A self-attention deep neural network regressor for real time blood glucose estimation in paediatric population using physiological signals

With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management. In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman’s correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population

Spectrogram-driven convolutional neural network for real-time non-invasive hyperglycaemia detection in paediatric Type-1 diabetes via wearable sensors

Real-time detection of glycaemic events is crucial in the effective management of type 1 diabetes, particularly in paediatric patients. Recent advances in wearable sensors and machine learning have allowed for the inference of glycaemic events based on non-invasive physiological signals such as electrocardiogram (ECG). However, existing approaches have limitations due to the limited number of ECG features analysed and their applicability to real-life conditions. To overcome these limitations, we propose a spectrogram-driven deep learning methodology for real-time glycaemic event detection. We calculated beat-level spectrograms using Short Time Fourier Transform (STFT) on ECG beats extracted from continuous signals using our deep learning ECG segmentation tool. Subject-specific multi-layer 2D convolutional neural networks were trained on the spectrograms. We evaluated our methodology on an original dataset comprising continuous ECG and interstitial glucose data collected from children with type-1 diabetes over several days in real-life conditions. Our approach achieved an average personalised hyperglycaemia detection accuracy of 96.9%