Towards a neonatal brain stethoscope: a framework for quantifying the accuracy of subjective and objective detection of neonatal brain injuries, and integration of a bluetooth communication system

Increasing and quantifying the accuracy of perceptual discrimination of brain injuries through auditory images. Appropriate conversion of a neonate EEG signal to an audio signal that is discriminative and sounds natural.The goal of this project is to work in different aspects of the development of a neonatal brain stethoscope. This tool is expected to be an alternative to the current devices, which are expensive and require a very specific training to be used. First, an interactive web interface has been developed to assess the detection of neonatal brain injuries by the users using an alternative interpretation method framework. The webpage presents the currently-used visual method, based on visualisation of electroencephalogram (EEG) traces, and an alternative way that also includes a sonification output and an AI-assisted decision support. Secondly, a connection has been established between an acquisition board and a portable device using Bluetooth Low Energy (BLE). This allows to wirelessly receive and store in real time the EEG signals that come from the electrodes through the acquisition board.La finalidad de este proyecto es trabajar en diferentes aspectos del desarrollo de un estetoscopio cerebral para recién nacidos. Esta herramienta está pensada como alternativa a los sistemas actuales, que generalmente son caros y cuyo uso requiere un entrenamiento muy específico. En primer lugar, se ha desarrollado una interfaz web interactiva para poder analizar y valorar la detección de daño cerebral en neonatos por parte de los usuarios utilizando un método alternativo de interpretación. La página web incluye el método gráfico actual, que corresponde a la visualización de señales electroencefalográficas (EEG), así como una forma alternativa de interpretar las señales, utilizando una representación acústica de éstas y un soporte de decisiones haciendo uso de un sistema asistido por inteligencia artificial. En segundo lugar, se ha establecido una conexión entre la placa de adquisición y una tableta táctil, utilizando la tecnología Bluetooth Low Energy. Esto permite, sin necesidad de cables, recibir y almacenar las señales EEG que provienen de los electrodos a través de la placa de adquisición.La finalitat d'aquest projecte es treballar en diferents aspectes del desenvolupament d'un estetoscopi cerebral per a nounats. Aquesta eina està concebuda com a alternativa als sistemes actuals, que resulten cars i el seu ús requereix un entrenament molt específic. Primer de tot, s'ha desenvolupat una interfície web interactiva per a analitzar i valorar la detecció per part dels usuaris de dany cerebral en neonats emprant un mètode alternatiu d?interpretació. La pàgina web inclou el mètode gràfic actual, que correspon a la visualització de senyals electroencefalogràfics (EEG), i també una forma alternativa d'interpretar les senyals, fent ús tant de la seva representació acústica com d'un suport de decisions emprant un sistema assistit per intel·ligència artificial. En segon lloc, s'ha establert la connexió entre una placa d'adquisició i una tauleta tàctil, fent ús de la tecnologia Bluetooth Low Energy. Això permet, sense necessitat de cables, rebre i emmagatzemar en temps real els senyals EEG que provenen dels elèctrodes a través de la placa d'adquisició

Android Implementation of a Visualisation, Sonification and AI-Assisted Interpretation of Neonatal EEG

Development of deep neural network models for detection of neonatal seizures. Implementation of the detection system as an Android application.The aim of this project is the implementation of an Android App to help healthcare professionals to check newborn health status by observing neonatal EEG signals, without having extensive training in EEG interpretation. To satisfy that aim, this project is divided in three blocks: AI-assisted neonatal EEG interpretation, EEG sonification and graphical user interface. The AI-assisted block has the function to detect neonatal seizures using a fully- convolutional deep neural network using the offline-trained existing model. The sonification work consisted of the adaptation of a previously developed algorithm, based on the phase vocoder, which was already implemented by another UPC student in the Android environment. The developed application core provides both sonification and AI detection functionalities, which are integrated in a user friendly graphical user interface.El objetivo de este proyecto era la implementación de una aplicación Android para ayudar a profesionales del ámbito médico a comprobar el estado de salud de neonatos en base a la observación del electroencefalograma (EEG), sin necesidad de tener mucha experiencia en el campo de la neonatología. Para cumplir dicho objetivo, el proyecto se ha dividido en tres bloques: interpretación asistida por IA, sonificación y interfaz de usuario gráfica. El bloque de IA se encarga de la detección de epilepsias en recién nacidos utilizando una red neuronal totalmente convolucional implementada en Android llevando a cabo la adaptación de un modelo ya existente en Python. El trabajo de sonificación del EEG ha consistido en la adaptación de un algoritmo basado en Phase Vocoder realizado por otro estudiante de la UPC La finalidad de la interfaz gráfica es mostrar de forma integrada la información recibida de la sonificación y la red neuronal para que el usuario pueda interpretarlas con facilidad, de forma que la aplicación resulte útil a un gran número de usuarios.L'objectiu d'aquest projecte era la implementació d'una aplicació Android per ajudar a professionals de l'àmbit mèdic a comprovar l'estat de salut de nounats en base a l'observació de l'electroencefalograma (EEG), sense necessitat de tenir molta experiència en neonatologia. Per tal d'acomplir aquest objectiu, el projecte s'ha dividit en tres blocs: interpretació assistida per IA, sonificació i interfície d'usuari gràfica. El bloc d'IA s'encarrega de la detecció d'epilèpsies en nadons utilitzant una xarxa neuronal totalment convolucional implementada en Android duent a terme l'adaptació d'un model ja existent programat en Python. El treball de sonificació de l'EEG ha consistit en l'adaptació d'un algoritme basat en Phase Vocoder realitzat per un altre estudiant de la UPC La finalitat de la interfície gràfica és mostrar de forma integrada la informació rebuda de la sonificació i la xarxa neuronal perquè l'usuari pugui interpretar-les amb facilitat, de manera que l'aplicació resulti útil a un gran nombre d'usuaris

Analysis of a low-cost EEG monitoring system and dry electrodes toward clinical use in the neonatal ICU

Electroencephalography (EEG) is an important clinical tool for monitoring neurological health. However, the required equipment, expertise, and patient preparation inhibits its use outside of tertiary care. Non-experts struggle to obtain high-quality EEG due to its low amplitude and artefact susceptibility. Wet electrodes are currently used, which require abrasive/conductive gels to reduce skin-electrode impedance. Advances in dry electrodes, which do not require gels, have simplified this process. However, the assessment of dry electrodes on neonates is limited due to health and safety barriers. This study presents a simulation framework for assessing the quality of EEG systems using a neonatal EEG database, without the use of human participants. The framework is used to evaluate a low-cost EEG acquisition system and compare performance of wet and dry (Micro Transdermal Interface Platforms (MicroTIPs), g.tec-g.SAHARA) electrodes using accurately acquired impedance models. A separate experiment assessing the electrodes on adult participants was conducted to verify the simulation framework’s efficacy. Dry electrodes have higher impedance than wet electrodes, causing a reduction in signal quality. However, MicroTIPs perform comparably to wet electrodes at the frontal region and g.tec-g.SAHARA performs well at the occipital region. Using the simulation framework, a 25dB signal-to-noise ratio (SNR) was obtained for the low-cost EEG system. The tests on adults closely matched the simulated results

System level framework for assessing the accuracy of neonatal EEG acquisition

Significant research has been conducted in recent years to design low-cost alternatives to the current EEG monitoring systems used in healthcare facilities. Testing such systems on a vulnerable population such as newborns is complicated due to ethical and regulatory considerations that slow down the technical development. This paper presents and validates a method for quantifying the accuracy of neonatal EEG acquisition systems and electrode technologies via clinical data simulations that do not require neonatal participants. The proposed method uses an extensive neonatal EEG database to simulate analogue signals, which are subsequently passed through electrical models of the skin-electrode interface, which are developed using wet and dry EEG electrode designs. The signal losses in the system are quantified at each stage of the acquisition process for electrode and acquisition board losses. SNR, correlation and noise values were calculated. The results verify that low-cost EEG acquisition systems are capable of obtaining clinical grade EEG. Although dry electrodes result in a significant increase in the skin-electrode impedance, accurate EEG recordings are still achievable

Neurologic Diagnostics in 2035: The Neurology Future Forecasting Series

Innovations and advances in technologies over the past few years have yielded faster and wider diagnostic applications to patients with neurologic diseases. This article focuses on the foreseeable developments of the diagnostic tools available to the neurologist in the next 15 years. Clinical judgment is and will remain the cornerstone of the diagnostic process, assisted by novel technologies, such as artificial intelligence and machine learning. Future neurologists must be educated to develop, cultivate, and rely on their clinical skills, while becoming familiar with novel, often complex, assistive technologies

Informatics for EEG biomarker discovery in clinical neuroscience

Neurological and developmental disorders (NDDs) impose an enormous burden of disease on children throughout the world. Two of the most common are autism spectrum disorder (ASD) and epilepsy. ASD has recently been estimated to affect 1 in 68 children, making it the most common neurodevelopmental disorder in children. Epilepsy is also a spectrum disorder that follows a developmental trajectory, with an estimated prevalence of 1%, nearly as common as autism. ASD and epilepsy co-occur in approximately 30% of individuals with a primary diagnosis of either disorder. Although considered to be different disorders, the relatively high comorbidity suggests the possibility of common neuropathological mechanisms. Early interventions for NDDs lead to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in detecting autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Similarly, no methods for detecting emerging epilepsy before seizures begin are currently known. Because atypical brain development is likely to precede overt behavioral manifestations by months or even years, a critical developmental window for early intervention may be opened by the discovery of brain based biomarkers. Analysis of brain activity with EEG may be under-utilized for clinical applications, especially for neurodevelopment. The hypothesis investigated in this dissertation is that new methods of nonlinear signal analysis, together with methods from biomedical informatics, can extract information from EEG data that enables detection of atypical neurodevelopment. This is tested using data collected at Boston Children’s Hospital. Several results are presented. First, infants with a family history of ASD were found to have EEG features that may enable autism to be detected as early as 9 months. Second, significant EEG-based differences were found between children with absence epilepsy, ASD and control groups using short 30-second EEG segments. Comparison of control groups using different EEG equipment supported the claim that EEG features could be computed that were independent of equipment and lab conditions. Finally, the potential for this technology to help meet the clinical need for neurodevelopmental screening and monitoring in low-income regions of the world is discussed