Title :
Dynamic SVM detection of tremor and dyskinesia during unscripted and unconstrained activities
Author :
Cole, Bryan T. ; Ozdemir, P. ; Nawab, S. Hamid
Author_Institution :
Drager Med. Syst., Inc., Andover, MA, USA
fDate :
Aug. 28 2012-Sept. 1 2012
Abstract :
In this paper, we report an experimental comparison of dynamic support vector machines (SVMs) to dynamic neural networks (DNNs) in the context of a system for detecting dyskinesia and tremor in Parkinson´s disease (PD) patients wearing accelerometer (ACC) and surface electromyographic (sEMG) sensors while performing unscripted and unconstrained activities of daily living. These results indicate that SVMs and DNNs of comparable computational complexities yield approximately identical performance levels when using an identical set of input features.
Keywords :
accelerometers; biomedical measurement; diseases; electromyography; medical signal processing; neural nets; support vector machines; DNN; Parkinson disease patients; accelerometer; dynamic SVM dyskinesia detection; dynamic SVM tremor detection; dynamic neural networks; support vector machines; surface electromyographic sensors; unconstrained activities; unscripted activities; Error analysis; Kernel; Neural networks; Sensors; Support vector machines; Training; Actigraphy; Algorithms; Diagnosis, Computer-Assisted; Dyskinesias; Humans; Monitoring, Ambulatory; Parkinson Disease; Reproducibility of Results; Sensitivity and Specificity; Support Vector Machines; Tremor;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-4119-8
Electronic_ISBN :
1557-170X
DOI :
10.1109/EMBC.2012.6347040
