Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

The possibility of using a radial basis function neural network (RBFNN) to accurately recognise and predict the onset of Parkinson’s disease tremors in human subjects is discussed in this paper. The data for training the RBFNN are obtained by means of deep brain electrodes implanted in a Parkinson disease patient’s brain. The effectiveness of a RBFNN is initially demonstrated by a real case study.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Features for detection of Parkinson's disease tremor from local field potentials of the subthalamic nucleus

Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.

An electrochemical study of 4-aminothiophenol/pt nanoparticle multilayers on gold electrodes

Quartz crystal microbalance (QCM) measurements of the formation of a 4-aminothiophenol (4-ATP)self-assembled monolayer (SAM) at a gold electrode showed that a surface coverage of 118 ng cm(-2) was obtained after a 3 h exposure period, indicating that good surface coverage was achieved. Cyclic voltammetry of the ferricyanide redox couple across this SAM modified surface produced similar results to those of a bare electrode; however, the electroreduction of oxygen was found to be impaired. The 4-ATP SAM layer was not stable to repeated electrochemical oxidation and reduction; it is believed that the 4-ATP SAM layer was first converted to a 4'-mercapto-N-phenylquinone diimine (NPQD) layer followed by subsequent formation of a 4'-mercapto-N-phenylquinone monoimine (NPQM) layer. We also report a quartz crystal microbalance study of the attachment of platinum nanoparticles to such SAM modified electrodes. We show that five times the amount of platinum nanoparticles can be attached to a 4-ATP modified electrode surface (observed frequency change - 187 Hz) compared with an NPQD modified electrode surface (observed frequency change -35 Hz). The presence of the platinum particles was confirmed electrochemically by their surface electrochemical properties, which were different from those of the underlying gold electrode. It is believed that this is the first time that such direct evidence of electrochemical communication between platinum nanoparticles and a SAM modified electrode surface has been obtained. It was also shown to be possible to build up multilayer SAM/nanoparticle modified surfaces while maintaining efficient electrochemical communication. Up to three SAM/nanoparticle sandwich layers were constructed.

Exploring the applications of a pulsating jet hydrodynamic modulated voltammetric (HMV) system - electrochemistry of nanostructured Pt electrodes and trace analysis

The electrochemistry of nanostructured electrodes is investigated using hydrodynamic modulated voltammetry (HMV). Here a liquid crystal templating process is used to produce a platinum modified electrode with a relatively high surface area (Roughness factor, Rf = 42.4). The electroreduction of molecular oxygen at a nanostructured platinum surface is used to demonstrate the ability of HMV to discriminate between Faradaic and non-Faradaic electrode reactions. The HMV approach shows that the reduction of molecular oxygen shows considerable hysteresis correlating with the formation and stripping of oxide species at the platinum surface. Without the HMV analysis it is difficult to discern the same detail under the conditions employed. In addition the detection limit of the apparatus is explored and shown, under ideal conditions, to be of the order of 45 nmol dm(-3) employing [Fe(CN)(6)](4-) as a test species. (C) 2009 Elsevier B.V. All rights reserved.

Electrochemical characterisation and anodic stripping voltammetry at mesoporous platinum rotating disc electrodes

Using the technique of liquid crystal templating a rotating disc electrode (RDE) was modified with a high surface area mesoporous platinum film. The surface area of the electrode was characterised by acid voltammetry, and found to be very high (ca. 86 cm(2)). Acid characterisation of the electrode produced distorted voltammograms was interpreted as being due to the extremely large surface area which produced a combination of effects such as localised pH change within the pore environment and also ohmic drop effects. Acid voltammetry in the presence of two different types of surfactant, namely Tween 20 and Triton X-100, suggested antifouling properties associated with the mesoporous deposit. Further analysis of the modified electrode using a redox couple in solution showed typical RDE behaviour although extra capacitive currents were observed due to the large surface area of the electrode. The phenomenon of underpotential deposition was exploited for the purpose of anodic stripping voltammetry and results were compared with data collected for microelectrodes. Underpotential deposition of metal ions at the mesoporous RDE was found to be similar to that at conventional platinum electrodes and mesoporous microelectrodes although the rate of surface coverage was found to be slower at a mesoporous RDE. It was found that a mesoporous RDE forms a suitable system for quantification of silver ions in solution.

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

Relationship between dietary-induced changes in intestinal commensal microflora and duodenojejunal myoelectric activity monitored by radiotelemetry in the rat in vivo

Interdigestive intestinal motility, and especially phase III of the migrating myoelectric/motor complex (MMC), is responsible for intestinal clearance and plays an important role in prevention of bacterial overgrowth and translocation in the gut. Yet previous results from gnotobiotic rats have shown that intestinal microflora can themselves affect the characteristics of the myoelectric activity of the gut during the interdigestive state. Given that the composition of the intestinal microflora can be altered by dietary manipulations, we investigated the effect of supplementation of the diet with synbiotics on intestinal microflora structure and the duodenojejunal myoelectric activity in the rat. To reduce animal distress caused by restraint and handling, which can itself affect GI motility, we applied radiotelemetry for duodenojejunal EMG recordings in conscious, freely moving rats. Thirty 16-month-old Spraque-Dawley rats were used. The diet for 15 rats (E group) was supplemented with chicory inulin, Lactobacillus rhamnosus and Bifidobacterium lactis. The remaining 15 rats were fed control diet without supplements (C group). Three rats from each group were implanted with three bipolar electrodes positioned at 2, 14 and 28 cm distal to the pylorus. After recovery, two 6 h recordings of duodenojejunal EMG were carried out on each operated rat. Subsequently, group C rats received feed supplements and group E rats received only control diet for 1 week, and an additional two 6 h recordings were carried out on each of these rats. Non-operated C and E rats were killed and samples of GI tract were collected for microbiological analyses. Supplementation of the diet with the pro- and prebiotics mixture increased the number of bifidobacteria, whereas it decreased the number of enterobacteria in jejunum, ileum, caecum and colon. In both caecum and colon, the dietary supplementation increased the number of total anaerobes and lactobacilli. Treatment with synbiotics increased occurrence of phase III of the MMC at all three levels of the small intestine. The propagation velocity of phase III in the whole recording segment was also increased from 3.7 +/- 0.2 to 4.4 +/- 0.2 cm min(-1) by dietary treatment. Treatment with synbiotics increased the frequency of response potentials of the propagated phase III of the MMC at both levels of the jejunum, but not in the duodenum. In both parts of the jejunum, the supplementation of the diet significantly decreased the duration of phase II of the MMC, while it did not change the duration of phase I and phase III. Using the telemetry technique it was demonstrated that changes in the gastrointestinal microflora exhibited an intestinal motility response and, more importantly, that such changes can be initiated by the addition of synbiotics to the diet.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

High sensitivity recording of afferent nerve activity using ultra-compliant microchannel electrodes: an acutein vivovalidation

Neuroprostheses interfaced with transected peripheral nerves are technological routes to control robotic limbs as well as convey sensory feedback to patients suffering from traumatic neural injuries or degenerative diseases. To maximize the wealth of data obtained in recordings, interfacing devices are required to have intrafascicular resolution and provide high signal-to-noise ratio (SNR) recordings. In this paper, we focus on a possible building block of a three-dimensional regenerative implant: a polydimethylsiloxane (PDMS) microchannel electrode capable of highly sensitive recordings in vivo. The PDMS 'micro-cuff' consists of a 3.5 mm long (100 µm × 70 µm cross section) microfluidic channel equipped with five evaporated Ti/Au/Ti electrodes of sub-100 nm thickness. Individual electrodes have average impedance of 640 ± 30 kΩ with a phase angle of −58 ± 1 degrees at 1 kHz and survive demanding mechanical handling such as twisting and bending. In proof-of-principle acute implantation experiments in rats, surgically teased afferent nerve strands from the L5 dorsal root were threaded through the microchannel. Tactile stimulation of the skin was reliably monitored with the three inner electrodes in the device, simultaneously recording signal amplitudes of up to 50 µV under saline immersion. The overall SNR was approximately 4. A small but consistent time lag between the signals arriving at the three electrodes was observed and yields a fibre conduction velocity of 30 m s−1. The fidelity of the recordings was verified by placing the same nerve strand in oil and recording activity with hook electrodes. Our results show that PDMS microchannel electrodes open a promising technological path to 3D regenerative interfaces.

Evaluation of negative photo-patternable PDMS for the encapsulation of neural electrodes

This communication examines the suitability of a photo-patternable polydimethylsiloxane (PP-PDMS) elastomer as an insulating material for implantable microelectrodes. PP-PDMS is produced by mixing a photoinitiator (2-hydroxy-2-methylpropiophenone) with the PDMS base and curing agent. Subsequent exposure to UV radiation and development of the elastomeric “photo-resist” allows for the definition of well-defined openings within the PP-PDMS film. The dielectric constants of PP-PDMS and PDMS are similar (ε ≈ 2.6, f <;1MHz). Gold film microelectrodes patterned on glass or a PDMS substrate are encapsulated with PP-PDMS, while recording sites as small as 104 μm2 can be obtained in the PP-PDMS layer. The cytotoxicity of the PP-PDMS was preliminary tested in vitro by culturing 3T3 fibroblasts in PP-PDMS extracts. No adverse effects were observed in cultures exposed to PP-PDMS films initially leached in isopropanol solvent for 48h.

A microchannel neuroprosthesis for bladder control after spinal cord injury in rat

A severe complication of spinal cord injury is loss of bladder function (neurogenic bladder), which is characterized by loss of bladder sensation and voluntary control of micturition (urination), and spontaneous hyperreflexive voiding against a closed sphincter (detrusor-sphincter dyssynergia). A sacral anterior root stimulator at low frequency can drive volitional bladder voiding, but surgical rhizotomy of the lumbosacral dorsal roots is needed to prevent spontaneous voiding and dyssynergia. However, rhizotomy is irreversible and eliminates sexual function, and the stimulator gives no information on bladder fullness. We designed a closed-loop neuroprosthetic interface that measures bladder fullness and prevents spontaneous voiding episodes without the need for dorsal rhizotomy in a rat model. To obtain bladder sensory information, we implanted teased dorsal roots (rootlets) within the rat vertebral column into microchannel electrodes, which provided signal amplification and noise suppression. As long as they were attached to the spinal cord, these rootlets survived for up to 3 months and contained axons and blood vessels. Electrophysiological recordings showed that half of the rootlets propagated action potentials, with firing frequency correlated to bladder fullness. When the bladder became full enough to initiate spontaneous voiding, high-frequency/amplitude sensory activity was detected. Voiding was abolished using a high-frequency depolarizing block to the ventral roots. A ventral root stimulator initiated bladder emptying at low frequency and prevented unwanted contraction at high frequency. These data suggest that sensory information from the dorsal root together with a ventral root stimulator could form the basis for a closed-loop bladder neuroprosthetic. Copyright © 2013, American Association for the Advancement of Science

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.