title: Dynamic neural network detection of tremor and dyskinesia from wearable sensor data.
abstract: We present a dynamic neural network (DNN) solution for detecting time-varying occurrences of tremor and dyskinesia at 1 s resolution from time series data acquired from surface electromyographic (sEMG) sensors and tri-axial accelerometers worn by patients with Parkinson's disease (PD). The networks were trained and tested on separate datasets, each containing approximately equal proportions of tremor, dyskinesia, and disorder-free data from 8 PD and 4 control subjects performing unscripted and unconstrained activities in an apartment-like environment. During DNN testing, tremor was detected with a sensitivity of 93% and a specificity of 95%, while dyskinesia was detected with a sensitivity of 91% and a specificity of 93%. Similar sensitivity and specificity levels were obtained when DNN testing was carried out on subjects who were not included in DNN training.
authors.affiliation: Dept. of Electrical and Computer Engineering (ECE), Boston University, MA 02215, USA. bcole@bu.edu
authors.forename: Bryan T
authors.initials: BT
authors.lastname: Cole
authors.forename: Serge H
authors.initials: SH
authors.lastname: Roy
authors.forename: Carlo J
authors.initials: CJ
authors.lastname: De Luca
authors.forename: S
authors.initials: S
authors.lastname: Nawab
mesh_terms: D001132:Arm; D003020:Clothing; D020820:Dyskinesias / Q000175:diagnosis*; D004576:Electromyography / Q000295:instrumentation*; D006801:Humans; D016571:Neural Networks, Computer; D014202:Tremor / Q000175:diagnosis*; D014953:Wrist