364 research outputs found
Affective Man-Machine Interface: Unveiling human emotions through biosignals
As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals
Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Despite their immense success in numerous fields, machine and deep learning
systems have not yet been able to firmly establish themselves in
mission-critical applications in healthcare. One of the main reasons lies in
the fact that when models are presented with previously unseen,
Out-of-Distribution samples, their performance deteriorates significantly. This
is known as the Domain Generalization (DG) problem. Our objective in this work
is to propose a benchmark for evaluating DG algorithms, in addition to
introducing a novel architecture for tackling DG in biosignal classification.
In this paper, we describe the Domain Generalization problem for biosignals,
focusing on electrocardiograms (ECG) and electroencephalograms (EEG) and
propose and implement an open-source biosignal DG evaluation benchmark.
Furthermore, we adapt state-of-the-art DG algorithms from computer vision to
the problem of 1D biosignal classification and evaluate their effectiveness.
Finally, we also introduce a novel neural network architecture that leverages
multi-layer representations for improved model generalizability. By
implementing the above DG setup we are able to experimentally demonstrate the
presence of the DG problem in ECG and EEG datasets. In addition, our proposed
model demonstrates improved effectiveness compared to the baseline algorithms,
exceeding the state-of-the-art in both datasets. Recognizing the significance
of the distribution shift present in biosignal datasets, the presented
benchmark aims at urging further research into the field of biomedical DG by
simplifying the evaluation process of proposed algorithms. To our knowledge,
this is the first attempt at developing an open-source framework for evaluating
ECG and EEG DG algorithms.Comment: Accepted in IEEE Transactions on Emerging Topics in Computational
Intelligenc
Detection of abnormalities in ECG using Deep Learning
A significant part of healthcare is focused on the information that the physiological signals
offer about the health state of an individual. The Electrocardiogram (ECG) cyclic
behaviour gives insight on a subject’s emotional, behavioral and cardiovascular state.
These signals often present abnormal events that affects their analysis. Two examples
are the noise, that occurs during the acquisition, and symptomatic patterns, that are
produced by pathologies.
This thesis proposes a Deep Neural Networks framework that learns the normal behaviour
of an ECG while detecting abnormal events, tested in two different settings:
detection of different types of noise, and; symptomatic events caused by different pathologies.
Two algorithms were developed for noise detection, using an autoencoder and
Convolutional Neural Networks (CNN), reaching accuracies of 98,18% for the binary
class model and 70,74% for the multi-class model, which is able to discern between base
wandering, muscle artifact and electrode motion noise. As for the arrhythmia detection
algorithm was developed using an autoencoder and Recurrent Neural Networks with
Gated Recurrent Units (GRU) architecture. With an accuracy of 56,85% and an average
sensitivity of 61.13%, compared to an average sensitivity of 75.22% for a 12 class
model developed by Hannun et al. The model detects 7 classes: normal sinus rhythm,
paced rhythm, ventricular bigeminy, sinus bradycardia, atrial fibrillation, atrial flutter
and pre-excitation.
It was concluded that the process of learning the machine learned features of the
normal ECG signal, currently sacrifices the accuracy for higher generalization. It performs
better at discriminating the presence of abnormal events in ECG than classifying different
types of events. In the future, these algorithms could represent a huge contribution in
signal acquisition for wearables and the study of pathologies visible in not only in ECG,
but also EMG and respiratory signals, especially applied to active learning
Learning Biosignals with Deep Learning
The healthcare system, which is ubiquitously recognized as one of the most influential
system in society, is facing new challenges since the start of the decade.The myriad of
physiological data generated by individuals, namely in the healthcare system, is generating
a burden on physicians, losing effectiveness on the collection of patient data. Information
systems and, in particular, novel deep learning (DL) algorithms have been prompting a
way to take this problem.
This thesis has the aim to have an impact in biosignal research and industry by
presenting DL solutions that could empower this field. For this purpose an extensive study
of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive
Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed.
Different architecture configurations were explored for signal processing and decision
making and were implemented in three different scenarios: (1) Biosignal learning and
synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram
(ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate
autonomously three types of biosignals with a high degree of confidence. As for (2) three
CNN-based architectures, and a RNN-based architecture (same used in (1)) were used
for both biometric identification, reaching values above 90% for electrode-base datasets
(Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric
authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH
and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal
and detection of its deviation was made and tested in two different scenarios: presence of
noise using autoencoder and fully-connected network (reaching 99% accuracy for binary
classification and 71% for multi-class), and; arrhythmia events by including a RNN to the
previous architecture (57% accuracy and 61% sensitivity).
In sum, these systems are shown to be capable of producing novel results. The incorporation
of several AI systems into one could provide to be the next generation of
preventive medicine, as the machines have access to different physiological and anatomical
states, it could produce more informed solutions for the issues that one may face in the
future increasing the performance of autonomous preventing systems that could be used
in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context
as explainable AI could trigger this perception and link the inner states of a network with
the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes
da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos
gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo
para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de
informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda
(DL) têm sido usados na procura de uma solução para este problema.
Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais,
apresentando soluções de DL que poderiam melhorar esta área de investigação. Para
esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais
convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para
o estudo de biosinais.
Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de
sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de
biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema
de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz
de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança.
Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN
(a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores
acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT
-BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo
taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para
CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção
do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído
usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão
na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN
na arquitetura anterior (57 % de precisão e 61 % de sensibilidade).
Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir
resultados inovadores. A incorporação de vários sistemas de inteligência artificial em
um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os
algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir
soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida
quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes
sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no
contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os
estados internos de uma rede às características biológicas
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques
Dissertação para obtenção do Grau de Mestre em
Engenharia BiomédicaOne of the biggest challenges when analysing data is to extract information from it,
especially if we dealing with very large sized data, which brings a new set of barriers to be overcome. The extracted information can be used to aid physicians in their diagnosis since biosignals often carry vital information on the subjects.
In this research work, we present a signal-independent algorithm with two main goals: perform events detection in biosignals and, with those events, extract information
using a set of distance measures which will be used as input to a parallel version of
the k-means clustering algorithm. The first goal is achieved by using two different approaches.
Events can be found based on peaks detection through an adaptive threshold defined as the signal’s root mean square (RMS) or by morphological analysis through the computation of the signal’s meanwave. The final goal is achieved by dividing the distance measures into n parts and by performing k-means individually. In order to improve speed performance, parallel computing techniques were applied.
For this study, a set of different types of signals was acquired and annotated by our
algorithm. By visual inspection, the L1 and L2 Minkowski distances returned an output
that allowed clustering signals’ cycles with an efficiency of 97:5% and 97:3%, respectively.
Using the meanwave distance, our algorithm achieved an accuracy of 97:4%. For the downloaded ECGs from the Physionet databases, the developed algorithm detected
638 out of 644 manually annotated events provided by physicians.
The fact that this algorithm can be applied to long-term raw biosignals and without
requiring any prior information about them makes it an important contribution in biosignals’ information extraction and annotation
Human activity recognition for an intelligent knee orthosis
Dissertação para obtenção do Grau de Mestre em
Engenharia BiomédicaActivity recognition with body-worn sensors is a large and growing field of research.
In this thesis we evaluate the possibility to recognize human activities based on data from
biosignal sensors solely placed on or under an existing passive knee orthosis, which will
produce the needed information to integrate sensors into the orthosis in the future.
The development of active orthotic knee devices will allow population to ambulate
in a more natural, efficient and less painful manner than they might with a traditional
orthosis. Thus, the term ’active orthosis’ refers to a device intended to increase the ambulatory
ability of a person suffering from a knee pathology by applying forces to correct
the position only when necessary and thereby make usable over longer periods of time.
The contribution of this work is the evaluation of the ability to recognize activities
with these restrictions on sensor placement as well as providing a proof-of-concept for
the development of an activity recognition system for an intelligent orthosis.
We use accelerometers and a goniometer placed on the orthosis and Electromyography
(EMG) sensors placed on the skin under the orthosis to measure motion and muscle activity
respectively. We segment signals in motion primitives semi-automatically and apply
Hidden-Markov-Models (HMM) to classify the isolated motion primitives. We discriminate
between seven activities like for example walking stairs up and ascend a hill. In
a user study with six participants, we evaluate the systems performance for each of the
different biosignal modalities alone as well as the combinations of them. For the best
performing combination, we reach an average person-dependent accuracy of 98% and a
person-independent accuracy of 79%
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