Transfer Learning-based Seizure Detection on Multiple Channels of Paediatric EEGs.

Epilepsy is a common neurological disease characterised by recurring seizures that affect up to 70 million people worldwide. During the first ten years of life, approximately one in every 150 children is diagnosed with epilepsy. EEG is an important tool for diagnosing seizures and other brain disorders. However, expert visual analysis of EEGs is time-consuming. In addition to reducing expert annotation time, the automatic seizure detection method is a powerful tool for assisting experts with the analysis of EEGs. Research on the automated detection of seizures in pediatric EEG has been limited. Deep learning algorithms are typically used in paediatric seizure detection methods; however, they are computationally expensive and take a long time to develop. This problem can be solved using transfer learning. In this study, we developed a transfer learning-based seizure detection method on multiple channels of paediatric EEGs. The publicly available CHB-MIT EEG dataset was used to build our method. The dataset was split into training (n=14), validation (n=4), and testing (n=6). Spectrograms generated from 10 s EEG signals with 5 s overlap were used as the input into three pre-trained transfer learning models (ResNet50, VGG16 and InceptionV3). We took care to separate the children into either the training or test set to ensure that the test set was independent. Based on the EEG test set, the method has 85.41% accuracy, 85.94% recall, and 85.49% precision. This method has the potential to assist researchers and clinicians in the automated analysis of seizures in paediatric EEGs.

A Machine Learning Approach for Sex and Age Classification of Paediatric EEGs.

Electroencephalography (EEG) is an important investigation of childhood seizures and other brain disorders. Expert visual analysis of EEGs can estimate subjects' age based on the presence of particular maturational features. The sex of a child, however, cannot be determined by visual inspection. In this study, we explored sex and age differences in the EEGs of 351 healthy male and female children aged between 6 and 10 years. We developed machine learning-based methods to classify the sex and age of healthy children from their EEGs. This preliminary study based on small EEG numbers demonstrates the potential for machine learning in helping with age determination in healthy children. This may be useful in distinguishing developmentally normal from developmentally delayed children. The model performed poorly for estimation of biological sex. However, we achieved 66.67% accuracy in age prediction allowing a 1 year error, on the test set.

Brain cell-specific origin of circulating microRNA biomarkers in experimental temporal lobe epilepsy.

The diagnosis of epilepsy is complex and challenging and would benefit from the availability of molecular biomarkers, ideally measurable in a biofluid such as blood. Experimental and human epilepsy are associated with altered brain and blood levels of various microRNAs (miRNAs). Evidence is lacking, however, as to whether any of the circulating pool of miRNAs originates from the brain. To explore the link between circulating miRNAs and the pathophysiology of epilepsy, we first sequenced argonaute 2 (Ago2)-bound miRNAs in plasma samples collected from mice subject to status epilepticus induced by intraamygdala microinjection of kainic acid. This identified time-dependent changes in plasma levels of miRNAs with known neuronal and microglial-cell origins. To explore whether the circulating miRNAs had originated from the brain, we generated mice expressing FLAG-Ago2 in neurons or microglia using tamoxifen-inducible Thy1 or Cx3cr1 promoters, respectively. FLAG immunoprecipitates from the plasma of these mice after seizures contained miRNAs, including let-7i-5p and miR-19b-3p. Taken together, these studies confirm that a portion of the circulating pool of miRNAs in experimental epilepsy originates from the brain, increasing support for miRNAs as mechanistic biomarkers of epilepsy.

Helping children and adolescents who lost a caregiver during the COVID-19 pandemic.

ABSTRACT: Between March 2020 and June of 2021, 140,000 children under 18 in the US lost a caregiver. Due to this sudden loss, their lives have been drastically impacted. This article presents interventions for this population's unique and stressful emotional trauma.

Machine learning-based clinical decision support systems for pregnancy care: A systematic review.

BACKGROUND: Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care. OBJECTIVE: This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers. METHODS: We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis. RESULTS: 17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing. CONCLUSION: Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use.

Towards an explainable clinical decision support system for large-for-gestational-age births.

A myriad of maternal and neonatal complications can result from delivery of a large-for-gestational-age (LGA) infant. LGA birth rates have increased in many countries since the late 20th century, partially due to a rise in maternal body mass index, which is associated with LGA risk. The objective of the current study was to develop LGA prediction models for women with overweight and obesity for the purpose of clinical decision support in a clinical setting. Maternal characteristics, serum biomarkers and fetal anatomy scan measurements for 465 pregnant women with overweight and obesity before and at approximately 21 weeks gestation were obtained from the PEARS (Pregnancy Exercise and Nutrition with smart phone application support) study data. Random forest, support vector machine, adaptive boosting and extreme gradient boosting algorithms were applied with synthetic minority over-sampling technique to develop probabilistic prediction models. Two models were developed for use in different settings: a clinical setting for white women (AUC-ROC of 0.75); and a clinical setting for women of all ethnicity and regions (AUC-ROC of 0.57). Maternal age, mid upper arm circumference, white cell count at the first antenatal visit, fetal biometry and gestational age at fetal anatomy scan were found to be important predictors of LGA. Pobal HP deprivation index and fetal biometry centiles, which are population-specific, are also important. Moreover, we explained our models with Local Interpretable Model-agnostic Explanations (LIME) to improve explainability, which was proven effective by case studies. Our explainable models can effectively predict the probability of an LGA birth for women with overweight and obesity, and are anticipated to be useful to support clinical decision-making and for the development of early pregnancy intervention strategies to reduce pregnancy complications related to LGA.

Addressing nurses' prolonged grief due to the COVID-19 pandemic.

ABSTRACT: The COVID-19 pandemic resulted in physical and emotional tolls on healthcare workers and caregivers, which have caused prolonged grief disorder and persistent complex bereavement disorder. Highlighting key learnings from healthcare workers' experiences during the pandemic, this article outlines self-care strategies to help nurses better prepare for future healthcare emergencies.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.

Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles.

BACKGROUND: Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis. METHOD: We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set. RESULT: Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions. CONCLUSION: The Deep-spindle system can reduce physicians' workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

A Clinical Decision Support System for the Prediction of Quality of Life in ALS.

Amyotrophic Lateral Sclerosis (ALS), also known as Motor Neuron Disease (MND), is a rare and fatal neurodegenerative disease. As ALS is currently incurable, the aim of the treatment is mainly to alleviate symptoms and improve quality of life (QoL). We designed a prototype Clinical Decision Support System (CDSS) to alert clinicians when a person with ALS is experiencing low QoL in order to inform and personalise the support they receive. Explainability is important for the success of a CDSS and its acceptance by healthcare professionals. The aim of this work isto announce our prototype (C-ALS), supported by a first short evaluation of its explainability. Given the lack of similar studies and systems, this work is a valid proof-of-concept that will lead to future work. We developed a CDSS that was evaluated by members of the team of healthcare professionals that provide care to people with ALS in the ALS/MND Multidisciplinary Clinic in Dublin, Ireland. We conducted a user study where participants were asked to review the CDSS and complete a short survey with a focus on explainability. Healthcare professionals demonstrated some uncertainty in understanding the system's output. Based on their feedback, we altered the explanation provided in the updated version of our CDSS. C-ALS provides local explanations of its predictions in a post-hoc manner, using SHAP (SHapley Additive exPlanations). The CDSS predicts the risk of low QoL in the form of a probability, a bar plot shows the feature importance for the specific prediction, along with some verbal guidelines on how to interpret the results. Additionally, we provide the option of a global explanation of the system's function in the form of a bar plot showing the average importance of each feature. C-ALS is available online for academic use.

An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus.

Gestational Diabetes Mellitus (GDM), a common pregnancy complication associated with many maternal and neonatal consequences, is increased in mothers with overweight and obesity. Interventions initiated early in pregnancy can reduce the rate of GDM in these women, however, untargeted interventions can be costly and time-consuming. We have developed an explainable machine learning-based clinical decision support system (CDSS) to identify at-risk women in need of targeted pregnancy intervention. Maternal characteristics and blood biomarkers at baseline from the PEARS study were used. After appropriate data preparation, synthetic minority oversampling technique and feature selection, five machine learning algorithms were applied with five-fold cross-validated grid search optimising the balanced accuracy. Our models were explained with Shapley additive explanations to increase the trustworthiness and acceptability of the system. We developed multiple models for different use cases: theoretical (AUC-PR 0.485, AUC-ROC 0.792), GDM screening during a normal antenatal visit (AUC-PR 0.208, AUC-ROC 0.659), and remote GDM risk assessment (AUC-PR 0.199, AUC-ROC 0.656). Our models have been implemented as a web server that is publicly available for academic use. Our explainable CDSS demonstrates the potential to assist clinicians in screening at risk patients who may benefit from early pregnancy GDM prevention strategies.

Spindle-AI: Sleep Spindle Number and Duration Estimation in Infant EEG.

OBJECTIVE: Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date. METHODS: We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist. RESULTS: The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%. CONCLUSION AND SIGNIFICANCE: Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy.

Objective.Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals.Approach.In this study, a machine learning-based seizure detection approach, 'Epi-AI', which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach.Main results.Epi-AI demonstrated a superior sensitivity, 91.4%-98.8%, and specificity, 93.1%-98.8%, in Models I-III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I-III ranged from 66.9%-91.3% sensitivity and 60.8%-97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I-III at 73.2%-80.1% and 75.8%-98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity.Significance.Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.

Predictive modelling of hypoxic ischaemic encephalopathy risk following perinatal asphyxia.

Hypoxic Ischemic Encephalopathy (HIE) remains a major cause of neurological disability. Early intervention with therapeutic hypothermia improves outcome, but prediction of HIE is difficult and no single clinical marker is reliable. Machine learning algorithms may allow identification of patterns in clinical data to improve prognostic power. Here we examine the use of a Random Forest machine learning algorithm and five-fold cross-validation to predict the occurrence of HIE in a prospective cohort of infants with perinatal asphyxia. Infants with perinatal asphyxia were recruited at birth and neonatal course was followed for the development of HIE. Clinical variables were recorded for each infant including maternal demographics, delivery details and infant's condition at birth. We found that the strongest predictors of HIE were the infant's condition at birth (as expressed by Apgar score), need for resuscitation, and the first postnatal measures of pH, lactate, and base deficit. Random Forest models combining features including Apgar score, most intensive resuscitation, maternal age and infant birth weight both with and without biochemical markers of pH, lactate, and base deficit resulted in a sensitivity of 56-100% and a specificity of 78-99%. This study presents a dynamic method of rapid classification that has the potential to be easily adapted and implemented in a clinical setting, with and without the availability of blood gas analysis. Our results demonstrate that applying machine learning algorithms to readily available clinical data may support clinicians in the early and accurate identification of infants who will develop HIE. We anticipate our models to be a starting point for the development of a more sophisticated clinical decision support system to help identify which infants will benefit from early therapeutic hypothermia.

Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal caregivers due to the motor and cognitive decline caused by the disease. This study aims to identify caregivers whose quality of life (QoL) may be impacted as a result of caring for a person with ALS. In this study, we worked towards the identification of the predictors of a caregiver's QoL in addition to the development of a model for clinical use to alert clinicians when a caregiver is at risk of experiencing low QoL. The data were collected through the Irish ALS Registry and via interviews on several topics with 90 patient and caregiver pairs at three time-points. The McGill QoL questionnaire was used to assess caregiver QoL-the MQoL Single Item Score measures the overall QoL and was selected as the outcome of interest in this work. The caregiver's existential QoL and burden, as well as the patient's depression and employment before the onset of symptoms were the features that had the highest impact in predicting caregiver quality of life. A small subset of features that could be easy to collect was used to develop a second model to use it in a clinical setting. The most predictive features for that model were the weekly caregiving duties, age and health of the caregiver, as well as the patient's physical functioning and age of onset.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer.

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

SCLpred-MEM: Subcellular localization prediction of membrane proteins by deep N-to-1 convolutional neural networks.

The knowledge of the subcellular location of a protein is a valuable source of information in genomics, drug design, and various other theoretical and analytical perspectives of bioinformatics. Due to the expensive and time-consuming nature of experimental methods of protein subcellular location determination, various computational methods have been developed for subcellular localization prediction. We introduce "SCLpred-MEM," an ab initio protein subcellular localization predictor, powered by an ensemble of Deep N-to-1 Convolutional Neural Networks (N1-NN) trained and tested on strict redundancy reduced datasets. SCLpred-MEM is available as a web-server predicting query proteins into two classes, membrane and non-membrane proteins. SCLpred-MEM achieves a Matthews correlation coefficient of 0.52 on a strictly homology-reduced independent test set and 0.62 on a less strict homology reduced independent test set, surpassing or matching other state-of-the-art subcellular localization predictors.

Systemic delivery of antagomirs during blood-brain barrier disruption is disease-modifying in experimental epilepsy.

Oligonucleotide therapies offer precision treatments for a variety of neurological diseases, including epilepsy, but their deployment is hampered by the blood-brain barrier (BBB). Previous studies showed that intracerebroventricular injection of an antisense oligonucleotide (antagomir) targeting microRNA-134 (Ant-134) reduced evoked and spontaneous seizures in animal models of epilepsy. In this study, we used assays of serum protein and tracer extravasation to determine that BBB disruption occurring after status epilepticus in mice was sufficient to permit passage of systemically injected Ant-134 into the brain parenchyma. Intraperitoneal and intravenous injection of Ant-134 reached the hippocampus and blocked seizure-induced upregulation of miR-134. A single intraperitoneal injection of Ant-134 at 2 h after status epilepticus in mice resulted in potent suppression of spontaneous recurrent seizures, reaching a 99.5% reduction during recordings at 3 months. The duration of spontaneous seizures, when they occurred, was also reduced in Ant-134-treated mice. In vivo knockdown of LIM kinase-1 (Limk-1) increased seizure frequency in Ant-134-treated mice, implicating de-repression of Limk-1 in the antagomir mechanism. These studies indicate that systemic delivery of Ant-134 reaches the brain and produces long-lasting seizure-suppressive effects after systemic injection in mice when timed with BBB disruption and may be a clinically viable approach for this and other disease-modifying microRNA therapies.

Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG.

Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.

Obesity in children and adolescents: An overview.

Childhood obesity is an epidemic in the US. This article discusses the evolution, prevention, and associated physical and psychosocial consequences of and interventions for obesity in the pediatric population.