361 research outputs found
Biosignals events detection. A Morphological Signal independent Approach
This study presents a signal-independent algorithm, which detects significant events in a biosignal, withoutprevious knowledge or specific pre-processing steps. From a morphological analysis, the algorithm computesthe instants when the most significant standard deviation discontinuities occur. An iterative optimization stepis then applied. This assures that a minimal error is achieved when modeling the signal segments (betweenthe detected instants) with a polynomial regression. The detection scale can be modified by an optional inputscale factor. An objective algorithm performance evaluation procedure was designed, and applied on twotypes of synthetic signals, for which the events instants were previously known. An overall mean error of20.32 ( 16.01) samples between the detected and the real events show the high accuracy of the proposedalgorithm. The algorithm was also applied on accelerometry and electromyography raw signals collected indifferent experimental scenarios. The fact that this approach does not require any previous knowledge and thegood level of accuracy represents a relevant contribution in events detection and biosignal analysis
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
Time series morphological analysis applied to biomedical signals events detection
Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical EngineeringAutomated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary
processing steps.
The present dissertation introduces a signal-independent algorithm, which detects significant events in a biosignal. From a time series morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur, segmenting the signal. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling these segments with polynomial regressions. The adjustment of a scale factor gives different detail levels of events detection.
An accurate and objective algorithm performance evaluation procedure was designed.
When applied on a set of synthetic signals, with known and quantitatively predefined events, an overall mean error of 20 samples between the detected and the actual events showed the high accuracy of the proposed algorithm. Its ability to perform the detection of signal activation onsets and transient waveshapes was also assessed, resulting in higher reliability than
signal-specific standard methods.
Some case studies, with signal processing requirements for which the developed algorithm can be suitably applied, were approached. The algorithm implementation in real-time, as part of an application developed during this research work, is also reported.
The proposed algorithm detects significant signal events with accuracy and significant
noise immunity. Its versatile design allows the application in different signals without previous knowledge on their statistical properties or specific preprocessing steps. It also brings added objectivity when compared with the exhaustive and time-consuming examiner analysis.
The tool introduced in this dissertation represents a relevant contribution in events detection, a particularly important issue within the wide digital biosignal processing research field
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 Ãnicio da década. A mirÃade de dados fisiológicos
gerados por indÃviduos, 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 sÃntese 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 ruÃdo
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 à s caracterÃsticas biológicas
Algorithms for time series clustering applied to biomedical signals
Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical EngineeringThe increasing number of biomedical systems and applications for human body understanding
creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field.
The present dissertation introduces new algorithms for time series clustering, where
we are able to separate and organize unlabeled data into different groups whose signals are similar to each other.
Signal processing algorithms were developed for the detection of a meanwave, which
represents the signal’s morphology and behavior. The algorithm designed computes
the meanwave by separating and averaging all cycles of a cyclic continuous signal. To
increase the quality of information given by the meanwave, a set of wave-alignment
techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which
models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show
the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention.
The algorithms produced are signal-independent, and therefore can be applied to
any type of signal providing it is a cyclic signal. The fact that this approach doesn’t
require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification
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%
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
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