Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods.

Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than 1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity. Our experimental results show that the individual performance of automated pediatric sleep stagers when evaluated on new subjects is equivalent to the expert-level one reported on adults. Combining the six stagers into ensemble models further boosts the staging accuracy, reaching an overall accuracy of 88.8%, a Cohens kappa of 0.852, and a macro F1-score of 85.8%. At the same time, the ensemble models lead to reduced predictive uncertainty. The results also show that the studied algorithms and their ensembles are robust to concept drift when the training and test data were recorded seven months apart and after clinical intervention. However, we show that the improvements in the staging performance are not necessarily clinically significant although the ensemble models lead to more favorable clinical measures than the six standalone models. Detailed analyses further demonstrate "almost perfect" agreement between the automatic stagers to one another and their similar patterns on the staging errors, suggesting little room for improvement

Comprehensive performance analysis of fully cooperative communication in WBANs

© 2013 IEEE. While relay-based cooperative networks (widely known in the literature as cooperative communication), where relays only forward signals from the sources to the destination, have been extensively researched, fully cooperative systems have not been thoroughly examined. Unlike relay networks, in a fully cooperative network, each node acts as both a source node sending its own data and a relay forwarding its partner's data to the destination. Mutual cooperation between neighboring nodes is believed to improve the overall system error performance, especially when space-time codes are incorporated. However, a comprehensive performance analysis of space-time-coded fully cooperative communication from all three perspectives, namel,y error performance, outage probability, and energy efficiency, is still missing. Answers to the commonly asked questions of whether, in what conditions, and to what extent the space-time-coded fully cooperative communication is better than direct transmission are still unknown. Motivated by this fact and inspired by the increasing popularity of healthcare applications in wireless body area networks (WBANs), this paper derives for the first time a comprehensive performance analysis of a decode-and-forward space-time coded fully cooperative communication network in Rayleigh and Rician fading channels in either identically or non-identically distributed fading scenario. Numerical analysis of error performance, outage probability, and energy efficiency, validated by simulations, show that fully cooperative communication is better than direct transmission from all three aspects in many cases, especially at a low-power and low signal-to-noise ratio regime, which is a typical working condition in WBANs

Unitary differential space-time-frequency codes for MB-OFDM UWB

In a multiple-input multiple-output (MIMO) multiband orthogonal frequency division multiplexing (MB-OFDM) ultra-wideband (UWB) system, coherent detection where the channel state information (CSI) is assumed to be exactly known at the receiver requires the transmission of a large number of symbols for channel estimation, thus reducing the bandwidth efficiency. This paper examines the use of unitary differential space-time frequency codes (DSTFCs) in MB-OFDM UWB, which increases the system bandwidth efficiency due to the fact that no CSI is required for differential detection. The proposed DSTFC MB-OFDM system would be useful when the transmission of multiple channel estimation symbols is impractical or uneconomical. Simulation results show that the application of DSTFCs can significantly improve the bit error performance of conventional differential MB-OFDM system (without MIMO). ©2009 IEEE

An environmental equalizer for underwater acoustic communications Tested at Hydralab III

It is known that small changes in source and receiver locations can cause significant changes in underwater acoustic channel impulse responses. At HYDRALAB III an underwater acoustic experiment was conducted to show that a source depth-shift causes a frequency-shift in the channel impulse response and that such behavior can be used to implement an environmental-based equalizer for underwater communications that compensates for the performance loss due to the source depth-shift

Novel receiver for correlated fading multi-antenna physical network coding TWRNs

Physical Network Coding (PNC) has recently been proposed for multi-antenna Two-Way Relay Networks (TWRNs) with independent fading channels because the total network throughput could be significantly improved. However, PNC for multi-antenna TWRNs with correlated fading channels has not been considered yet. This paper thus considers an important class of multi-antenna TWRNs with the following properties: single-antenna source nodes and two-antenna relay; distance between source nodes and the relay is significantly larger than that between antennas of the relay; and channels between source nodes and the relay are correlated. For such a system, we first propose a novel correlation model that facilitates an easy method to create fading channels with certain correlation properties. We then propose an ovelreceiver design for correlated fading TWRNs. Simulation results show that the proposed receiver design provides much better error performance than the well-known Log-Likelihood Ratio (LLR) algorithm proposed in the literature

First Order Static Excitation Potential: Scheme for Excitation Energies and Transition Moments

We present an approximation scheme for the calculation of the principal excitation energies and transition moments of finite many-body systems. The scheme is derived from a first order approximation to the self energy of a recently proposed extended particle-hole Green's function. A hermitian eigenvalue problem is encountered of the same size as the well-known Random Phase Approximation (RPA). We find that it yields a size consistent description of the excitation properties and removes an inconsistent treatment of the ground state correlation by the RPA. By presenting a hermitian eigenvalue problem the new scheme avoids the instabilities of the RPA and should be well suited for large scale numerical calculations. These and additional properties of the new approximation scheme are illuminated by a very simple exactly solvable model.Comment: 15 pages revtex, 1 eps figure included, corrections in Eq. (A1) and Sec. II

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases

SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification.

BACKGROUND: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. METHODS: Towards interpretability, this work proposes a sequence-to-sequence sleep staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the models decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the models decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. RESULTS: Making sense of the transformers self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. CONCLUSION: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. SIGNIFICANCE: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings