485 research outputs found
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
Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning
People with diabetes have to manage their blood glucose level to keep it
within an appropriate range. Predicting whether future glucose values will be
outside the healthy threshold is of vital importance in order to take
corrective actions to avoid potential health damage. In this paper we describe
our research with the aim of predicting the future behavior of blood glucose
levels, so that hypoglycemic events may be anticipated. The approach of this
work is the application of transformation functions on glucose time series, and
their use in convolutional neural networks. We have tested our proposed method
using real data from 4 different diabetes patients with promising results.Comment: 7 pages, 7 figures, 3 table
Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection
Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity. © 2011 Biomedical Engineering Society
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children
Research in diabetes, especially when it comes to building data-driven models
to forecast future glucose values, is hindered by the sensitive nature of the
data. Because researchers do not share the same data between studies, progress
is hard to assess. This paper aims at comparing the most promising algorithms
in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory
(LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support
Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and
trained on a population of 10 virtual children from the Type 1 Diabetes
Metabolic Simulator software to predict future glucose values at a prediction
horizon of 30 minutes. The performances of the models are evaluated using the
Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis
(CG-EGA). While most of the models end up having low RMSE, the GP model with a
Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction,
has the lowest. Despite having good RMSE values, we show that the models do not
necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only
the LSTM, SVR and GP-DP models have overall acceptable results, each of them
performing best in one of the glycemia regions
Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions
This paper presents the Derivatives Combination Predictor (DCP), a novel
model fusion algorithm for making long-term glucose predictions for diabetic
people. First, using the history of glucose predictions made by several models,
the future glucose variation at a given horizon is predicted. Then, by
accumulating the past predicted variations starting from a known glucose value,
the fused glucose prediction is computed. A new loss function is introduced to
make the DCP model learn to react faster to changes in glucose variations.
The algorithm has been tested on 10 \textit{in-silico} type-1 diabetic
children from the T1DMS software. Three initial predictors have been used: a
Gaussian process regressor, a feed-forward neural network and an extreme
learning machine model. The DCP and two other fusion algorithms have been
evaluated at a prediction horizon of 120 minutes with the root-mean-squared
error of the prediction, the root-mean-squared error of the predicted
variation, and the continuous glucose-error grid analysis.
By making a successful trade-off between prediction accuracy and
predicted-variation accuracy, the DCP, alongside with its specifically designed
loss function, improves the clinical acceptability of the predictions, and
therefore the safety of the model for diabetic people
Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus
Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag
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