The Strongly Asynchronous Massive Access Channel.

This paper considers the Strongly Asynchronous, Slotted, Discrete Memoryless, Massive Access Channel (SAS-DM-MAC) in which the number of users, the number of messages, and the asynchronous window length grow exponentially with the coding blocklength with their respective exponents. A joint probability of error is enforced, ensuring that all the users' identities and messages are correctly identified and decoded. Achievability bounds are derived for the case that different users have similar channels, the case that users' channels can be chosen from a set which has polynomially many elements in the blocklength, and the case with no restriction on the users' channels.

Cache-Aided General Linear Function Retrieval.

Coded Caching, proposed by Maddah-Ali and Niesen (MAN), has the potential to reduce network traffic by pre-storing content in the users' local memories when the network is underutilized and transmitting coded multicast messages that simultaneously benefit many users at once during peak-hour times. This paper considers the linear function retrieval version of the original coded caching setting, where users are interested in retrieving a number of linear combinations of the data points stored at the server, as opposed to a single file. This extends the scope of the authors' past work that only considered the class of linear functions that operate element-wise over the files.

On the need for adaptive learning in on-demand Deep Brain Stimulation for Movement Disorders.

The results presented in this paper indicate that future on-demand Deep Brain Stimulation (DBS) systems for chronic use in patients with movement disorders should continuously and adaptively "learn" in order to maintain high symptom control efficacy. In this work, two machine learning algorithms-Decision Tree and LArge Memory STorage And Retrieval (LAMSTAR) neural network, both with surface Electromyography and accelerometry as control signals-are used to predict onset of tremor after DBS has been switched off in two patients, one suffering from Parkinson's disease and the other from essential tremor. The novelty of this work is that training and testing are done by using different data recorded during sessions at least one week apart.

Who May Benefit From On-Demand Control of Deep Brain Stimulation? Noninvasive Evaluation of Parkinson Patients.

Background: In closed-loop on-demand control (ODC) of deep brain stimulation (DBS), stimulation is applied only when symptoms appear. Following stimulation of a fixed duration, DBS is switched off until the symptoms reappear. By repeating these demand-driven cycles, the amount of stimulation delivered can be decreased, thereby reducing DBS side-effects and improving battery-life of the pulse-generator.

Towards fully automated closed-loop Deep Brain Stimulation in Parkinson's disease patients: A LAMSTAR-based tremor predictor.

This paper describes the application of the LAMSTAR (LArge Memory STorage and Retrieval) neural network for prediction of onset of tremor in Parkinson's disease (PD) patients to allow for on-off adaptive control of Deep Brain Stimulation (DBS). Currently, the therapeutic treatment of PD by DBS is an open-loop system where continuous stimulation is applied to a target area in the brain. This work demonstrates a fully automated closed-loop DBS system so that stimulation can be applied on-demand only when needed to treat PD symptoms.

On modeling the neuronal activity in movement disorder patients by using the Ornstein Uhlenbeck process.

Mathematical models of the neuronal activity in the affected brain regions of Essential Tremor (ET) and Parkinson's Disease (PD) patients could shed light into the underlying pathophysiology of these diseases, which in turn could help develop personalized treatments including adaptive Deep Brain Stimulation (DBS). In this paper, we use an Ornstein Uhlenbeck Process (OUP) to model the neuronal spiking activity recorded from the brain of ET and PD patients during DBS stereotactic surgery. The parameters of the OUP are estimated based on Inter Spike Interval (ISI) measurements, i.

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.

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 Parkinson's 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.

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 patient's 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.

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.

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.

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

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.

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.