Query: Delgado recurrent neural

Candidate papers:

abstract: Low power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non invasive monitoring of physiological signals. In order to endow such devices with clinical value, classical signal processing has encountered numerous challenges. However, data driven methods, such as machine learning, offer attractive accuracies at the expense of being resource and memory demanding. In this paper, we focus on the inference of neural networks running in microcontrollers and low power processors which wearable sensors and devices are generally equipped with. In particular, we adapted an existing convolutional recurrent neural network, designed to detect and classify cardiac arrhythmias from a singlelead electrocardiogram, to the low power embedded System onChip nRF52 from Nordic Semiconductor with an ARM's CortexM4 processing core. We show our implementation in fixedpoint precision, using the CMSIS NN libraries, yields a drop of F1 score from 0.8 to 0.784, from the original implementation, with a memory footprint of 195.6 KB, and a throughput of 33.98 MOps/s.
author_names: Antonino Faraone

Ricard Delgado-Gonzalo
title: Convolutional Recurrent Neural Networks on Low Power Wearable Platforms for Cardiac Arrhythmia Detection
venue: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
year: 2020

abstract: While most heart arrhythmias are not immediately harmful, they can lead to severe complications. In particular, atrial fibrillation, the most common arrhythmia, is characterized by fast and irregular heart beats and increases the risk of suffering a stroke. To detect such abnormal heart conditions, we propose a system composed of two main parts: a smart vest with two cooperative sensors to collect ECG data and a neural network architecture to classify heart rhythms. The smart vest uses two dry bi electrodes to record a single lead ECG signal. The biopotential signal is then streamed via a gateway to the cloud where a neural network detects and classifies the heart arrhythmias. We selected an architecture that combines convolutional and recurrent layers. The convolutional layers extract relevant features from sliding windows of ECG and the recurrent layer aggregates them for a final softmax layer that performs the classification. Our neural network achieves an accuracy of 87.50% on the dataset of the challenge of Computing in Cardiology 2017.
author_names: Jerome Van Zaen

Olivier Chetelat

Mathieu Lemay

Enric Muntane Calvo

Ricard Delgado-Gonzalo
title: Classification of Cardiac Arrhythmias from Single Lead ECG with a Convolutional Recurrent Neural Network
venue: BIOSIGNALS
year: 2019

abstract: Research in anti spoofing for automatic speaker verification has advanced considerably in the last three years. Antispoofing is a particularly difficult pattern classification problem since the characteristics of spoofed speech vary considerably and can never be predicted with any certainty in the wild. The design of features suited to the detection of unpredictable spoofing attacks is thus a staple of current research. End to end approaches to spoofing detection with exploit automatic feature learning have shown success and offer obvious appeal. This paper presents our efforts to develop such a system using recurrent neural networks and a particular algorithm known as neuroevolution of augmenting topologies (NEAT) Contributions include a new fitness function for network learning that not only results in better generalisation than the baseline system, but which also improves on raw performance by 22% relative when assessed using the ASVspoof 2017 database of bona fide speech and replay spoofing attacks. Results also show that mini batch training helps to improve generalisation, a technique which could also be of benefit to other solutions to the spoofing detection problem.
author_names: Giacomo Valenti

Hector Delgado

Massimiliano Todisco

Nicholas W D Evans

Laurent Pilati
title: An end to end spoofing countermeasure for automatic speaker verification using evolving recurrent neural networks
venue: Odyssey
year: 2018

abstract: We developed a convolutional recurrent neural network to classify 12 lead ECG signals for the challenge of Phy sioNet/Computing in Cardiology 2020 as team Pink Irish Hat. The model combines convolutional and recurrent layers, takes sliding windows of ECG signals as input and yields the probability of each class as output. The convolutional part extracts features from each sliding window. The bi directional gated recurrent unit (GRU) layer and an attention layer aggregate these features from all windows into a single feature vector. Finally, a dense layer outputs class probabilities. The final decision is made using test time augmentation (TTA) and an optimized decision threshold. Several hyperparameters of our architecture were optimized, the most important of which turned out to be the choice of optimizer and the number of filters per convolutional layer. Our network achieved a challenge score of 0.511 on the hidden validation set and 0.167 on the full hidden test set, ranking us 24th out of 41 in the official ranking.
author_names: Halla Sigurthorsdottir

Jerome Van Zaen

Ricard Delgado-Gonzalo

Mathieu Lemay
title: ECG Classification With a Convolutional Recurrent Neural Network
venue: 2020 Computing in Cardiology
year: 2020

abstract: Except for a few specific types, cardiac arrhythmias are not immediately life threatening. However, if not treated appropriately, they can cause serious complications. In particular, atrial fibrillation, which is characterized by fast and irregular heart beats, increases the risk of stroke. We propose three neural network architectures to detect abnormal rhythms from single lead ECG signals. These architectures combine convolutional layers to extract high level features pertinent for arrhythmia detection from sliding windows and recurrent layers to aggregate these features over signals of varying durations. We applied the neural networks to the dataset used for the challenge of Computing in Cardiology 2017 and a dataset built by joining three databases available on PhysioNet. Our architectures achieved an accuracy of 86.23% on the first dataset, similar to the winning entries of the challenge, and an accuracy of 92.02% on the second dataset.
author_names: Jerome Van Zaen

Ricard Delgado-Gonzalo

Damien Ferrario

Mathieu Lemay
title: Cardiac Arrhythmia Detection from ECG with Convolutional Recurrent Neural Networks
venue: BIOSTEC
year: 2019

abstract: The application of neural networks to solve a problem involves tasks with a high computational cost until a suitable network is found, and these tasks mainly involve the selection of the network topology and the training step. We usually select the network structure by means of a trial and error procedure, and we then train the network. In the case of recurrent neural networks (RNNs) the lack of suitable training algorithms sometimes hampers these procedures due to vanishing gradient problems. This paper addresses the simultaneous training and topology optimization of RNNs using multiobjective hybrid procedures. The proposal is based on the SPEA2 and NSGA2 algorithms for making hybrid methods using the Baldwinian hybridization strategy. We also study the effects of the selection of the objectives, crossover, and mutation in the diversity during evolution. The proposals are tested in the experimental section to train and optimize the networks in the competition on artificial time series (CATS) benchmark.
author_names: Miguel Delgado

Manuel P Cuellar

Maria del Carmen Pegalajar Jimenez
title: Multiobjective Hybrid Optimization and Training of Recurrent Neural Networks
venue: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
year: 2008

abstract: A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.
author_names: Alberto Delgado

Chandrasekhar Kambhampati

Kevin Warwick
title: Dynamic recurrent neural network for system identification and control
year: 1995

abstract: Grammatical inference has been extensively studied in recent years as a result of its wide field of application, and in turn, recurrent neural networks have proved themselves to be a good tool for grammatical inference. The learning algorithms for these neural networks, however, have been far less studied than those for feed forward neural networks. Classical training methods for recurrent neural networks suffer from being trapped in local minimal and having a high computational time. In addition, selecting the optimal size of a neural network for a particular application is a difficult task. This suggests that the problems of developing methods to determine optimal topologies and new training algorithms should be studied. In this paper, we present a multi objective evolutionary algorithm which is able to determine the optimal size of recurrent neural networks in any particular application. This is specially analyzed in the case of grammatical inference: in particular, we study how to establish the optimal size of a recurrent neural network in order to learn positive and negative examples in a certain language, and how to determine the corresponding automaton using a self organizing map once the training has been completed.
author_names: Miguel Delgado

Maria del Carmen Pegalajar Jimenez
title: A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference
venue: Pattern Recognit.
year: 2005

abstract: Artificial neural networks are bio inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time series prediction problems.
author_names: Miguel Delgado

Maria del Carmen Pegalajar Jimenez

Manuel P Cuellar
title: Memetic evolutionary training for recurrent neural networks: an application to time series prediction
venue: Expert Syst. J. Knowl. Eng.
year: 2006

abstract: Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) in order to identify the input output dynamics of a class of nonlinear systems. The number of states of the identified network is constrained to be the same as the number of states of the plant.
author_names: Alberto Delgado

Chandrasekhar Kambhampati

Kevin Warwick
title: Identification of nonlinear systems with a dynamic recurrent neural network
year: 1995