Estimating bradykinesia severity in Parkinson's disease by analysing gait through a waist-worn sensor.

Bradykinesia is a cardinal symptom of Parkinson's disease (PD) and describes the slowness of movement revealed in patients. Current PD therapies are based on dopamine replacement, and given that bradykinesia is the symptom that best correlates with the dopaminergic deficiency, the knowledge of its fluctuations may be useful in the diagnosis, treatment and better understanding of the disease progression. This paper evaluates a machine learning method that analyses the signals provided by a triaxial accelerometer placed on the waist of PD patients in order to automatically assess bradykinetic gait unobtrusively. This method employs Support Vector Machines to determine those parts of the signals corresponding to gait. The frequency content of strides is then used to determine bradykinetic walking bouts and to estimate bradykinesia severity based on an epsilon-Support Vector Regression model. The method is validated in 12 PD patients, which leads to two main conclusions. Firstly, the frequency content of the strides allows for the dichotomic detection of bradykinesia with an accuracy higher than 90%. This process requires the use of a patient-dependant threshold that is estimated based on a leave-one-patient-out regression model. Secondly, bradykinesia severity measured through UPDRS scores is approximated by means of a regression model with errors below 10%. Although the method has to be further validated in more patients, results obtained suggest that the presented approach can be successfully used to rate bradykinesia in the daily life of PD patients unobtrusively.

Dyskinesia and motor state detection in Parkinson's disease patients with a single movement sensor.

Parkinson's Disease (PD) is a neurodegenerative disease that alters the patients' motor performance. Patients suffer many motor symptoms: bradykinesia, dyskinesia and freezing of gait, among others. Furthermore, patients alternate between periods in which they are able to move smoothly for some hours (ON state), and periods with motor complications (OFF state). An accurate report of PD motor states and symptoms will enable doctors to personalize medication intake and, therefore, improve response to treatment. Additionally, real-time reporting could allow an automatic management of PD by means of an automatic control of drug-administration pump doses. Such a system must be able to provide accurate information without disturbing the patients' daily life activities. This paper presents the results of the MoMoPa study classifying motor states and dyskinesia from 20 PD patients by using a belt-worn single tri-axial accelerometer. The algorithms obtained will be validated in a further study with 15 PD patients and will be enhanced in the REMPARK project.

Multisite recording of extracellular potentials produced by microchannel-confined neurons in-vitro.

Towards establishing electrical interfaces with patterned in vitro neurons, we have previously described the fabrication of hybrid elastomer-glass devices polymer-on-multielectrode array technology and obtained single-electrode recordings of extracellular potentials from confined neurons (Claverol-Tinturé et al., 2005). Here, we demonstrate the feasibility of spatially localized multisite recordings from individual microchannel-guided neurites extending from microwell-confined somas with good signal-to-noise ratios (20 dB) and spike magnitudes of up to 300 microV. Single-cell current source density (scCSD) analysis of the spatio-temporal patterns of membrane currents along individual processes is illustrated.

Multielectrode arrays with elastomeric microstructured overlays for extracellular recordings from patterned neurons.

Multielectrode array technology constitutes a promising approach for the characterization of the activity-dependent neuronal plasticity underlying information processing in the nervous system. For this purpose, long-term monitoring and stimulation of cultured neuronal networks with one-to-one neuron-sensor interfacing is advantageous. Existing neurochips that meet these specifications have made use of custom 3D structures requiring clean-room intensive microfabrication techniques. Low-cost fabrication procedures with potential for mass production would facilitate progress in the area. To this end, we have developed a sandwich structure comprising an elastomeric film, microstructured by replica moulding and microhole punching, for neuronal patterning, and a standard planar microelectrode array (MEA), for stimulation and recording. The elastomeric film includes microwells for cell body confinement, and microchannels capable of guiding neurites for network topology specification. The device is formed by overlaying the elastomeric structures on planar arrays. The combination of replica moulding, rapid prototyping and planar MEAs results in low-cost neurochips accessible to most neurophysiology labs. Single neuron patterning and recordings of extracellular potentials are demonstrated.

Oriented principal component analysis for large margin classifiers.

Large margin classifiers (such as MLPs) are designed to assign training samples with high confidence (or margin) to one of the classes. Recent theoretical results of these systems show why the use of regularisation terms and feature extractor techniques can enhance their generalisation properties. Since the optimal subset of features selected depends on the classification problem, but also on the particular classifier with which they are used, global learning algorithms for large margin classifiers that use feature extractor techniques are desired. A direct approach is to optimise a cost function based on the margin error, which also incorporates regularisation terms for controlling capacity. These terms must penalise a classifier with the largest margin for the problem at hand. Our work shows that the inclusion of a PCA term can be employed for this purpose. Since PCA only achieves an optimal discriminatory projection for some particular distribution of data, the margin of the classifier can then be effectively controlled. We also propose a simple constrained search for the global algorithm in which the feature extractor and the classifier are trained separately. This allows a degree of flexibility for including heuristics that can enhance the search and the performance of the computed solution. Experimental results demonstrate the potential of the proposed method.