Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units through EEG, ECG and Video Recordings: A Survey

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEG-based NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic

Action compositionality with focus on neurodevelopmental disorders

A central question in motor neuroscience is how the Central Nervous System (CNS) would handle flexibility at the effector level, that is, how the brain would solve the problem coined by Nikolai Bernstein as the “degrees of freedom problem”, or the task of controlling a much larger number of degrees of freedom (dofs) that is often needed to produce behavior. Flexibility is a bless and a curse: while it enables the same body to engage in a virtually infinite number of behaviors, the CNS is left with the job of figuring out the right subset of dofs to use and how to control and coordinate these degrees. Similarly, at the level of perception, the CNS seeks to obtain information pertaining to the action and actors involved based on perceived motion of other people’s dofs. This problem is believed to be solved with a particular dimensionality reduction strategy, where action production would consist of tuning only a few parameters that control and coordinate a small number of motor primitives, and action perception would take place by applying grouping processes that would solve the inverse problem, that is to identify the motor primitives and the corresponding tuning parameters used by an actor. These parameters can encode not only information on the action per se, but also higher-order cognitive cues like body language or emotion. This compositional view of action representation has an obvious parallel with language: we can think of primitives as words and cognitive systems (motor, perceptual) as different languages. Little is known, however, about how words/primitives would be formed from low-level signals measured at each dof. Here we introduce the SB-ST method, a bottom-up approach to find full-body postural primitives as a set of key postures, that is, vectors corresponding to key relationships among dofs (such as joint rotations) which we call spatial basis (SB) and second, we impose a parametric model to the spatio-temporal (ST) profiles of each SB vector. We showcase the method by applying SB vectors and ST parameters to study vertical jumps of young adults (YAD) typically developing (TD) children and children with Developmental Coordination Disorder (DCD) obtained with motion capture. We also go over a number of other topics related with compositionality: we introduce a top-down system of tool-use primitives based on kinematic events between body parts and objects. The kinematic basis of these events is inspired by the hand-to-object velocity signature reported by movement psychologists in the 1980’s. We discuss the need for custom-made movement measurement strategies to study action primitives on some target populations, for example infants. Having the right tools to record infant movement would be of help, for example, to research in Autism Spectrum Disorder (ASD) where early sensorimotor abnormalities were shown to be linked to future diagnoses of ASD and the development of the typical social traits ASD is mostly known for. We continue the discussion on infant movement measurement where we present an alternative way of processing movement data by using textual descriptions as re- placements to the actual movement signals observed in infant behavioral trials. We explore the fact that these clinical descriptions are freely available as a byproduct of the diagnosis process itself. A typical/atypical classification experiment shows that, at the level of sentences, traditionally used text features in Natural Language Processing such as term frequencies and TF-IDF computed from unigrams and bigrams can be potentially helpful. In the end, we sketch a conceptual, compositional model of action generation based on exploratory results on the jump data, according to which the presence of disorders would be related not to differences in key postures, but in how they are controlled throughout execution. We next discuss the nature of action and actor information representation by analyzing a second dataset with arm-only data (bi-manual coordination and object manipulations) with more target populations than in the jump dataset: TD and DCD children, YAD and seniors with and without Parkinson’s Disease (PD). Multiple group analyses on dofs coupled with explained variances at SB representations suggest that the cost of representing a task as performed by an actor may be equivalent to the cost of representing the task alone. Plus, group discriminating information appears to be more compressed than task-only discriminating information, and because this compression happens at the top spatial bases, we conjecture that groups may be recognized faster than tasks

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Estimation of thorax shape for forward modelling in lungs EIT

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models

Rapid generation of subject-specific thorax forward models

For real-time monitoring of lung function using accurate patient geometry, shape information needs to be acquired and a forward model generated rapidly. This paper shows that warping a cylindrical model to an acquired shape results in meshes of acceptable mesh quality, in terms of stretch and aspect ratio

Torso shape detection to improve lung monitoring

Two methodologies are proposed to detect the patient-specific boundary of the chest, aiming to produce a more accurate forward model for EIT analysis. Thus, a passive resistive and an inertial prototypes were prepared to characterize and reconstruct the shape of multiple phantoms. Preliminary results show how the passive device generates a minimum scatter between the reconstructed image and the actual shap

Nanoparticle electrical impedance tomography

We have developed a new approach to imaging with electrical impedance tomography (EIT) using gold nanoparticles (AuNPs) to enhance impedance changes at targeted tissue sites. This is achieved using radio frequency (RF) to heat nanoparticles while applying EIT imaging. The initial results using 5-nm citrate coated AuNPs show that heating can enhance the impedance in a solution containing AuNPs due to the application of an RF field at 2.60 GHz