Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcare expenditures, with anticipated escalation in the future. It is essential to classify HF patients into three groups based on their ejection fraction: reduced (HFrEF), mid-range (HFmEF), and preserved (HFpEF), such as for diagnosis, risk assessment, treatment choice, and the ongoing monitoring of heart failure. Nevertheless, obtaining a definitive prediction poses challenges, requiring the reliance on echocardiography. On the contrary, an electrocardiogram (ECG) provides a straightforward, quick, continuous assessment of the patient's cardiac rhythm, serving as a cost-effective adjunct to echocardiography. In this research, we evaluate several machine learning (ML)-based classification models, such as K-nearest neighbors (KNN), neural networks (NN), support vector machines (SVM), and decision trees (TREE), to classify left ventricular ejection fraction (LVEF) for three categories of HF patients at hourly intervals, using 24-hour ECG recordings. Information from heterogeneous group of 303 heart failure patients, encompassing HFpEF, HFmEF, or HFrEF classes, was acquired from a multicenter dataset involving both American and Greek populations. Features extracted from ECG data were employed to train the aforementioned ML classification models, with the training occurring in one-hour intervals. To optimize the classification of LVEF levels in coronary artery disease (CAD) patients, a nested cross-validation approach was employed for hyperparameter tuning. HF patients were best classified using TREE and KNN models, with an overall accuracy of 91.2% and 90.9%, and average area under the curve of the receiver operating characteristics (AUROC) of 0.98, and 0.99, respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm were the ones that contributed to the highest classification accuracy. The results pave the way for creating an automated screening system tailored for patients with CAD, utilizing optimal measurement timings aligned with their circadian cycles.

Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images.

BACKGROUND AND OBJECTIVE: Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information. METHODS: In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information. RESULTS: Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m2 [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage. CONCLUSIONS: The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare.

Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.

One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary markedly in their ability to detect and interpret pathological patterns. Moreover, echo is still causing cost difficulties for low- and middle-income countries. Here, we developed a deep learning-based attention transformer model to automate the detection of heart murmurs caused by CHD at an early stage of life using cost-effective and widely available phonocardiography (PCG). PCG recordings were obtained from 942 young patients at four major auscultation locations, including the aortic valve (AV), mitral valve (MV), pulmonary valve (PV), and tricuspid valve (TV), and they were annotated by experts as absent, present, or unknown murmurs. A transformation to wavelet features was performed to reduce the dimensionality before the deep learning stage for inferring the medical condition. The performance was validated through 10-fold cross-validation and yielded an average accuracy and sensitivity of 90.23 % and 72.41 %, respectively. The accuracy of discriminating between murmurs' absence and presence reached 76.10 % when evaluated on unseen data. The model had accuracies of 70 %, 88 %, and 86 % in predicting murmur presence in infants, children, and adolescents, respectively. The interpretation of the model revealed proper discrimination between the learned attributes, and AV channel was found important (score 0.75) for the murmur absence predictions while MV and TV were more important for murmur presence predictions. The findings potentiate deep learning as a powerful front-line tool for inferring CHD status in PCG recordings leveraging early detection of heart anomalies in young people. It is suggested as a tool that can be used independently from high-cost machinery or expert assessment.

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features.

Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient's cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.

Depressive Disorder Remote Detection through Touchscreen Typing Behaviour.

Depressive Disorder (DD) is a leading cause of disability worldwide. Passive tools for screening the symptoms of DD are essential in monitoring and limiting the spread of the disease. From an alternative perspective, individuals' kinetic expression and activities, including smartphone interaction, reflect their mental status. Such widely available data in everyday life form a promising source of information on keystroke dynamics and their characteristics. This work explores how keystroke dynamics derived from touchscreen typing patterns have revealed the diagnosis of mental disorders, particularly depressive disorders. Different deep learning approaches were established to detect patients' depressive tendencies denoted by the self-reported Patient Health Questionnaire-9 (PHQ-9) score based on keystroke digital biomarkers. In particular, Convolutional Neural Networks (CNN), Long-Short-Term-Memory (LSTM), and CNN-LSTM models were examined and compared. The keystroke sequences are captured unobtrusively during routine interaction with touchscreen smartphones in a non-clinical setting. This study used 23,264 typing sessions provided by 10 DD patients and 14 healthy controls (HC). The proposed approach was investigated under two keystroke feature combinations and validated utilizing a leave-one-subject-out (LOSO) cross-validation scheme. The best-performing LSTM-with-hold-time (LSTM-HT) model achieved an Area Under Curve (AUC) of 0.86 with the correlated probabilities for subjects' status [95% confidence interval (CI):0.66-1.00, sensitivity/specificity (SE/SP) of 0.8/0.93].Clinical relevance- The findings of this research have the potential to contribute to improving digital tools for objectively screening mental disorders in the wild. Moreover, they would potentially provide the users and their attending psychiatrists with information regarding the evolution of their mental health.

Dynamic Monitoring of Probiotics Effect in Parkinson's Disease Patients via Swarm Decomposition and Bispectral Analysis of Electrogastrograms.

People with Parkinson's Disease (PwP) experience a significant deterioration of their daily life quality due to non-motor symptoms, with gastrointestinal dysfunctions manifesting as a vanguard of the latter. Electrogastrography (EGG) is a noninvasive diagnostic tool that can potentially provide biomarkers for the monitoring of dynamic gastric alterations that are related to daily lifestyle and treatment regimens. In this work, a robust analysis of EGG dynamics is introduced to evaluate the effect of probiotic treatment on PwP. The proposed framework, namely biSEGG, introduces a Swarm Decomposition-based enhancement of the EGG, combined with Bispectral feature engineering to model the underlying Quadratic Phase Coupling interactions between the gastric activity oscillatory components of EGG. The biSEGG features are benchmarked against the conventional Power Spectrum-based ones and evaluated through machine learning classifiers. The experimental results, when biSEGG was applied on data epochs from 11 PwP (probiotic vs placebo, AUROC: 0.67, Sensitivity/Specificity: 75/58%), indicate the superiority of biSEGG over Power Spectrum-based approaches and justify the efficiency of biSEGG in capturing and explaining intervention- and meal consumption-related alterations of the gastric activity in PwP.Clinical relevance- biSEGG holds potential for dynamic monitoring of gastrointestinal dysfunction and health status of PwP across diverse daily life scenarios.

Classification of children with ADHD through task-related EEG recordings via Swarm-Decomposition-based Phase Locking Value.

Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder mainly affecting children. ADHD children brain activity is reported to present alterations from neurotypically developed children, yet establishment of an EEG biomarker, which is of high importance in clinical practice and research, has not been achieved. In this work, task-related EEG recordings from 61 ADHD and 60 age-matched non-ADHD children are analyzed to examine the underlying Cross-Frequency Coupling phenomena. The proposed framework introduces personalized brain rhythm extraction in the form of oscillatory modes via Swarm Decomposition, allowing for the transition from sensor-level connectivity to source-level connectivity. Oscillatory modes are then subjected to a phase locking value-based feature extraction and the efficiency of the extracted features in separating ADHD from non-ADHD individuals is evaluated by means of a nested 5-fold cross validation scheme. The experimental results of the proposed framework (Area Under the Receiver Operating Characteristics Curve-AUROC: 0.9166) when benchmarked against the commonly used filter-based brain rhythm extraction (AUROC: 0.8361) underscore its efficiency and demonstrate its overall superiority over other state-of-the-art functional connectivity approaches in this classification task for this dataset.Clinical relevance-This framework provides novel insights about brain regions of interest that are involved in ADHD task-related function and holds promise in providing objective ADHD biomarkers by extending classic sensor-level connectivity to source-level.

Emotional Climate Recognition in Conversations using Peers' Speech-based Bispectral Features and Affect Dynamics.

Emotion recognition in conversations using artificial intelligence (AI) has recently gained a lot of attention, as it can provide additional emotion cues that can be correlated with human social behavior. An extension towards an AI-based emotional climate (EC) recognition, i.e., the recognition of the joint emotional atmosphere dynamically created and perceived by the peers throughout a conversation, is proposed here. In our approach, namely MLBispeC (Machine Learning Based Bispectral Classification), the peers' speech signals during their conversation are subjected to time-windowed bispectral analysis, allowing for feature extraction related to dynamic harmonics nonlinear interactions. In addition, peers' affect dynamics, derived from their same time-windowed emotion labeling, are combined to form an extended feature vector, inputted into two well-known machine learning classifiers (Support Vector Machine, K-Nearest Neighbor). MLBispeC was evaluated on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) open access dataset, which contains 2D emotions, i.e., Arousal (A) and valence (V) that are divided into (low/high) classes. The experimental results have shown that MLBispeC outperforms previous state-of-the-art techniques, achieving an accuracy of 0.826A/0.754V, sensitivity of 0.864A/0.774V, and area under the curve (AUC) of 0.821A/0.799V. This demonstrates the effectiveness of MLBispeC to objectively recognize peers' EC during their conversation, allowing for insights into their emotional and social interactions.Clinical relevance-Unobtrusive, objective and dynamic recognition of the EC built during peers' conversation can scaffold effective assessment of patients with physiological, psychological, and mental diseases, at various age ranges (children, adults, and older adults).

Parkinsonian Tremor Detection with Compact Convolutional Transformer from Bispectrum Representation of tri-Axial Accelerometer Signals.

After the breakthroughs of Transformer networks in Natural Language Processing (NLP) tasks, they have led to exciting progress in visual tasks as well. Nonetheless, there has been a parallel growth in the number of parameters and the amount of training data, which led to the conclusion that Transformers are not suited for small datasets. This paper is the first to convey the feasibility of Compact Convolutional Transformers (CCT) for the prediction of Parkinsonian postural tremor based on the Bispectrum (BS) representation of IMU accelerometer time series. The dataset includes tri-axial accelerometer signals collected unobtrusively in-the-wild while subjects are on a phone call, and labelled by neurologists and signal processing experts. The BS is a noise-immune, higher-order representation that reflects a signal's deviation from Gaussianity and measures quadratic phase coupling. We performed comparative classification experiments using the CCT, pre-trained CNNs such as VGG-16 and ResNet-50, and the conventional Vision Transformer (ViT). Our model achieves competitive prediction accuracy and F1 score of 96% with only 1.016 M trainable parameters, compared to the ViT with 21.659 M trainable parameters, in a five-fold cross-validation scheme. Our model also outperforms pre-trained CNNs such as VGG-16 and ResNet-50. Furthermore, we show that the performance gains are maintained when training on a larger dataset of BS images. Our effort here is motivated by the hypothesis that data-efficient transformers outperform transfer learning using pre-trained CNNs, paving the way for promising deep learning architecture for small-scale, novel and noisy medical imaging datasets.Clinical relevance- Novel deep learning model for unobtrusive prediction of Parkinsonian Postural Tremor from Bispectrum image representation of tri-axial accelerometer signals collected in-the-wild.

Effect of Comorbidities Features in Machine Learning Models for Survival Analysis to Predict Prodromal Alzheimer's Disease.

Alzheimer's Disease (AD) is the most common form of dementia, specifically a progressive degenerative disorder affecting 47 million people worldwide and is only expected to grow in the elderly population. The detection of AD in its early stages is crucial to allow early intervention aiding in the prevention or slowing down of the disease. The effect of using comorbidity features in machine learning models to predict the time until a patient develops a prodrome was observed. In this study, we used Alzheimer's Disease Neuroimaging Initiative (ADNI) high-dimensional clinical data to compare the performance of six machine learning algorithms for survival analysis, combined with six feature selection methods trained on two settings: with and without comorbidities features. Our ridge model combined with permutation feature selection achieves maximum performance of 0.90 when using comorbidity features with the concordance index as a performance indicator. This demonstrated that incorporating comorbidities into the feature set enhances the performance of survival analysis for Alzheimer's disease. There is potential to identify risk factors (coronary artery disease) from comorbidities which could guide preventative care based on medical history.

Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning.

Heart failure refers to the inability of the heart to pump enough amount of blood to the body. Nearly 7 million people die every year because of its complications. Current gold-standard screening techniques through echocardiography do not incorporate information about the circadian rhythm of the heart and clinical information of patients. In this vein, we propose a novel approach to integrate 24-hour heart rate variability (HRV) features and patient profile information in a single multi-parameter and color-coded polar representation. The proposed approach was validated by training a deep learning model from 7,575 generated images to predict heart failure groups, i.e., preserved, mid-range, and reduced left ventricular ejection fraction. The developed model had overall accuracy, sensitivity, and specificity of 93%, 88%, and 95%, respectively. Moreover, it had a high area under the receiver operating characteristics curve (AUROC) of 0.88 and an area under the precision-recalled curve (AUPR) of 0.79. The novel approach proposed in this study suggests a new protocol for assessing cardiovascular diseases to act as a complementary tool to echocardiography as it provides insights on the circadian rhythm of the heart and can be potentially personalized according to patient clinical profile information.Clinical relevance- Implementing polar representations with deep learning in clinical settings to supplement echocardiography leverages continuous monitoring of the heart's circadian rhythm and personalized cardiovascular medicine while reducing the burden on medical practitioners.

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage.

Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1-1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A 'microangiopathy score' compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.

Optoacoustic biomarkers of lipids, hemorrhage and inflammation in carotid atherosclerosis.

Imaging plays a critical role in exploring the pathophysiology and enabling the diagnostics and therapy assessment in carotid artery disease. Ultrasonography, computed tomography, magnetic resonance imaging and nuclear medicine techniques have been used to extract of known characteristics of plaque vulnerability, such as inflammation, intraplaque hemorrhage and high lipid content. Despite the plethora of available techniques, there is still a need for new modalities to better characterize the plaque and provide novel biomarkers that might help to detect the vulnerable plaque early enough and before a stroke occurs. Optoacoustics, by providing a multiscale characterization of the morphology and pathophysiology of the plaque could offer such an option. By visualizing endogenous (e.g., hemoglobin, lipids) and exogenous (e.g., injected dyes) chromophores, optoacoustic technologies have shown great capability in imaging lipids, hemoglobin and inflammation in different applications and settings. Herein, we provide an overview of the main optoacoustic systems and scales of detail that enable imaging of carotid plaques in vitro, in small animals and humans. Finally, we discuss the limitations of this novel set of techniques while investigating their potential to enable a deeper understanding of carotid plaque pathophysiology and possibly improve the diagnostics in future patients with carotid artery disease.

Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review.

The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches' efficacy.

Clinical and Translational Imaging and Sensing of Diabetic Microangiopathy: A Narrative Review.

Microvascular changes in diabetes affect the function of several critical organs, such as the kidneys, heart, brain, eye, and skin, among others. The possibility of detecting such changes early enough in order to take appropriate actions renders the development of appropriate tools and techniques an imperative need. To this end, several sensing and imaging techniques have been developed or employed in the assessment of microangiopathy in patients with diabetes. Herein, we present such techniques; we provide insights into their principles of operation while discussing the characteristics that make them appropriate for such use. Finally, apart from already established techniques, we present novel ones with great translational potential, such as optoacoustic technologies, which are expected to enter clinical practice in the foreseeable future.

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels.

With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals' smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare.

INTRODUCTION: Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. AREAS COVERED: In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. EXPERT OPINION: The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

Gut microbiome dysbiosis in Alzheimer's disease and mild cognitive impairment: A systematic review and meta-analysis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that causes gradual memory loss. AD and its prodromal stage of mild cognitive impairment (MCI) are marked by significant gut microbiome perturbations, also known as gut dysbiosis. However, the direction and extent of gut dysbiosis have not been elucidated. Therefore, we performed a meta-analysis and systematic review of 16S gut microbiome studies to gain insights into gut dysbiosis in AD and MCI. METHODS: We searched MEDLINE, Scopus, EMBASE, EBSCO, and Cochrane for AD gut microbiome studies published between Jan 1, 2010 and Mar 31, 2022. This study has two outcomes: primary and secondary. The primary outcomes explored the changes in α-diversity and relative abundance of microbial taxa, which were analyzed using a variance-weighted random-effects model. The secondary outcomes focused on qualitatively summarized ß-diversity ordination and linear discriminant analysis effect sizes. The risk of bias was assessed using a methodology appropriate for the included case-control studies. The geographic cohorts' heterogeneity was examined using subgroup meta-analyses if sufficient studies reported the outcome. The study protocol has been registered with PROSPERO (CRD42022328141). FINDINGS: Seventeen studies with 679 AD and MCI patients and 632 controls were identified and analyzed. The cohort is 61.9% female with a mean age of 71.3±6.9 years. The meta-analysis shows an overall decrease in species richness in the AD gut microbiome. However, the phylum Bacteroides is consistently higher in US cohorts (standardised mean difference [SMD] 0.75, 95% confidence interval [CI] 0.37 to 1.13, p < 0.01) and lower in Chinese cohorts (SMD -0.79, 95% CI -1.32 to -0.25, p < 0.01). Moreover, the Phascolarctobacterium genus is shown to increase significantly, but only during the MCI stage. DISCUSSION: Notwithstanding possible confounding from polypharmacy, our findings show the relevance of diet and lifestyle in AD pathophysiology. Our study presents evidence for region-specific changes in abundance of Bacteroides, a major constituent of the microbiome. Moreover, the increase in Phascolarctobacterium and the decrease in Bacteroides in MCI subjects shows that gut microbiome dysbiosis is initiated in the prodromal stage. Therefore, studies of the gut microbiome can facilitate early diagnosis and intervention in Alzheimer's disease and perhaps other neurodegenerative disorders.

One-Dimensional W-NETR for Non-Invasive Single Channel Fetal ECG Extraction.

Fetal cardiac monitoring is very helpful in the early detection of the potential risk of fetal cardiac abnormalities, which enables prompt preventative care and ensures safe births. As a result, it is crucial to regularly check on the embryonic heart. Methods of non-invasively fetal ECG extraction from maternal abdominal ECG signal are thoroughly discussed. Although fetal signals are generally obscured by maternal ECG signals and noise, extracting a clean fetal ECG is a significant difficulty. The majority of techniques for fetal ECG extraction include many extraction steps. We describe a unique method for splitting a single-channel maternal abdominal ECG into maternal and fetus ECG employing two parallel U-nets with transformer encoding, which we refer to as W-NEt TRansformers (W-NETR). Due to its enhanced capacity to simulate remote interactions and capture global context, the suggested pipeline utilizes the self-attention mechanism of the transformer. We tested the proposed pipeline on synthetic and real datasets and outperformed the current state-of-the-art deep learning models. The proposed model achieved the best results on both datasets for QRS detection precision, recall, and F1 scores. More specifically, it achieved F1 score of 99.88% and 98.9% on the real ADFECGDB and PCDB datasets, respectively. These encouraging results highlight the suggested W-NETR's effectiveness in precisely extracting the fetal ECG, which was achieved with high SSIM and PSNR values in the results. This provides the bed set for long-term maternal and fetal monitoring via portable devices as the proposed system performs real-time execution.