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

A review of automated sleep stage scoring based on physiological signals for the new millennia

Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results: Our review shows that all of these signals contain information for sleep stage scoring. Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost

Learning from Teacher's Eye Movement: Expertise, Subject Matter and Video Modeling

How teachers' eye movements can be used to understand and improve education is the central focus of the present paper. Three empirical studies were carried out to understand the nature of teachers' eye movements in natural settings and how they might be used to promote learning. The studies explored 1) the relationship between teacher expertise and eye movement in the course of teaching, 2) how individual differences and the demands of different subjects affect teachers' eye movement during literacy and mathematics instruction, 3) whether including an expert's eye movement and hand information in instructional videos can promote learning. Each study looked at the nature and use of teacher eye movements from a different angle but collectively converge on contributions to answering the question: what can we learn from teachers' eye movements? The paper also contains an independent methodology chapter dedicated to reviewing and comparing methods of representing eye movements in order to determine a suitable statistical procedure for representing the richness of current and similar eye tracking data. Results show that there are considerable differences between expert and novice teachers' eye movement in a real teaching situation, replicating similar patterns revealed by past studies on expertise and gaze behavior in athletics and other fields. This paper also identified the mix of person-specific and subject-specific eye movement patterns that occur when the same teacher teaches different topics to the same children. The final study reports evidence that eye movement can be useful in teaching; by showing increased learning when learners saw an expert model's eye movement in a video modeling example. The implications of these studies regarding teacher education and instruction are discussed.PHDEducation & PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145853/1/yizhenh_1.pd

Automated sleep stage detection and classification of sleep disorders

Studies have demonstrated that more than 1 million Australians experience some sort of sleep-related disorder in their lifetime [12]. In order to improve the diagnostic and clinical treatment of sleep disorders, the first important step is to identify or automatically detect the sleep stages. The most common method, known as the visual sleep stage scoring, can be a tedious and time-consuming process. Because of that, there is a need to create or develop an improved automatic sleep stage detection method to assist the sleep physician to efficiently and accurately evaluate the sleep stages of patients or non-patients. This research project consisted of two parts. The first part focused on the automatic sleep stages detection based on two individual bio-signals, which made up an overnight polysomnography (PSG), such as the electroencephalogram (EEG), and electrooculogram (EOG). Several features were extracted from these two bio-signals in the time and frequency domains. The decision tree and classification methods were utilised for the classification of the sleep stages. The second part of this project focused on the automatic classification of different sleep and psychiatric disorders, such as patients with periodic limb movements of sleep (PLMs), sleep apnea-hypopnea syndrome (SAHS), primary insomnia, schizophrenia and healthy sleep. Different PSG parameters were computed for the classification of sleep disorders, such as descriptive statistics of sleep architecture. In conclusion, the advantage of an automatic sleep stage detection method based on a single-channel EEG or EOG signal can be undertaken with portable sleep stage recording instead of full the PSG system, which includes multichannel bio-signals. An automatic classification method of sleep and psychiatric disorders based on the descriptive statistics of sleep architecture statistics was found to be an effective technique for screening sleep and psychiatric disorders. This classification method can assist physicians to quickly undertake a diagnostic procedure

Noninvasive Dynamic Characterization of Swallowing Kinematics and Impairments in High Resolution Cervical Auscultation via Deep Learning

Swallowing is a complex sensorimotor activity by which food and liquids are transferred from the oral cavity to the stomach. Swallowing requires the coordination between multiple subsystems which makes it subject to impairment secondary to a variety of medical or surgically related conditions. Dysphagia refers to any swallowing disorder and is common in patients with head and neck cancer and neurological conditions such as stroke. Dysphagia affects nearly 9 million adults and causes death for more than 60,000 yearly in the US. In this research, we utilize advanced signal processing techniques with sensor technology and deep learning methods to develop a noninvasive and widely available tool for the evaluation and diagnosis of swallowing problems. We investigate the use of modern spectral estimation methods in addition to convolutional recurrent neural networks to demarcate and localize the important swallowing physiological events that contribute to airway protection solely based on signals collected from non-invasive sensors attached to the anterior neck. These events include the full swallowing activity, upper esophageal sphincter opening duration and maximal opening diameter, and aspiration. We believe that combining sensor technology and state of the art deep learning architectures specialized in time series analysis, will help achieve great advances for dysphagia detection and management in terms of non-invasiveness, portability, and availability. Like never before, such advances will enable patients to get continuous feedback about their swallowing out of standard clinical care setting which will extremely facilitate their daily activities and enhance the quality of their lives

Utilizing EEG Signal in Music Information Retrieval

Master'sMASTER OF SCIENC

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity