East Eur. J. Soft Comput. 2013, 2, 42-49.
98. Hadjahmadi, A.; Askari, T. A decision support system for parkinsonʼs disease diagnosis using classification and regression tree. J. Math. Comput. Sci. 2012, 4, 257-263.
99. Das, R. A comparison of multiple classification methods for diagnosis of parkinson disease. Expert Syst. Appl. 2010, 37, 1568-1572.
100. Bakar, Z. A.; Ispawi, D. I.; Ibrahim, N. F.; Tahir, N. M. Classification of parkinsonʼs disease based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction. In Proceedings of the 8th International Colloquium on Signal Processing and its Applications, Melaka, Malaysia, 23-25 March 2012; pp. 63-67.
101. Chen, H. L.; Huang, C. C.; Yu, X. G.; Xu, X.; Sun, X.; Wang, G.; Wang, S. J. An efficient diagnosis system for detection of parkinson's disease using fuzzy k-nearest neighbor approach. Expert Syst. Appl. 2013, 40, 263-271.
102. Polat, K. Classification of parkinsonʼs disease using feature weighting method on the basis of fuzzy c-means clustering. Int. J. Syst. Sci. 2012, 43, 597-609.
103. Rahn, D. A.; Chou, M.; Jiang, J. J.; Zhang, Y. Phonatory impairment in parkinsonʼs disease: Evidence from nonlinear dynamic analysis and perturbation analysis. J. Voice 2007, 21, 64-71.
104. Tolosa, E.; Wenning, G.; Poewe, W. The diagnosis of parkinsonʼs disease. Lancet Neurol. 2006, 5, 75-86.
105. Singh, N.; Pillay, V.; Choonara, Y. E. Advances in the treatment of parkinsonʼs disease. Prog. Neurobiol. 2007, 81, 29-44.
106. Smith, S. EEG in the diagnosis, classification, and management of patients with epilepsy. J. Neurol. Neurosurg. Psychiatry 2005, 76, ii2-ii7.
107. Henry, J. C. Electroencephalography: Basic principles, clinical applications, and related fields. Neurology 2006, 67, doi: 10. 1212/01. wnl. 0000243257. 85592. 9a.
108. Niedermeyer, E.; da Silva, F. L. Electroencephalography: Basic principles, Clinical Applications, and Related Fields; Lippincott Williams & Wilkins: Baltimore, MD, USA, 2005.
109. Criswell, E. Cramʼs Introduction to Surface Electromyography; Jones & Bartlett Publishers: Sudbury, MA, USA, 2010.
110. Robichaud, J. A.; Pfann, K. D.; Comella, C. L.; Corcos, D. M. Effect of medication on emg patterns in individuals with parkinsonʼs disease. Mov. Disord. 2002, 17, 950-960.
111. Lukhanina, E.; Karaban, I.; Berezetskaya, N. Diagnosis of Parkinsonʼs Disease by Electrophysiological Methods; InTech Open Access Publisher: Winchester, UK, 2011.
112. Electromyography (EMG): Monitors Peripheral, Lumbar, and Cranial Nerves. Available online: www. calderdevelopment. com/modalities/emg. html (accessed on 25 Janaury 2015).
113. Neuroimaging: Advantages and Disadvatages of CT Scans. Available online: http: //web. stanford. edu/ group/hopes/cgi-bin/hopes_test/neuroimaging/#advantages-and-disadvantages-of-ct (accessed on 25 Janaury 2015).
114. Gould, T. A. How MRI Works; HowStuffWorks Inc: Atlanta, GA, USA, 2008.
115. Khemphila, A.; Boonjing, V. Parkinsons disease classification using neural network and feature selection. World Acad. Sci. Eng. Technol. 2012, 64, 15-18.
116. Patel, S.; Park, H.; Bonato, P.; Chan, L.; Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 2012, 9, doi: 10. 1186/1743-0003-9-21.
117. Bonato, P. Wearable sensors/systems and their impact on biomedical engineering. IEEE Eng. Med. Biol. Mag. 2003, 22, 18-20.
118. Bonato, P. Wearable sensors and systems. IEEE Eng. Med. Biol. Mag. 2010, 29, 25-36.
119. Bonato, P. Advances in wearable technology for rehabilitation. Stud. Health Technol. Inf. 2009, 145, 145-159.
120. Bonato, P. Advances in wearable technology and applications in physical medicine and rehabilitation. J. NeuroEng. Rehabil. 2005, 2, doi: 10. 1186/1743-0003-2-2.
121. Pastorino, M.; Arredondo, M.; Cancela, J.; Guillen, S. Wearable sensor network for health monitoring: The case of parkinson disease. J. Phys. Conf. Ser. 2013, 450, doi: 10. 1088/1742-6596/450/1/012055.
122. Oviatt, S. Advances in robust multimodal interface design. IEEE Comput. Graph. Appl. 2003, 23, 62-68.
123. Luo, R. C.; Kay, M. G. Multisensor integration and fusion in intelligent systems. IEEE Trans. Syst. Man Cybern. 1989, 19, 901-931.
124. Dumas, B.; Lalanne, D.; Oviatt, S. Multimodal interfaces: A survey of principles, models and frameworks. In Human Machine Interaction; Springer: Berlin, Germany, 2009; pp. 3-26.
125. Oviatt, S. Multimodal interfaces. In The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications; L. Erlbaum Associates Inc: Hillsdale, NJ, USA, 2003; pp. 286-304.
126. Oviatt, S.; Cohen, P. Perceptual user interfaces: Multimodal interfaces that process what comes naturally. Commun. ACM 2000, 43, 45-53.
127. Luo, R. C.; Chang, C. C. Multisensor fusion and integration: A review on approaches and its applications in mechatronics. IEEE Trans. Ind. Inf. 2012, 8, 49-60.
128. Luo, R. C.; Chang, C. C.; Lai, C. C. Multisensor fusion and integration: Theories, applications, and its perspectives. IEEE Sens. J. 2011, 11, 3122-3138.
129. Luo, R. C.; Chou, Y. C.; Chen, O. Multisensor fusion and integration: Algorithms, applications, and future research directions. In Proceedings of the International Conference on Mechatronics and Automation, 2007 (ICMA 2007), Harbin, China, 5-8 August 2007; pp. 1986-1991.
130. Chew, N.; Goh, K.; Tan, C. Parkinson's disease in university hospital, kuala lumpur. Neurol J. Southeast Asia 1998, 3, 75-80.