Search Results for author: Fernando Andreotti

Found 8 papers, 6 papers with code

Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients

no code implementations24 Dec 2020 Oliver Carr, Stojan Jovanovic, Luca Albergante, Fernando Andreotti, Robert Dürichen, Nadia Lipunova, Janie Baxter, Rabia Khan, Benjamin Irving

In this work we apply deep semi-supervised embedded clustering to determine data-driven patient subgroups of heart failure from the electronic health records of 4, 487 heart failure and control patients.

Clustering

Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers

1 code implementation4 Jul 2020 Oliver Carr, Fernando Andreotti, Kate E. A. Saunders, Niclas Palmius, Guy M. Goodwin, Maarten De Vos

The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder.

Management

Screening for REM Sleep Behaviour Disorder with Minimal Sensors

1 code implementation24 Oct 2019 Navin Cooray, Fernando Andreotti, Christine Lo, Mkael Symmonds, Michele T. M. Hu, Maarten De Vos

This study investigates a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors.

EEG Electroencephalogram (EEG) +2

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

2 code implementations28 Sep 2018 Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, Maarten De Vos

At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.

General Classification Sleep Staging

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

1 code implementation16 May 2018 Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, Maarten De Vos

While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways.

Automatic Sleep Stage Classification Classification +2

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