Tracking Rhythmicity in Biomedical Signals using Sequential Monte Carlo methods

Cyclical patterns are common in signals that originate from natural systems such as the human body and man-made machinery. Often these cyclical patterns are not perfectly periodic. In that case, the signals are called pseudo-periodic or quasi-periodic and can be modeled as a sum of time-varying sinusoids, whose frequencies, phases, and amplitudes change slowly over time. Each time-varying sinusoid represents an individual rhythmical component, called a partial, that can be characterized by three parameters: frequency, phase, and amplitude. Quasi-periodic signals often contain multiple partials that are harmonically related. In that case, the frequencies of other partials become exact integer multiples of that of the slowest partial. These signals are referred to as multi-harmonic signals. Examples of such signals are electrocardiogram (ECG), arterial blood pressure (ABP), and human voice. A Markov process is a mathematical model for a random system whose future and past states are independent conditional on the present state. Multi-harmonic signals can be modeled as a stochastic process with the Markov property. The Markovian representation of multi-harmonic signals enables us to use state-space tracking methods to continuously estimate the frequencies, phases, and amplitudes of the partials. Several research groups have proposed various signal analysis methods such as hidden Markov Models (HMM), short time Fourier transform (STFT), and Wigner-Ville distribution to solve this problem. Recently, a few groups of researchers have proposed Monte Carlo methods which estimate the posterior distribution of the fundamental frequency in multi-harmonic signals sequentially. However, multi-harmonic tracking is more challenging than single-frequency tracking, though the reason for this has not been well understood. The main objectives of this dissertation are to elucidate the fundamental obstacles to multi-harmonic tracking and to develop a reliable multi-harmonic tracker that can track cyclical patterns in multi-harmonic signals

A smartphone-based system for detecting hand tremors in unconstrained environments

The detection of tremors can be crucial for the early diagnosis and proper treatment of some disorders such as Parkinson’s disease. A smartphone-based applica- tion has been developed for detecting hand tremors. This application runs in background and distinguishes hand tremors from common daily activities. This application can facilitate the continuous monitoring of patients or the early detection of this symptom. The evaluation analyzes 1770 accelerometer samples with cross-validation for assessing the ability of the system for processing unknown data, obtaining a sensitivity of 95.8 % and a specificity of 99.5 %. It also analyzes continuous data for some volun- teers for several days, which corroborated its high performance

A neuroprothesis for tremor management

Tremor is the most common movement disorder, affecting ∼ 15 % of people over 50 years old according to some estimates. It appears due to a number of syndromes, being essential tremor and Parkinson's disease the most prevalent among them. None of these conditions is fully understood. Tremor is currently treated through drugs or neurosurgery, but unfortunately, it is not managed effectively in ∼25 % of the patients. Therefore, it constitutes a major cause of loss of independence and quality of life. Various alternative approaches for tremor management are reported in the literature. Among them, those devices that rely on the application of forces to the tremulous segments show a considerable potential. A number of prototypes that exploit this principle are available, spanning fixed devices and orthoses. However, none of them has fulfilled user's expectation for continuous use during daily living. This thesis presents the development and validation of a neuroprosthesis for tremor management. A neuroprosthesis is a system that restores or compensates for a neurological function that is lost. In this case, the neuroprosthesis aims at compensating the functional disability caused by the tremor. To this end, it applies forces to the tremulous limb through the control of muscle contraction, which is modulated according to the characteristics of the tremor. The concept design envisions the device as a textile that is worn on the affected limb, thus meeting the usability requirements defined by the patients. The development of the neuroprosthesis comprised the following tasks: 1. The development of a concept design of the neuroprosthesis, which incorporates state of the art knowledge on tremor, and user's needs. 2. The design and validation of a cognitive interface that parameterizes the tremor in functional contexts. This interface provides the information that the neuroprosthesis uses for tremor suppression. Two versions are developed: a multimodal interface that integrates the recordings of the whole neuromusculoskeletal system, and an interface incorporating only wearable movement sensors. The latter is intended for the functional validation of the neuroprosthesis, while the former is a proof of concept of an optimal interface for this type of applications. 3. The development of a novel approach for tremor suppression through transcutaneous neurostimulation. The approach relies on the modulation of muscle cocontraction as a means of attenuating the tremor without the need of conventional actuators. The experimental validation here provided demonstrates the feasibility and interest of the approach. In parallel with the validation of the neuroprosthesis, I performed a detailed study on the physiology of motoneurons in tremor, given the lack of a complete description of its behavior. The outcome of this study contributes to the interpretation of the results obtained with the neuroprosthesis, and opens new research lines, both related to alternative interventions and basic neuroscience. In summary, the results here presented demonstrate that tremor may be accurately parameterized while the patient performs functional activities, and that this information may be exploited to drive a neuroprosthesis for tremor management. Furthermore, the novel approach for tremor suppression presented in this dissertation constitutes a potential approach for treating upper limb tremor, either alone, or as a complement to pharmacotherapy. These results encourage the validation of the neuroprosthesis in a large cohort of patients, in order to enable its translation to the market. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El temblor es el trastorno del movimiento más común, afectando, según algunas estimaciones, al ∼15 % de la población de más de 50 años. Existen diversos "síndromes" que causan temblor, siendo el temblor esencial y la enfermedad de Parkinson los que presentan mayor prevalencia. Además, cabe resaltar que no existe una descripción completa de ninguno de ellos. En la actualidad el temblor se trata mediante una serie de fármacos o neurocirugía. A pesar de ello, el ∼ 25 % de los pacientes sufren problemas funcionales debido a su condición. Por tanto, es evidente que el temblor constituye una de las principales causas de dependencia y pérdida de calidad de vida. Realizando una revisión de las publicaciones científicas sobre el temblor, se observa que se ha propuesto un considerable número de tratamientos alternativos. Entre ellos destacan los dispositivos que se fundamentan en la aplicación de fuerzas sobre los segmentos afectados por el temblor, de los que ya se ha evaluado una serie de prototipos. Estos abarcan desde dispositivos fijados a otras estructuras hasta ortesis. Sin embargo, ninguno de ellos satisface las expectativas de los usuarios para su uso durante el día a día. Esta tesis presenta el diseño y validación de una neruoprótesis para el tratamiento del temblor. Una neuroprótesis es un sistema que reemplaza o compensa una función neurológica perdida. En este caso, la neuroprótesis tiene como objetivo compensar la discapacidad motora causada por el temblor. Para ello aplica fuerzas al miembro afectado a través del control del nivel de contracción muscular, que se modula según las características del temblor. El diseño conceptual contempla al dispositivo como un textil que se viste en el brazo afectado, satisfaciendo los requisitos de usabilidad definidos por los pacientes. El desarrollo de la neuroprótesis abarcó las siguientes tareas: 1. El desarrollo del diseño conceptual de la neuroprótesis, que incorpora el conocimiento actual sobre el temblor, y las necesidades de los usuarios. 2. El diseño y validación de una interfaz cognitiva que parametriza el temblor durante tareas funcionales. La información obtenida con esta interfaz es usada por la neuroprótesis para modular la corriente aplicada mediante técnicas de neuroestimulación. Se desarrollan dos versiones de la interfaz cognitiva: una interfaz multimodal que integra información de todo el sistema neuromusculoesquelético, y una interfaz que implementa únicamente sensores vestibles de movimiento. La segunda interfaz fue la que se usó durante la validación funcional de la neuroprótesis, mientras que la primera es una prueba de concepto de una interfaz óptima para este tipo de aplicaciones. 3. El desarrollo de una nueva aproximación para la supresión del temblor mediante neuroestimulación transcutánea. Dicha aproximación se fundamenta en la modulación del grado de co-contracción de los músculos afectados como forma de atenuar el temblor, sin necesidad de usar actuadores convencionales. La evaluación experimental sirvió para demostrar la viabilidad e interés de la intervención. En paralelo a la validación de la neuroprótesis, llevé a cabo un estudio detallado de la fisiología de las motoneuronas en el caso del temblor, dado que no existe una descripción del funcionamiento de las mismas en el caso de este trastorno. Este estudio sirve para ayudar a la interpretación de los resultados de la neuroprótesis, y para abrir una serie de líneas futuras de investigación, tanto sobre nuevas intervenciones para el temblor, como sobre neurociencia básica. En resumen, los resultados que se presentan en esta tesis demuestran que es posible parametrizar de una forma precisa el temblor durante la realización de tareas funcionales, y que esta información sirve para controlar una neuroprótesis para el tratamiento del temblor. Además, la nueva aproximación para la compensación del temblor que se presenta tiene el potencial de convertirse en un tratamiento alternativo para el temblor de miembro superior, ya sea de forma independiente o como complemento a los fármacos. Estos resultados alientan la validación de la neuroprótesis en una cohorte grande de pacientes, con el objetivo de facilitar su transferencia al mercado

Selective attention and speech processing in the cortex

In noisy and complex environments, human listeners must segregate the mixture of sound sources arriving at their ears and selectively attend a single source, thereby solving a computationally difficult problem called the cocktail party problem. However, the neural mechanisms underlying these computations are still largely a mystery. Oscillatory synchronization of neuronal activity between cortical areas is thought to provide a crucial role in facilitating information transmission between spatially separated populations of neurons, enabling the formation of functional networks. In this thesis, we seek to analyze and model the functional neuronal networks underlying attention to speech stimuli and find that the Frontal Eye Fields play a central 'hub' role in the auditory spatial attention network in a cocktail party experiment. We use magnetoencephalography (MEG) to measure neural signals with high temporal precision, while sampling from the whole cortex. However, several methodological issues arise when undertaking functional connectivity analysis with MEG data. Specifically, volume conduction of electrical and magnetic fields in the brain complicates interpretation of results. We compare several approaches through simulations, and analyze the trade-offs among various measures of neural phase-locking in the presence of volume conduction. We use these insights to study functional networks in a cocktail party experiment. We then construct a linear dynamical system model of neural responses to ongoing speech. Using this model, we are able to correctly predict which of two speakers is being attended by a listener. We then apply this model to data from a task where people were attending to stories with synchronous and scrambled videos of the speakers' faces to explore how the presence of visual information modifies the underlying neuronal mechanisms of speech perception. This model allows us to probe neural processes as subjects listen to long stimuli, without the need for a trial-based experimental design. We model the neural activity with latent states, and model the neural noise spectrum and functional connectivity with multivariate autoregressive dynamics, along with impulse responses for external stimulus processing. We also develop a new regularized Expectation-Maximization (EM) algorithm to fit this model to electroencephalography (EEG) data

Characterizing Unstructured Motor Behaviors in the Epilepsy Monitoring Unit

Key advancements in recording hardware, data computation, clinical care, and cognitive science continue to drive new possibilities in how humans and machines can interact directly through thought. Neural data analyses with these advancements has progressed neuroscience research in functional brain mapping and brain-computer interfaces (BCIs). Much of our knowledge about BCIs is informed by data collected through carefully controlled experiments. Constraining BCI experiments with structured paradigms allows researchers to collect a high number of consistent data in a short amount of time, while also controlling for external confounds. Very little is currently known about how well these task-based relationships extend to daily life, in part because collecting data outside of the lab is challenging. To further understand natural brain activity, we must study more complex behaviors in more environmentally relevant settings. The results of this dissertation address three general challenges to studying neural correlates to unstructured behaviors. First, we continuously monitored unstructured human movements in the epilepsy monitoring unit using a video sensor synchronized to clinical intracortical electrodes. Second, we annotated unstructured behaviors from these video using both manual and computer vision methods. Finally, analyzed neural features with respect to unstructured human movements, and evaluated the performance of features identified in previous task-based studies. The preliminary nature of this work means that a majority of our demonstrations are whether the continuous paradigm can be leveraged, how one might go about leveraging it, and evaluations that tie our results back to earlier task-based studies. Our advances here motivate future works that focus more intently on what types of behaviors and neural signal features to explore

Accelerometry based detection of epileptic seizures

Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there is still a large group of epilepsy patients that continues to have frequent seizures. For these patients automated detection of epileptic seizures can be of great clinical importance. Seizure detection can influence daily care or can be used to evaluate treatment effect. Furthermore automated detection can be used to trigger an alarm system during seizures that might be harmful to the patient. This thesis focusses on accelerometry (ACM) based seizure detection. A detailed overview is provided, on the perspectives for long-term epilepsy monitoring and automated seizure detection. The value of accelerometry for seizure detection is shown by means of a clinical evaluation and the first steps are made towards automatic detection of epileptic seizures based on ACM. With accelerometers movements are recorded. A large group of epileptic seizures manifest in specific movement patterns, so called motor seizures. Chapter 2 of this thesis presents an overview of the published literature on available methods for epileptic seizure detection in a long-term monitoring context. Based on this overview recommendations are formulated that should be used in seizure detection research and development. It is shown that for seizure detection in home environments, other sensor modalities besides EEG become more important. The use of alternative sensor modalities (such as ACM) is relatively new and so is the algorithm development for seizure detection based on these measures. It was also found that for both the adaptation of existing techniques and the development of new algorithms, clinical information should be taken more into account. The value of ACM for seizure detection is shown by means of a clinical evaluation in chapter 3. Here 3-D ACM- and EEG/video-recordings of 18 patients with severe epilepsy are visually analyzed. A striking outcome presented in this chapter is the large number of visually detected seizures versus the number of seizures that was expected on forehand and the number of seizures that was observed by the nurses. These results underscore the need for an automatic seizure detection device even more, since in the current situation many seizures are missed and therefore it is possible that patients do not get the right (medical) treatment. It was also observed that 95% of the ACM-patterns during motor seizures are sequences of three elementary patterns: myoclonic, tonic and clonic patterns. These characteristic patterns are a starting point for the development of methods for automated seizure detection based on ACM. It was decided to use a modular approach for the detection methodology and develop algorithms separately for motor activity in general, myoclonic seizures and tonic seizures. Furthermore, clinical information is incorporated in the detection methodology. Therefore in this thesis features were used that are either based on the shape of the patterns of interest as described in clinical practice (chapter 4 and 7), or the features were based on a physiological model with parameters that are related to seizure duration and intensity (chapter 5 and 6). In chapter 4 an algorithm is developed to distinguish periods with and without movement from ACM-data. Hence, when there is no movement there is no motor seizure. The amount of data that needs further analysis for seizure detection is thus reduced. From 15 ACM-signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space a linear threshold function is used for classification. For training and testing the algorithm ACM data along with video data are used from nocturnal recordings in mentally retarded patients with severe epilepsy. Using this algorithm the amount of data that needs further analysis is reduced considerably. The results also indicate that the algorithm is robust for fluctuations across patients and thus there is no need for training the algorithm for each new patient. For the remaining data it needs to be established whether the detected movement is seizure related or not. To this purpose a model is developed for the accelerometer pattern measured on the arm during a myoclonic seizure (chapter 5). The model consists of a mechanical and an electrophysiological part. This model is used as a matched wavelet filter to detect myoclonic seizures. In chapter 6 the model based wavelet is compared to three other time frequency measures: the short time Fourier transform, the Wigner distribution and the continuous wavelet transform using a Daubechies wavelet. All four time-frequency methods are evaluated in a linear classification setup. Data from mentally retarded patients with severe epilepsy are used for training and evaluation. The results show that both wavelets are useful for detection of myoclonic seizures. On top of that, our model based wavelet has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiological meaningful. Besides myoclonic seizures, the model is also useful for the detection of clonic seizures; physiologically these are repetitive myoclonic seizures. Finally for the detection of tonic seizures, in chapter 7 a set of features is studied that incorporate the mean characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification in the multi-dimensional feature space. For training and testing the algorithm, again data are used from recordings in mentally retarded patients with severe epilepsy. The results show that our approach is useful for the automated detection of tonic seizures based on 3-D ACM and that it is a promising contribution in a complete multi-sensor seizure detection setup

Functional connectivity inference from time-series of events and application to computer networks

Today’s commercial and Internet Service Provider’s networks are large, heterogeneous, fast-evolving and commonly emit millions of events per second. Monitoring, responding to, and predicting incidents is a key challenge for network management operators. This thesis argues that the concept of functional connectivity, widely used in neuroscience and defined in terms of the presence of statistical dependencies between nodes, can unlock informational value about event logs and assist operators in identifying (and even predicting) service outages. However, existing functional connectivity inference methods are not adapted to event data from computer networks. These methods may either require unavailable models of event propagation, be computationally too costly for large networks and/or long recordings, be not adapted to sparse and discrete activity, and/or assume a static network topology. We thus first describe in depth a major commercial network to highlight the challenges faced by network operators and the opportunities offered by thinking in terms of functional connectivities. Next, using a pair of independent Bernoulli processes as reference, we develop a new statistic aimed at measuring coupling strength by quantifying deviation from independence. However, because many statistics will be large by chance, identifying functional edges from the distribution of statistics over every pair of nodes is challenging. Hence, we then develop a method that infers, in specific contexts, the function that associates each statistic to the probability it accounts for the presence of a functional edge. Next, using the previously described statistic, we propose a novel framework to infer a time-varying functional topology from the time-series of emitted events. Applying this paradigm to two major commercial networks, we show that it can reveal a priori unknown groups of devices providing particular services. Finally, we argue that this thesis has implications beyond assisting network monitoring, such as enabling more robust inference of functional connectivity in neuroscience

Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces

Nineteenth Annual Conference on Manual Control

No abstract availabl

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology