Autoregressive models for biomedical signal processing.

Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model.

Artificial intelligence-enhanced epileptic seizure detection by wearables.

Objective: Wrist- or ankle-worn devices are less intrusive than the widely used electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom-developed deep-learning seizure detection models, we demonstrate the detection of a broad range of seizure types by wearable signals.

Methods: Patients admitted to the epilepsy monitoring unit were enrolled and asked to wear wearable sensors on either wrists or ankles.

Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications.

Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware.

A Multi-Modal AI-Driven Cohort Selection Tool to Predict Suboptimal Non-Responders to Aflibercept Loading-Phase for Neovascular Age-Related Macular Degeneration: PRECISE Study Report 1.

Patients diagnosed with exudative neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate individualised therapies.

Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation.

Performance in an engineering system tends to degrade over time due to a variety of wearing or ageing processes. In supervisory controlled processes there are typically many signals being monitored that may help to characterize performance degradation. It is preferred to select the least amount of information to obtain high quality of predictive analysis from a large amount of collected data, in which labeling the data is not always feasible.

Evaluating Rehabilitation Progress Using Motion Features Identified by Machine Learning.

Evaluating progress throughout a patient's rehabilitation episode is critical for determining the effectiveness of the selected treatments and is an essential ingredient in personalised and evidence-based rehabilitation practice. The evaluation process is complex due to the inherently large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress.

Effective Assessments of a Short-Duration Poor Posture on Upper Limb Muscle Fatigue Before Physical Exercise.

A forward head and rounded shoulder posture is a poor posture that is widely seen in everyday life. It is known that sitting in such a poor posture with long hours will bring health issues such as muscle pain. However, it is not known whether sitting in this poor posture for a short period of time will affect human activities.

Feature Learning in Assistive Rehabilitation Robotic Systems.

Feature learning plays a crucial role in data analysis when the measured data is in a high dimensional space. This paper applied the feature learning technique in data set collected from human movement experiments in an upper limb rehabilitation robotic device. The results showed that the proposed feature learning technique can identify key features to characterize the upper limb movements of humans, even though human variations exist.

EMU: A transparent 3D robotic manipulandum for upper-limb rehabilitation.

This paper introduces the EMU, a three-dimensional robotic manipulandum for rehabilitation of the upper extremity for patients with neurological injury. The device has been designed to be highly transparent, have a large workspace, and allow the use of the hand for interaction with real-world objects to provide additional contextual cues during exercises. The transparency is achieved through the use of a capstan transmission for the drive joints; a hybrid serial parallel kinematics minimising moving inertia; and lightweight materials.

Probing to Observe Neural Dynamics Investigated with Networked Kuramoto Oscillators.

The expansion of frontiers in neural engineering is dependent on the ability to track, detect and predict dynamics in neural tissue. Recent innovations to elucidate information from electrical recordings of brain dynamics, such as epileptic seizure prediction, have involved switching to an active probing paradigm using electrically evoked recordings rather than traditional passive measurements. This paper positions the advantage of probing in terms of information extraction, by using a coupled oscillator Kuramoto model to represent brain dynamics.

Fourier Tract Sampling (FouTS): A framework for improved inference of white matter tracts from diffusion MRI by explicitly modelling tract volume.

Diffusion MRI tractography algorithm development is increasingly moving towards global techniques to incorporate "downstream" information and conditional probabilities between neighbouring tracts. Such approaches also enable white matter to be represented more tangibly than the abstract lines generated by the most common approaches to fibre tracking. However, previously proposed algorithms still use fibre-like models of white matter corresponding to thin strands of white matter tracts rather than the tracts themselves, and therefore require many components for accurate representations, which leads to poorly constrained inverse problems.

Solving the Bloch equation with periodic excitation using harmonic balancing: application to Rabi modulated excitation.

In waveform design for magnetic resonance applications, periodic continuous-wave excitation offers potential advantages that remain largely unexplored because of a lack of understanding of the Bloch equation with periodic continuous-wave excitations. Using harmonic balancing techniques the steady state solutions of the Bloch equation with periodic excitation can be effectively solved. Moreover, the convergence speed of the proposed series approximation is such that a few terms in the series expansion suffice to obtain a very accurate description of the steady state solution.

Effect of sensory experience on motor learning strategy.

It is well known that the central nervous system automatically reduces a mismatch in the visuomotor coordination. Can the underlying learning strategy be modified by environmental factors or a subject's learning experiences? To elucidate this matter, two groups of subjects learned to execute reaching arm movements in environments with task-irrelevant visual cues. However, one group had previous experience of learning these movements using task-relevant visual cues.

Modelling extracellular electrical stimulation: part 4. Effect of the cellular composition of neural tissue on its spatio-temporal filtering properties.

Objective: The objective of this paper is to present a concrete application of the cellular composite model for calculating the membrane potential, described in an accompanying paper.

Approach: A composite model that is used to determine the membrane potential for both longitudinal and transverse modes of stimulation is demonstrated.

Main Results: Two extreme limits of the model, near-field and far-field for an electrode close to or distant from a neuron, respectively, are derived in this paper.

Modelling extracellular electrical stimulation: part 3. Derivation and interpretation of neural tissue equations.

Objective: A common approach in modelling extracellular electrical stimulation is to represent neural tissue by a volume conductor when calculating the activating function as the driving term in a cable equation for the membrane potential. This approach ignores the cellular composition of tissue, including the neurites and their combined effect on the extracellular potential. This has a number of undesirable consequences.

The dynamics of the epileptic brain reveal long-memory processes.

The pattern of epileptic seizures is often considered unpredictable and the interval between events without correlation. A number of studies have examined the possibility that seizure activity respects a power-law relationship, both in terms of event magnitude and inter-event intervals. Such relationships are found in a variety of natural and man-made systems, such as earthquakes or Internet traffic, and describe the relationship between the magnitude of an event and the number of events.

Rabi resonance in spin systems: theory and experiment.

The response of a magnetic resonance spin system is predicted and experimentally verified for the particular case of a continuous wave amplitude modulated radiofrequency excitation. The experimental results demonstrate phenomena not previously observed in magnetic resonance systems, including a secondary resonance condition when the amplitude of the excitation equals the modulation frequency. This secondary resonance produces a relatively large steady state magnetisation with Fourier components at harmonics of the modulation frequency.

Modeling individual human motor behavior through model reference iterative learning control.

A computational model is proposed in this paper to capture learning capacity of a human subject adapting his or her movements in novel dynamics. The model uses an iterative learning control algorithm to represent human learning through repetitive processes. The control law performs adaptation using a model designed using experimental data captured from the natural behavior of the individual of interest.

Bayesian model evidence for order selection and correlation testing.

Model selection is a critical component of data analysis procedures, and is particularly difficult for small numbers of observations such as is typical of functional MRI datasets. In this paper we derive two Bayesian evidence-based model selection procedures that exploit the existence of an analytic form for the linear Gaussian model class. Firstly, an evidence information criterion is proposed as a model order selection procedure for auto-regressive models, outperforming the commonly employed Akaike and Bayesian information criteria in simulated data.

Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons.

This paper evaluates the patient-specific seizure prediction performance of pre-ictal changes in bivariate-synchrony between pairs of intracranial electroencephalographic (iEEG) signals within 15min of a seizure in patients with pharmacoresistant focal epilepsy. Prediction horizons under 15min reduce the durations of warning times and should provide adequate time for a seizure control device to intervene. Long-term continuous iEEG was obtained from 6 patients.

A paradigm for epileptic seizure prediction using a coupled oscillator model of the brain.

This paper presents a novel theoretical paradigm for epileptic seizure prediction based on a coupled oscillator model of brain dynamics. This model is used to investigate prediction methods capable of tracking the synchronization changes that may lead to a seizure. Previous results indicate that state-space reconstruction of a coupled oscillator model from an EEG-like signal is ill-posed, therefore, monitoring system synchronization via the EEG signal is unlikely to give advanced warning of imminent seizure activity.

Seizure detection using seizure probability estimation: comparison of features used to detect seizures.

This paper analyses seizure detection features and their combinations using a probability-based scalp EEG seizure detection framework developed by Marc Saab and Jean Gotman. Our method was evaluated on 525 h of data, including 88 seizures in 21 patients. The individual performances of the three features used by Saab and Gotman were compared to six alternative features, and combinations of these nine features were analyzed in order to find a superior detector.

A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms.

The assessment of Diffusion-Weighted MRI (DW-MRI) fibre-tracking algorithms has been limited by the lack of an appropriate 'gold standard'. Practical limitations of alternative methods and physical models have meant that numerical simulations have become the method of choice in practice. However, previous numerical phantoms have consisted of separate fibres embedded in homogeneous backgrounds, which do not capture the true nature of white matter.

On extremum seeking in bioprocesses with multivalued cost functions.

Finding optimal operating modes for bioprocesses has been, for a long time, a relevant issue in bioengineering. The problem is of special interest when it implies the simultaneous optimization of competing objectives. In this paper, we address the problem of finding optimal steady states that achieve the best tradeoff between yield and productivity by using nonmodel-based extremum-seeking control with semiglobal practical stability and convergence properties.

Magnetic resonance described in the excitation dependent rotating frame of reference.

An excitation dependent rotating frame of reference to observe the magnetic resonance phenomenon is introduced in this paper that, to the best of our knowledge, has not been used previously in the nuclear magnetic resonance context. The mathematical framework for this new rotating frame of reference is presented based on time scaling the Bloch equation after transformation to the classical rotating frame of reference whose transverse plane is rotating at the Larmor frequency. To this end, the Bloch equation is rewritten in terms of a magnetisation vector observed from the excitation dependent rotating frame of reference.