74 research outputs found
ICA and Sparse ICA for Biomedical Signals
Biomedical signs or bio signals are a wide range of signals obtained from the human body that can be at the cell organ or sub-atomic level Electromyogram refers to electrical activity from muscle sound signals electroencephalogram refers to electrical activity from the encephalon electrocardiogram refers to electrical activity from the heart electroretinogram refers to electrical activity from the eye and so on Monitoring and observing changes in these signals assist physicians whose work is related to this branch of medicine in covering predicting and curing various diseases It can also assist physicians in examining prognosticating and curing numerous condition
Optimal Resource Allocation Using Deep Learning-Based Adaptive Compression For Mhealth Applications
In the last few years the number of patients with chronic diseases that require constant monitoring increases rapidly; which motivates the researchers to develop scalable remote health applications. Nevertheless, transmitting big real-time data through a dynamic network limited by the bandwidth, end-to-end delay and transmission energy; will be an obstacle against having an efficient transmission of the data. The problem can be resolved by applying data reduction techniques on the vital signs at the transmitter side and reconstructing the data at the receiver side (i.e. the m-Health center). However, a new problem will be introduced which is the ability to receive the vital signs at the server side with an acceptable distortion rate (i.e. deformation of vital signs because of inefficient data reduction).
In this thesis, we integrate efficient data reduction with wireless networking to deliver an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. A Deep Learning (DL) approach was used to implement an adaptive compression technique to compress and reconstruct the vital signs in general and specifically the Electroencephalogram Signal (EEG) with the minimum distortion. Then, a resource allocation framework was introduced to minimize the transmission energy along with the distortion of the reconstructed signa
Edge Intelligence for Empowering IoT-based Healthcare Systems
The demand for real-time, affordable, and efficient smart healthcare services
is increasing exponentially due to the technological revolution and burst of
population. To meet the increasing demands on this critical infrastructure,
there is a need for intelligent methods to cope with the existing obstacles in
this area. In this regard, edge computing technology can reduce latency and
energy consumption by moving processes closer to the data sources in comparison
to the traditional centralized cloud and IoT-based healthcare systems. In
addition, by bringing automated insights into the smart healthcare systems,
artificial intelligence (AI) provides the possibility of detecting and
predicting high-risk diseases in advance, decreasing medical costs for
patients, and offering efficient treatments. The objective of this article is
to highlight the benefits of the adoption of edge intelligent technology, along
with AI in smart healthcare systems. Moreover, a novel smart healthcare model
is proposed to boost the utilization of AI and edge technology in smart
healthcare systems. Additionally, the paper discusses issues and research
directions arising when integrating these different technologies together.Comment: This paper has been accepted in IEEE Wireless Communication Magazin
Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems
Objective: Diagnosis of sleep disorders is an expensive procedure that requires performing a sleep study, known as polysomnography (PSG), in a controlled environment. This study monitors the neural, eye and muscle activity of a patient using electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals which are then scored in to different sleep stages. Home PSG is often cited as an alternative of clinical PSG to make it more accessible, however it still requires patients to use a cumbersome system with multiple recording channels that need to be precisely placed. This thesis proposes a wearable sleep staging system using a single channel of EEG. For realisation of such a system, this thesis presents novel features for REM sleep detection from EEG (normally detected using EMG/EOG), a low-complexity automatic sleep staging algorithm using a single EEG channel and its complete integrated circuit implementation.
Methods: The difference between Spectral Edge Frequencies (SEF) at 95% and 50% in the 8-16 Hz frequency band is shown to have high discriminatory ability for detecting REM sleep stages. This feature, together with other spectral features from single-channel EEG are used with a set of decision trees controlled by a state machine for classification. The hardware for the complete algorithm is designed using low-power techniques and implemented on chip using 0.18μm process node technology.
Results: The use of SEF features from one channel of EEG resulted in 83% of REM sleep epochs being correctly detected. The automatic sleep staging algorithm, based on contextually aware decision trees, resulted in an accuracy of up to 79% on a large dataset. Its hardware implementation, which is also the very first complete circuit level implementation of any sleep staging algorithm, resulted in an accuracy of 98.7% with great potential for use in fully wearable sleep systems.Open Acces
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the
IEEE Signal Processing Society (SPS) promotes activities within the broad
technical field of biomedical image and signal processing. Areas of interest
include medical and biological imaging, digital pathology, molecular imaging,
microscopy, and associated computational imaging, image analysis, and
image-guided treatment, alongside physiological signal processing,
computational biology, and bioinformatics. BISP has 40 members and covers a
wide range of EDICS, including CIS-MI: Medical Imaging, BIO-MIA: Medical Image
Analysis, BIO-BI: Biological Imaging, BIO: Biomedical Signal Processing,
BIO-BCI: Brain/Human-Computer Interfaces, and BIO-INFR: Bioinformatics. BISP
plays a central role in the organization of the IEEE International Symposium on
Biomedical Imaging (ISBI) and contributes to the technical sessions at the IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
and the IEEE International Conference on Image Processing (ICIP). In this
paper, we provide a brief history of the TC, review the technological and
methodological contributions its community delivered, and highlight promising
new directions we anticipate
Sitting behaviour-based pattern recognition for predicting driver fatigue
The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver’s privacy, more acceptable by drivers
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
Power Efficient Data Compression Hardware for Wearable and Wireless Biomedical Sensing Devices
This thesis aims to verify a possible benefit lossless data compression and reduction techniques can bring to a wearable and wireless biomedical device, which is anticipated to be system power saving. A wireless transceiver is one of the main contributors to the system power of a wireless biomedical sensing device, and reducing the data transmitted by the transceiver with a minimum hardware cost can therefore help to save the power. This thesis is going to investigate the impact of the data compression and reduction on the system power of a wearable and wireless biomedical device and trying to find a proper compression technique that can achieve power saving of the device.
The thesis first examines some widely used lossy and lossless data compression and reduction techniques for biomedical data, especially EEG data. Then it introduces a novel lossless biomedical data compression technique designed for this research called Log2 sub-band encoding. The thesis then moves on to the biomedical data compression evaluation of the Log2 sub-band encoding and an existing 2-stage technique consisting of the DPCM and the Huffman encoding. The next part of this thesis explores the signal classification potential of the Log2 sub-band encoding. It was found that some of the signal features extracted as a by-product during the Log2 sub-band encoding process could be used to detect certain signal events like epileptic seizures, with a proper method. The final section of the thesis focuses on the power analysis of the hardware implementation of two compression techniques referred to earlier, as well as the system power analysis. The results show that the Log2 sub-band is comparable and even superior to the 2-stage technique in terms of data compression and power performance. The system power requirement of an EEG signal recorder that has the Log2 sub-band implemented is significantly reduced
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