44 research outputs found
EXPERIMENTAL-COMPUTATIONAL ANALYSIS OF VIGILANCE DYNAMICS FOR APPLICATIONS IN SLEEP AND EPILEPSY
Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy
Beyond time-homogeneity for continuous-time multistate Markov models
Multistate Markov models are a canonical parametric approach for data
modeling of observed or latent stochastic processes supported on a finite state
space. Continuous-time Markov processes describe data that are observed
irregularly over time, as is often the case in longitudinal medical and
biological data sets, for example. Assuming that a continuous-time Markov
process is time-homogeneous, a closed-form likelihood function can be derived
from the Kolmogorov forward equations -- a system of differential equations
with a well-known matrix-exponential solution. Unfortunately, however, the
forward equations do not admit an analytical solution for continuous-time,
time-inhomogeneous Markov processes, and so researchers and practitioners often
make the simplifying assumption that the process is piecewise time-homogeneous.
In this paper, we provide intuitions and illustrations of the potential biases
for parameter estimation that may ensue in the more realistic scenario that the
piecewise-homogeneous assumption is violated, and we advocate for a solution
for likelihood computation in a truly time-inhomogeneous fashion. Particular
focus is afforded to the context of multistate Markov models that allow for
state label misclassifications, which applies more broadly to hidden Markov
models (HMMs), and Bayesian computations bypass the necessity for
computationally demanding numerical gradient approximations for obtaining
maximum likelihood estimates (MLEs)
A Large-Scale Study of a Sleep Tracking and Improving Device with Closed-loop and Personalized Real-time Acoustic Stimulation
Various intervention therapies ranging from pharmaceutical to hi-tech
tailored solutions have been available to treat difficulty in falling asleep
commonly caused by insomnia in modern life. However, current techniques largely
remain ill-suited, ineffective, and unreliable due to their lack of precise
real-time sleep tracking, in-time feedback on the therapies, an ability to keep
people asleep during the night, and a large-scale effectiveness evaluation.
Here, we introduce a novel sleep aid system, called Earable, that can
continuously sense multiple head-based physiological signals and simultaneously
enable closed-loop auditory stimulation to entrain brain activities in time for
effective sleep promotion. We develop the system in a lightweight, comfortable,
and user-friendly headband with a comprehensive set of algorithms and dedicated
own-designed audio stimuli. We conducted multiple protocols from 883 sleep
studies on 377 subjects (241 women, 119 men) wearing either a gold-standard
device (PSG), Earable, or both concurrently. We demonstrate that our system
achieves (1) a strong correlation (0.89 +/- 0.03) between the physiological
signals acquired by Earable and those from the gold-standard PSG, (2) an 87.8
+/- 5.3% agreement on sleep scoring using our automatic real-time sleep staging
algorithm with the consensus scored by three sleep technicians, and (3) a
successful non-pharmacological stimulation alternative to effectively shorten
the duration of sleep falling by 24.1 +/- 0.1 minutes. These results show that
the efficacy of Earable exceeds existing techniques in intentions to promote
fast falling asleep, track sleep state accurately, and achieve high social
acceptance for real-time closed-loop personalized neuromodulation-based home
sleep care.Comment: 33 pages, 8 figure
Automation of Sleep Staging
This thesis primarily covers the automation problem for sleep versus awake detection, which is sometimes accomplished by differentiating the various sleep stages prior to clustering. This thesis documents various experimentation into areas where the performance can be improved, including classifer design and feature selection from EEG, EOG and Context.
In terms of classifers, it was found that the neural network MLP outperforms the continuous Hidden Markov Model with an accuracy of 91.91%, and additional performance requires better feature sets and more training data.
Improved EEG features based on time frequency representation were optimized to differentiate Awake with 93.52% sensitivity and 94.60% specificity, differentiate REM with 96.12% sensitivity and 93.63% specificity, differentiate
Stages II and III with 96.81% sensitivity and 89.28% specificity, and differentiate Stages III and IV with 93.60% sensitivity and 90.43% specificity.
Due to the limited data set, an example of applying contextual information using a One-Cycle-Duo-Direction model was built and shown to improve
EEG features by up to 10%. This level of performance is comparable if not superior to the human scorer accuracy of 88% to 94%.
This thesis improved some aspects of sleep staging automation, but due to the limitations on resources, the full potential of these improvements could not be demonstrated. To further develop these improvements, additional
data sets customized by sleep staging experts is crucial
Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated for Markov chain modelling from 100 trials of 128-channel EEG signals during two different face perception tasks. Prediction accuracies with such finite Markov chain models for synchrostate transition are also compared, under a data-partitioning based cross-validation scheme.The work
presented in this paper was supported by FP7 EU funded MICHELANGELO
project, Grant Agreement #288241
Automatic neonatal sleep stage classification:A comparative study
Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study