Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Drug Use - Attitudinal Dimensions within the Student Population

AbstractDrug use has become a serious social problem that crosses continents and most countries. Surprisingly, studies on the prevalence and genesis of drug use in the academic environment are extremely rare.The study in question represents a sequence within a broader research devoted to drug use and analysis of predictive factors of consumer behavior in the student environment. Are identified four categories of predictive factors in the ensemble which attitudes occupy a distinct position. The results highlight the importance of attitudes for understanding motivation for drug use and for the construction of prevention programs

A convolutional neural network approach to detect congestive heart failure

Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection

Deep learning for electrocardiogram analysis applied to health monitoring applications

Several recent advances fuelled the significant increase in interest for Artificial Intelligence-based healthcare innovations: the vast availability of biomedical data, accurate wearable sensors, electronic health records, the advancement in Machine Learning methods and affordable computational resources. This thesis focuses on electrocardiogram signal analysis using a range of deep learning techniques, including Convolutional Neural Networks, Recurrent Neural Networks and Convolutional Autoencoders for developing several health monitoring applications. The performance of the proposed models was investigated in two applications (i.e. nocturnal low glucose detection and congestive heart failure diagnosis), that share the same aspects of the input data including noise, time-dependence, heterogeneity. This thesis explores the efficacy of a personalised deep learning-based system for the automatic detection of low glucose levels in real-life settings, overcoming the limitations of previous attempts. Furthermore, this thesis explores unsupervised methods for learning time series representations to address the high cost and scarcity of the labelled biomedical data. A novel deep learning-based method that employed raw electrocardiogram signals was explored for congestive heart failure diagnosis. Added to their superior performance, the results presented in this thesis bring forward the intuition that short electrocardiogram recordings, of just about 5 minutes, can be sufficient to diagnose correctly severe congestive heart failure. A third case study presented in this thesis advances the idea that certain heart rate variability features can be estimated from a short (≈ 1 minute) raw electrocardiogram signal, which might facilitate the development of real-time applications. This thesis shows that leveraging deep learning-based models for physiological signal analysis, not only bypasses the costly feature extraction and selection process, but they can reveal unintuitive patterns in the input data that are class-discriminative. Providing transparency on how the models reached certain conclusions is crucial for the healthcare field, firstly because the medical professionals expect specific evidence for their recommendations and secondly, the models can help doctors better understand the physiological processes. Overall, the findings of this thesis confirmed that deep learning methods applied for electrocardiogram analysis could improve and extend current diagnostic models, might bring new research opportunities and contribute to the development of real-time health monitoring applications

Are pre-hospital deaths from accidental injury preventable?

Heart rate variability (HRV) analysis has increasingly become a promising marker for the assessment of the autonomic nervous system. The easy derivation of the HRV has determined its popularity, being successfully used in many research and clinical studies. However, the conventional HRV analysis is performed on 5 minutes ECG recordings which in e-health monitoring might be unsuitable, due to real-time requirements. Thus, the aim of this study is to evaluate the association between the raw ECG heartbeats and the HRV features to further reduce the number of heart beats required for the HRV estimation enabling real time monitoring. We propose a deep learning based system, specifically a recurrent neural network for the inference of two time domain HRV features: AVNN (the average of all the NN intervals) and IHR (instantaneous heart rate). The obtained results suggest that both AVNN and IHR can be accurately inferred from a shorter ECG interval of about 1 minute, with a mean error of < 5% of the computed HRV features

CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation

International audienc

Skin Dialogues in Atopic Dermatitis

Atopic dermatitis (AD) is a chronic skin disorder associated with significant quality-of-life impairment and increased risk for allergic and non-allergic comorbidities. The aim of this review is to elucidate the connection between AD and most common comorbidities, as this requires a holistic and multidisciplinary approach. Advances in understanding these associations could lead to the development of highly effective and targeted treatments

Plasma Lipocalin Concentrations in Relation to Visceral Fat, Risk Factor for Endometrial Cancer

Objective The study aims to evaluate the presence of a correlation between visceral fat assessed by ultrasonography and the plasmatic level of lipocalin in patients diagnosed with endometrial cancer. Material and Method The study is a case-control analysis including 2 groups of patients: group I – 44 patients diagnosed with endometrial cancer, group II – 44 patients without gynecological pathology or inflammatory disorders. After the clinical examination and anthropometric measurements, these patients underwent ultrasonography (US) examination, in view of determining the visceral fat. At the patients included in this study, we also determined the plasmatic levels for lipocalin. Results At the patients diagnosed with endometrial cancer, the intraabdominal fat area evaluated by US and the plasmatic level of lipocalin is significantly larger (p<0.0001) compared to the control group. A correlation was also found between the intraperitoneal fat area evaluated by US and the plasmatic level of lipocalin. Conclusions The measurement of the intraperitoneal fat by US in correlation with the plasmatic level of lipocalin can be a screening method for endometrial cancer in obese patients