Fernando Andreotti
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View article: Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients Open
Determining phenotypes of diseases can have considerable benefits for in-hospital patient care and to drug development. The structure of high dimensional data sets such as electronic health records are often represented through an embeddin…
View article: Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of\n Heart Failure Patients
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of\n Heart Failure Patients Open
Determining phenotypes of diseases can have considerable benefits for\nin-hospital patient care and to drug development. The structure of high\ndimensional data sets such as electronic health records are often represented\nthrough an embed…
View article: Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units
Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units Open
In this work, we propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records (EHRs) at different time horizons. The proposed approach is compared to a standard …
View article: Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers
Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers Open
Current management of bipolar disorder relies on self-reported questionnaires and interviews with clinicians. The development of objective measures of deteriorating mood may also allow for early interventions to take place to avoid transit…
View article: Early risk assessment for COVID-19 patients from emergency department data using machine learning
Early risk assessment for COVID-19 patients from emergency department data using machine learning Open
Background Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic, with more than 4.8 million reported cases and 310 000 deaths worldwide. While epidemiological and clinical…
View article: Screening for REM Sleep Behaviour Disorder with Minimal Sensors
Screening for REM Sleep Behaviour Disorder with Minimal Sensors Open
Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This study investigates a minimal set of sensors to achieve effective screening f…
View article: Detection of REM sleep behaviour disorder by automated polysomnography analysis
Detection of REM sleep behaviour disorder by automated polysomnography analysis Open
This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.
View article: SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging Open
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence cla…
View article: Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis Open
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD…
View article: Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data
Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data Open
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disea…
View article: Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification Open
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and,…
View article: Visualising Convolutional Neural Network Decisions in Automatic Sleep Scoring
Visualising Convolutional Neural Network Decisions in Automatic Sleep Scoring Open
Current sleep medicine relies on the supervised analysis of polysomnographic recordings, which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN…
View article: Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG
Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG Open
The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts.In order to improve patient management and reduce healthcare cos…
View article: Linking Changes in Heart Rate Variability to Mood Changes in Daily Life
Linking Changes in Heart Rate Variability to Mood Changes in Daily Life Open
Background: Dynamic changes of heart rate are considered as markers for autonomic nervous system (ANS) balance and potentially mood.Objective markers of mood are especially of interest when monitoring mental health conditions such as bipol…
View article: The use of non-invasive fetal electrocardiography in diagnosing second-degree fetal atrioventricular block
The use of non-invasive fetal electrocardiography in diagnosing second-degree fetal atrioventricular block Open
This study demonstrates, for the first time, the feasibility of the non-invasive fetal electrocardiogram as a supplementary method to diagnose of the fetal atrioventricular block. Combined with current fetal monitoring techniques, non-inva…
View article: fernandoandreotti/fecgsyn: Fetal signal quality indices added
fernandoandreotti/fecgsyn: Fetal signal quality indices added Open
Features Novel signal quality indices (SQI) added as presented in Andreotti et al 2017 Naive Bayes classifier summarizes different SQI metrics into a consensus metric. Minor restructuring of folders
View article: Monitoring fetal maturation—objectives, techniques and indices of autonomic function
Monitoring fetal maturation—objectives, techniques and indices of autonomic function Open
Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins o…
View article: A practical guide to non-invasive foetal electrocardiogram extraction and analysis
A practical guide to non-invasive foetal electrocardiogram extraction and analysis Open
Non-Invasive foetal electrocardiography (NI-FECG) represents an alternative foetal monitoring technique to traditional Doppler ultrasound approaches, that is non-invasive and has the potential to provide additional clinical information. Ho…
View article: An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms
An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms Open
Over the past decades, many studies have been published on the extraction of non-invasive foetal electrocardiogram (NI-FECG) from abdominal recordings. Most of these contributions claim to obtain excellent results in detecting foetal QRS (…
View article: Assessment Of Source Separation Techniques To Extract Vital Parameters From Videos
Assessment Of Source Separation Techniques To Extract Vital Parameters From Videos Open
Publication in the conference proceedings of EUSIPCO, Nice, France, 2015
View article: Assessment of source separation techniques to extract vital parameters from videos
Assessment of source separation techniques to extract vital parameters from videos Open
Camera-based photoplethysmography is a contactless mean to assess vital parameters, such as heart rate and respiratory rate. In the field of camera-based photoplethysmography, blind source separation (BSS) techniques have been extensively …