Ishita Basu

Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design.

OBJECTIVE We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinsons disease (PD) and essential tremor (ET). APPROACH The tremor prediction algorithm uses a set of spectral (Fourier and wavelet) and nonlinear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals. MAIN RESULTS The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials and 80.2% for all PD trials. By using a Pearsons chi-square test, the prediction results are shown to significantly differ from a random prediction outcome. SIGNIFICANCE The tremor prediction algorithm can be potentially used for designing the next generation of non-invasive closed-loop predictive ON-OFF controllers for deep brain stimulation (DBS), used for suppressing pathological tremor in such patients. Such a system is based on alternating ON and OFF DBS periods, an incoming tremor being predicted during the time intervals when DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before tremor re-appears so that the patient is tremor-free for the entire DBS ON-OFF cycle and the tremor-free DBS OFF interval should be maximized in order to minimize the current injected in the brain and battery usage.

Adaptively controlling deep brain stimulation in essential tremor patient via surface electromyography

Abstract Objectives: We present patient test outcomes to show that on–off control of deep brain stimulation sequences in essential tremor patients is achievable in a self-adaptive manner via non-invasive surface-electromyography, to prevent tremors in these patients. Method: In our study, an essential tremor patient, who underwent bilateral deep brain stimulation implantation 8 years earlier, was subjected to deep brain stimulation at 130 pulses/second, with a 90-microsecond pulse-width, in packets of durations from 20 to 73 seconds and was monitored with surface-electromyography. Results: At the end of these stimulation packets, tremor-free intervals followed, averaging over 20 seconds, before tremor reappeared. Wavelet analysis of the eletromyographic signals allowed predicting onset of tremors at the end of the tremor-free intervals and was successful in all test cycles. Furthermore, once stimulation was restarted, the tremors disappeared within 0.5 seconds on average. When restarting stimulation approximately 2 seconds ahead of the end of tremor-free post-simulation intervals as predicted by visual inspection of unprocessed electromyograms, no tremors occurred during three successive cycles of stimulation-on and stimulation-off. Maximal ratio of tremor-free duration to stimulation duration was computed, to determine a best DBS (deep brain stimulation) duration range (20–35 seconds). Conclusions: We show existence of a tremor-free interval averaging over 20 seconds that follows applying stimulation packets of 20–35 seconds and that surface electomyogram allows predicting onset of tremor to facilitate activation of a next stimulation packet before tremor reappears. This establishes the feasibility of electromyographic-based predictive on–off control of deep brain stimulation in certain essential tremor patients. Best tremor-free duration to stimulation duration ratio may differ over the progression of the disorder and from patient to patient.

Reaching Criterion of a Three-Phase Voltage-Source Inverter Operating With Passive and Nonlinear Loads and Its Impact on Global Stability

We develop and demonstrate a technique based on composite Lyapunov functions (CLFs) to analyze the impacts of passive (RL and RC) and nonlinear (diode rectifier) loads on the reaching dynamics of a three-phase voltage-source inverter (VSI). The reaching criterion (which ensures convergences of state trajectories to an orbit) is synthesized using piecewise linear models of the VSI and the loads and conditions for switching among the various models (corresponding to the different switching states). Once orbital existence is ensured using the reaching criterion , we extend the CLF-based approach to predict the stability of the nominal (period-1) orbit of the system (comprising the three-phase VSI and the load) and compare these predictions with those obtained using a conventional impedance-criterion technique that is developed based on a linearized averaged model. Overall, we demonstrate the significance of analyzing the reaching condition from the standpoint of orbital existence and why such a criterion is necessary for analyzing global stability. On a broader note, the methodology outlined in this paper is useful for analyzing the global stability of multiphase inverters, potentially leading to advanced control design of VSI for applications including uninterrupted power supplies, telecommunication power supplies, grid-connected inverters, motor drives, and active filters.

A neural network-based design of an on-off adaptive control for Deep Brain Stimulation in movement disorders

The current Food and Drug Administration approved system for the treatment of tremor disorders through Deep Brain Stimulation (DBS) of the area of the brain that controls movement, operates open-loop. It does not automatically adapt to the instantaneous patients needs or to the progression of the disease. This paper demonstrates an adaptive closed-loop controlled DBS that, after switching off stimulation, tracks few physiological signals to predict the reappearance of tremor before the patient experiences discomfort, at which point it instructs the DBS controller to switch on stimulation again. The core of the proposed approach is a Neural Network (NN) which effectively extracts tremor predictive information from non-invasively recorded surface-electromyogram(sEMG) and accelerometer signals measured at the symptomatic extremities. A simple feed-forward back-propagation NN architecture is shown to successfully predict tremor in 31 out of 33 trials in two Parkinsons Disease patients with an overall accuracy of 75.8% and sensitivity of 92.3%. This work therefore shows that closed-loop DBS control is feasible in the near future and that it can be achieved without modifications of the electrodes implanted in the brain, i.e., is backward compatible with approved DBS systems.

Adaptive control of deep brain stimulator for Essential Tremor: Entropy-based tremor prediction using surface-EMG

Entropy, as a measure of randomness in time-varying signals, is widely used in areas such as thermodynamics, statistical mechanics and information theory. This paper investigates the use of two commonly employed entropy measures, namely Wavelet Entropy and Approximate Entropy, as a predictor of tremor reappearance in Essential Tremor patients; the predictor input is a raw surface-electromyographic (sEMG) signal measured from tremor affected muscles of patients implanted with a Deep Brain Stimulator (DBS). A combination of both types of entropy measure is shown to successfully predict the occurrence of tremor few seconds before its visual manifestation. This result can potentially lead to a novel sEMG-based adaptive on-off DBS controller that can be added on to existing open-loop DBS systems with minimal changes; an adaptive DBS system provides stimulation only when needed thereby reducing the risk of brain over stimulation, delaying DBS intolerance and prolonging DBS battery life.

A study of the dynamics of seizure propagation across micro domains in the vicinity of the seizure onset zone

OBJECTIVE The use of micro-electrode arrays to measure electrical activity from the surface of the brain is increasingly being investigated as a means to improve seizure onset zone (SOZ) localization. In this work, we used a multivariate autoregressive model to determine the evolution of seizure dynamics in the [Formula: see text] Hz high frequency band across micro-domains sampled by such micro-electrode arrays. We showed that a directed transfer function (DTF) can be used to estimate the flow of seizure activity in a set of simulated micro-electrode data with known propagation pattern. APPROACH We used seven complex partial seizures recorded from four patients undergoing intracranial monitoring for surgical evaluation to reconstruct the seizure propagation pattern over sliding windows using a DTF measure. MAIN RESULTS We showed that a DTF can be used to estimate the flow of seizure activity in a set of simulated micro-electrode data with a known propagation pattern. In general, depending on the location of the micro-electrode grid with respect to the clinical SOZ and the time from seizure onset, ictal propagation changed in directional characteristics over a 2-10 s time scale, with gross directionality limited to spatial dimensions of approximately [Formula: see text]. It was also seen that the strongest seizure patterns in the high frequency band and their sources over such micro-domains are more stable over time and across seizures bordering the clinically determined SOZ than inside. SIGNIFICANCE This type of propagation analysis might in future provide an additional tool to epileptologists for characterizing epileptogenic tissue. This will potentially help narrowing down resection zones without compromising essential brain functions as well as provide important information about targeting anti-epileptic stimulation devices.

Stochastic modeling of the neuronal activity in the subthalamic nucleus and model parameter identification from Parkinson patient data

Several stochastic models, with various degrees of complexity, have been proposed to model the neuronal activity from different parts of the human brain. In this article, we use a simple Ornstein–Uhlenbeck process (OUP) to model the spike activity recorded from the subthalamic nucleus of patients suffering from Parkinson’s disease at the time of implantation of the electrodes for deep brain stimulation. From the recorded data, which contains information about the spike times of a single neuron, we identify and extract the model parameters of the OUP. We then use these parameters to numerically simulate the inter-spike intervals and the voltage across the neuron membrane. We finally assess how well the proposed mathematical model fits to the measured data and compare it with other commonly adopted stochastic models. We show an excellent agreement between the computer-generated data according to the OUP model and the measured one, as well as the superiority of the OUP model when compared to the Poisson process model and the random walk model; thus, establishing the validity of the OUP as a simple yet biologically plausible model of the neuronal activity recorded from the subthalamic nucleus of Parkinson’s disease patients.

A decision tree classifier for postural and movement conditions in Essential Tremor patients

This paper proposes a decision tree based classifier to discriminate between movement and postural conditions in Essential Tremor (ET) patients when their Deep Brain Stimulator (DBS) is switched OFF and they do not yet present tremor symptoms. This aims to be the first stage of a fully automated closed-loop ON-OFF DBS system in which the algorithm for prediction of tremor onset uses optimized parameters depending on the patients postural or movement condition. The classifier inputs are the power of the surface-electromyogram (sEMG) and accelerometer (Acc) signals recorded at the symptomatic extremities of the patients. The proposed classification tree uses Gini splitting rule and an optimized pruning scheme. The classifier achieves an overall accuracy of 96.55% by correctly classifying 112 out of 116 trials in four ET patients: 49 trials were in the movement condition and 67 were in postural condition. A classification accuracy of 100.00% (49 trials out of 49) and 94.03% (63 trials out of 67) is achieved for movement and posture conditions, respectively.

Towards closed-loop deep brain stimulation: Decision tree-based Essential Tremor patient's state classifier and tremor reappearance predictor

Deep Brain Stimulation (DBS) is a surgical procedure to treat some progressive neurological movement disorders, such as Essential Tremor (ET), in an advanced stage. Current FDA-approved DBS systems operate open-loop, i.e., their parameters are unchanged over time. This work develops a Decision Tree (DT) based algorithm that, by using non-invasively measured surface EMG and accelerometer signals as inputs during DBS-OFF periods, classifies the ET patients state and then predicts when tremor is about to reappear, at which point DBS is turned ON again for a fixed amount of time. The proposed algorithm achieves an overall accuracy of 93.3% and sensitivity of 97.4%, along with 2.9% false alarm rate. Also, the ratio between predicted tremor delay and the actual detected tremor delay is about 0.93, indicating that tremor prediction is very close to the instant where tremor actually reappeared.

Stochastic modeling of the neuronal activity in the thalamus of Essential Tremor patient

Several stochastic models, with various degrees of complexity, have been proposed to model the neuronal activity from different parts of the human brain. In this paper, we use an Ornstein-Uhlenbeck Process (OUP) to model the spike activity recorded from the thalamus of a patient suffering from Essential Tremor at the time of implantation of the electrodes for Deep Brain Stimulation. From the recorded data, which contains information about the spike times of a single neuron, we identify the model parameters of the OUP.We then use these parameters to numerically simulate the inter-spike interval distribution. We show that the OUP provides excellent fits to the data recorded both without any external stimulation as well as with stimulation. We finally compare the fits with other stochastic models commonly used and we show the superiority of the OUP model in general.