Remote smartphone monitoring for management of Parkinson's Disease

Parkinson's Disease (PD) is a disabling neurodegenerative disease that affects approximately 1 million people in the United States. PD symptoms such as tremor, rigidity, bradykinesia and gait disturbances can be alleviated with drug treatments or deep brain stimulation; however, these treatments need to be appropriately adjusted over time. Monitoring the disease severity during intermittent physician visits for this purpose is notoriously imprecise. Remote and continuous monitoring of the severity of parkinsonism in these patients could therefore significantly improve the patient's health and quality of life. In recent work, we showed it is possible to discriminate between varying levels of parkinsonism and normal brain activity, based on motor cortex electroencephalograms (EEGs). We believe it may be possible to detect similar patterns in ambulatory human EEGs collected periodically during home health care visits. In this study, we investigate how these EEG readings can be used together with continuous smartphone gyroscope and accelerometer movement measurements to allow improved management of Parkinson's Disease treatments.

[1]  T. Steffen,et al.  Testing functional performance in people with Parkinson disease. , 2005, Physical therapy.

[2]  H. Eichenbaum,et al.  Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. , 2010, Journal of neurophysiology.

[3]  G. Carvell,et al.  Application of Modified Regression Techniques to a Quantitative Assessment for the Motor Signs of Parkinson's Disease , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Robert LeMoyne,et al.  Implementation of an iPhone for characterizing Parkinson's disease tremor through a wireless accelerometer application , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[5]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[6]  Ned Jenkinson,et al.  Rapid tremor frequency assessment with the iPhone accelerometer. , 2011, Parkinsonism & related disorders.

[7]  M. Berger,et al.  High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex , 2006, Science.

[8]  Giuliana Grimaldi,et al.  Neurological Tremor: Sensors, Signal Processing and Emerging Applications , 2010, Sensors.

[9]  V. Preedy,et al.  Unified Parkinson's Disease Rating Scale , 2010 .

[10]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[11]  M. Sekine,et al.  Quantitative evaluation of movement using the timed up-and-go test , 2008, IEEE Engineering in Medicine and Biology Magazine.

[12]  Mark A. Clements,et al.  Digital Signal Processing and Statistical Classification , 2002 .

[13]  Xiaoping Yun,et al.  Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking , 2006, IEEE Trans. Robotics.

[14]  Pierre Jallon,et al.  A graph based method for timed up & go test qualification using inertial sensors , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  F. Horak,et al.  iTUG, a Sensitive and Reliable Measure of Mobility , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Luca Palmerini,et al.  Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.