Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms

A method for AI assisted human interpretation of neonatal EEG

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.

An EEG analysis framework through AI and sonification on low power IoT edge devices

This study explores the feasibility of implementation of an analysis framework of neonatal EEG, including ML, sonification and intuitive visualization, on a low power IoT edge device. Electroencephalography (EEG) analysis is a very important tool to detect brain disorders. Neonatal seizure detection is a known, challenging problem. Under-resourced communities across the globe are particularly affected by the cost associated with EEG analysis and interpretation. Machine learning (ML) techniques have been successfully utilized to automate seizure detection in neonatal EEG, in order to assist a healthcare professional in visual analysis. Several usage scenarios are reviewed in this study. It is shown that both sonification and ML can be efficiently implemented on low-power edge platforms without any loss of accuracy. The developed platform can be easily expanded to address EEG analysis applications in neonatal and adult population.

Grading hypoxic-ischemic encephalopathy in neonatal EEG with convolutional neural networks and quadratic time-frequency distributions

OBJECTIVE

To develop an automated system to classify the severity of hypoxic-ischaemic encephalopathy injury (HIE) in neonates from the background electroencephalogram (EEG).

METHOD

By combining a quadratic time{frequency distribution (TFD) with a convolutional neural network, we develop a system that classifies 4 EEG grades of HIE. The network learns directly from the two- dimensional TFD through 3 independent layers with convolution in the time, frequency, and time{frequency directions. Computationally efficient algorithms make it feasible to transform each 5 minute epoch to the time-frequency domain by controlling for oversampling to reduce both computation and computer memory. The system is developed on EEG recordings from 54 neonates. Then the system is validated on a large unseen dataset of 338 hours of EEG recordings from 91 neonates obtained across multiple international centres.

RESULTS

The proposed EEG HIE-grading system achieves a leave-one-subject-out testing accuracy of 88.9% and kappa of 0.84 on the development dataset. Accuracy for the large unseen test dataset is 69.5% (95% confidence interval, CI: 65.3 to 73.6%) and kappa of 0.54, which is a significant (P < 0.001) improvement over a state-of-the-art feature-based method with an accuracy of 56.8% (95% CI: 51.4 to 61.7%) and kappa of 0.39. Performance of the proposed system was unaffected when the number of channels in testing was reduced from 8 to 2|accuracy for large validation dataset remained at 69.5% (95% CI: 65.5 to 74.0%).

SIGNIFICANCE

The proposed system outperforms the state-of-the-art machine learning algorithms for EEG grade classification on a large multi-centre unseen dataset, indicating the potential to assist clinical decision making for neonates with HIE.

Remote Health Monitoring Systems Based on Bluetooth Low Energy (BLE) Communication Systems

Nowadays, remote healthcare monitoring systems (RHMS) are attracting patients, doctors and caregivers. RHMS reduces the number of unessential hospitalizations by providing the required healthcare services for patients at home. Furthermore, continuous health monitoring using RHMS is a hopeful solution for elderly people suffering from chronic diseases. RHMS is in general three tiers architecture where the first tier uses intelligent wearable sensors to gather physiological signs. The majority of wearable sensors constructors commercialized sensing devices with Bluetooth Low Energy (BLE) communication interfaces, which lead to the development of diverse RHMS deploying BLE communication interfaces for physiological patient data gathering. In this paper, we introduce the basic concepts related to RHMS design and development. Besides that, we focus our investigation on the BLE communication protocol used in the healthcare context and its configuration to sense several physiological data. Also, we highlight the different steps enabling reading sensed data on mobile application.

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.

Video and audio processing in paediatrics: a review

OBJECTIVE

Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU).

APPROACH

For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts-full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization.

MAIN RESULTS

The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed.

SIGNIFICANCE

Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.