Christian O'Reilly

Manual processing of sleep recordings is extremely time-consuming. Efforts to automate this process have shown promising results, but automatic systems are generally evaluated on private databases, not allowing accurate cross-validation with other systems. In lacking a common benchmark, the relative performances of different systems are not compared easily and advances are compromised. To address this fundamental methodological impediment to sleep study, we propose an open-access database of polysomnographic biosignals. To build this database, whole-night recordings from 200 participants [97 males (aged 42.9 ± 19.8 years) and 103 females (aged 38.3 ± 18.9 years); age range: 18–76 years] were pooled from eight different research protocols performed in three different hospital-based sleep laboratories. All recordings feature a sampling frequency of 256 Hz and an electroencephalography (EEG) montage of 4–20 channels plus standard electro-oculography (EOG), electromyography (EMG), electrocardiography (ECG) and respiratory signals. Access to the database can be obtained through the Montreal Archive of Sleep Studies (MASS) website (http://www.ceams-carsm.ca/en/MASS), and requires only affiliation with a research institution and prior approval by the applicant's local ethical review board. Providing the research community with access to this free and open sleep database is expected to facilitate the development and cross-validation of sleep analysis automation systems. It is also expected that such a shared resource will be a catalyst for cross-centre collaborations on difficult topics such as improving inter-rater agreement on sleep stage scoring.

BACKGROUND: A convergence of studies has revealed sleep spindles to be associated with sleep-related cognitive processing and even with fundamental waking state capacities such as intelligence. However, some spindle characteristics, such as propagation direction and delay, may play a decisive role but are only infrequently investigated because of technical difficulties.
NEW METHOD: A new methodology for assessing sleep spindle propagation over the human scalp using noninvasive electroencephalography (EEG) is described. This approach is based on the alignment of time-frequency representations of spindle activity across recording channels.
RESULTS: This first of a two-part series concentrates on framing theoretical considerations related to EEG spindle propagation and on detailing the methodology. A short example application is provided that illustrates the repeatability of results obtained with the new propagation measure in a sample of 32 night recordings. A more comprehensive experimental investigation is presented in part two of the series.
COMPARISON WITH EXISTING METHOD(S): Compared to existing methods, this approach is particularly well adapted for studying the propagation of sleep spindles because it estimates time delays rather than phase synchrony and it computes propagation properties for every individual spindle with windows adjusted to the specific spindle duration.
CONCLUSIONS: The proposed methodology is effective in tracking the propagation of spindles across the scalp and may thus help in elucidating the temporal aspects of sleep spindle dynamics, as well as other transient EEG and MEG events. A software implementation (the Spyndle Python package) is provided as open source software.

BACKGROUND:This communication is the second of a two-part series that describes and tests a new methodology for assessing the propagation of EEG sleep spindles. Whereas the first part describes the methodology in detail, this part proposes a thorough evaluation of the approach by applying it to a sample of laboratory sleep recordings.
NEW METHOD:The tested methodology is based on the alignment of time-frequency representations of spindle activity across recording channels and is used for assessing sleep spindle propagation over the human scalp using noninvasive EEG.
RESULTS: Spindle propagation displays features that suggest wave displacements of global synaptic potential fields. Propagation patterns that are coherent (as opposed to random), laterally symmetrical, and highly repeatable within and between subjects were observed. Propagation was slower from posterior to anterior and from central to lateral brain regions than in the opposite directions. Propagation speeds varied between 2.3 and 7.0m/s were obtained. A distinct grouping of propagation properties was noted for a small cluster of frontal electrodes. No propagation between distantly separated scalp locations was observed. The values of spindle characteristics such as average frequency, RMS amplitude, frequency slope, and duration, depend largely on propagation direction but are only mildly correlated with propagation delay.
COMPARISON WITH EXISTING METHOD(S): Results obtained are in line with many results published in the literature and offer new measures for describing sleep spindle behavior.
CONCLUSIONS: Propagation properties provide new information about sleep spindle behaviors and thus allow more precise automated assessments of spindle-related functions.

This keynote paper is divided into two parts. On the one hand, it explores how the modeling of pen strokes can be exploited to design biomedical tools allowing the analysis of neuromuscular systems with the objective of developing a diagnostic protocol useful in assessing brain stroke risk factors. On the other hand, it explains how the methodology followed to model a neuromuscular system producing handwriting strokes can be generalized, by means of various strides, to model the Solar System, the Milky Way and the whole Universe. The conducting thread that links up such apparently unrelated pattern recognition problems is the Central Limit Theorem.

In this paper we discuss the existence of oscillations in a specific recurrent neural network: the 3-node network with two weight parameters and one time delay. Simple and practical criteria for fixing the range of the parameters in this network model are derived. Computer simulations are used to show typical patterns and to point out that these criteria are only sufficient conditions for such oscillations to happen.

Lorsqu'employée en reconnaissance des formes, la modélisation des mouvements humains vise, entre autres, à procurer certaines assises théoriques au traitement en ligne de l'écriture manuscrite et à fournir des connaissances fondamentales pouvant servir de balises dans ...

This paper proposes an on-line signature representation based on Sigma–Lognormal modeling. It briefly overviews the prior art published on signature modeling and on human movement analysis for handwriting. Then it presents the Sigma–Lognormal paradigm and gives the key steps for the development of a completely automatic parameter extractor for complex human movements. Results of its application on signatures from a proprietary database and from the SVC2004 database are reported and analyzed in regards of the curve fitting quality. Other possible applications and future works are also suggested.

In this paper, the existence of oscillations for a type of recurrent neural network with time delays between neural interconnections is investigated. A new set of criteria is proposed to guarantee the existence of oscillations for a network model. The results are shown to be the generalization of some published works dealing with no delay systems.

In this paper, the existence of oscillations for a class of recurrent neural networks with time delays between neural interconnections is investigated. By using the fixed point theory and Liapunov functional, we prove that a recurrent neural network might have a unique equilibrium point which is unstable. This particular type of instability, combined with the boundedness of the solutions of the system, will force the network to generate a permanent oscillation. Some necessary and sufficient conditions for these oscillations are obtained. Simple and practical criteria for fixing the range of parameters in this network are also derived. Typical simulation examples are presented.

In the context of the occidental population aging, new preventive approaches must be developed to reduce the disability related to brain stroke in the elderly. The stroke susceptibility assessment based on the analysis of human movements is one of the potential avenues needing investigation. As a first step in this direction, this paper reports results on the relationship linking the most important stroke risk factors to some characteristics of human movement. Various features were extracted using the Sigma–Lognormal model on 1440 stereotypical triangular movements performed by 120 subjects having different health conditions. These features were combined through a linear modeling to maximize the predictability of presence of stroke risk factors in the studied cohort. The receiver operating characteristic (ROC) curve and the area under this curve (AUC) were used to evaluate the clinical significance of this relationship. Using only the information derived from the movements, the six tested risk factors (cardiac problems, diabetes mellitus, hypercholesterolemia, hypertension, obesity, and cigarette smoking) can be predicted with an AUC ranging from .68 to .82.

In this paper, 14 healthy subjects in two age groups have produced rapid handwriting strokes with a direction reversal. The delta-lognormal model was used to obtain a detailed description of the velocity of these movements and of the neuromuscular synergy that produces them. This modeling also allowed the derivation of new hypothesis on the nature of the slowing effect due to aging (i.e., a direct effect or a coping strategy) and on its repartition on the different steps of the movement production (i.e., its preparation versus its execution). Our analysis revealed a substantial increase of neuromuscular response delays and a decrease of the command amplitudes with age. For the parameters that show a significant decrease in performance, the agonist and antagonist systems were affected similarly. In addition, we observed that the age has a proportional effect on the various time characteristics of the movements and that even in the case of a significant slowing down of the neuromuscular systems, the elderly can still achieve optimal movement responses, characterized by the use of a single delta-lognormal primitive. This performance might be related to the preservation of some movement timing properties and relationships between the agonist and the antagonist neuromuscular systems.

The main goal of this work is to determine whether a computer mouse can be used as a low-cost device for the acquisition of two-dimensional human movement velocity signals in the context of psychophysical studies and biomedical applications. A comprehensive overview of the related literature is presented, and the problem of characterizing mouse movement acquisition is analyzed and discussed. Then, the quality of velocity signals acquired with this kind of device is measured on horizontal oscillatory movements by comparing the mouse data to the signals acquired simultaneously by a video motion tracking system and a digitizing tablet. A synthesis of the information gathered in this work indicates that the computer mouse can be used for the reliable acquisition of biosignals in the context of human movement studies, particularly for many applications dealing with the velocity of the end effector of the upper limb. This paper concludes by discussing the possibilities and limitations of such use.

Fast reaching movements are an important component of our daily interaction with the world and are consequently under investigation in many fields of science and engineering. Today, useful models are available for such studies, with tools for solving the inverse dynamics problem involved by these analyses. These tools generally provide a set of model parameters that allows an accurate and locally optimal reconstruction of the original movements. Although the solutions that they generate may provide a data curve fitting that is sufficient for some pattern recognition applications, the best possible solution is often necessary in others, particularly those involving neuroscience and biomedical signal processing. To generate these solutions, we present a globally optimal parameter extractor for the delta-lognormal modeling of reaching movements based on the branch-and-bound strategy. This algorithm is used to test the impact of white noise on the delta-lognormal modeling of reaching movements and to benchmark the state-of-the-art locally optimal algorithm. Our study shows that, even with globally optimal solutions, parameter averaging is important for obtaining reliable figures. It concludes that physiologically derived rules are necessary, in addition to global optimality, to achieve meaningful ∆Λ extractions which can be used to investigate the control patterns of these movement primitives.

This paper reports the results of a model-based analysis of movements gathered in a 4×4 experimental design of speed/accuracy tradeoffs with variable target distances and width. Our study was performed on a large (120 participants) and varied sample (both genders, wide age range, various health conditions). The delta-lognormal equation was used for data modeling to investigate the interaction between the output of the agonist and the antagonist neuromuscular systems. Empirical observations show that the subjects must correlate more tightly the impulse commands sent to both neuromuscular systems in order to achieve good performances as the difficulty of the task increases whereas the correlation in the timing of the neuromuscular action co-varies with the size of the geometrical properties of the task. These new phenomena are discussed under the paradigm provided by the Kinematic Theory and new research hypotheses are proposed for further investigation of the speed/accuracy tradeoffs.