115 research outputs found
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with
significant health ramifications, including an elevated susceptibility to
ischemic stroke, heart disease, and heightened mortality. Photoplethysmography
(PPG) has emerged as a promising technology for continuous AF monitoring for
its cost-effectiveness and widespread integration into wearable devices. Our
team previously conducted an exhaustive review on PPG-based AF detection before
June 2019. However, since then, more advanced technologies have emerged in this
field. This paper offers a comprehensive review of the latest advancements in
PPG-based AF detection, utilizing digital health and artificial intelligence
(AI) solutions, within the timeframe spanning from July 2019 to December 2022.
Through extensive exploration of scientific databases, we have identified 59
pertinent studies. Our comprehensive review encompasses an in-depth assessment
of the statistical methodologies, traditional machine learning techniques, and
deep learning approaches employed in these studies. In addition, we address the
challenges encountered in the domain of PPG-based AF detection. Furthermore, we
maintain a dedicated website to curate the latest research in this area, with
regular updates on a regular basis
A Systematic Review of Detecting Sleep Apnea Using Deep Learning
Sleep apnea is a sleep related disorder that significantly affects the population.
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert
technician is needed to score. Numerous researchers have proposed and implemented automatic
scoring processes to address these issues, based on fewer sensors and automatic classification
algorithms. Deep learning is gaining higher interest due to database availability, newly developed
techniques, the possibility of producing machine created features and higher computing power that
allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep
apnea research has currently gained significant interest in deep learning. The goal of this work is to
analyze the published research in the last decade, providing an answer to the research questions such
as how to implement the different deep networks, what kind of pre-processing or feature extraction is
needed, and the advantages and disadvantages of different kinds of networks. The employed signals,
sensors, databases and implementation challenges were also considered. A systematic search was
conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were
selected by considering the inclusion and exclusion criteria, using the preferred reporting items for
systematic reviews and meta-analyses (PRISMA) approach.info:eu-repo/semantics/publishedVersio
Automated Classification for Electrophysiological Data: Machine Learning Approaches for Disease Detection and Emotion Recognition
Smart healthcare is a health service system that utilizes technologies, e.g., artificial intelligence and
big data, to alleviate the pressures on healthcare systems. Much recent research has focused on the
automatic disease diagnosis and recognition and, typically, our research pays attention on automatic
classifications for electrophysiological signals, which are measurements of the electrical activity.
Specifically, for electrocardiogram (ECG) and electroencephalogram (EEG) data, we develop a
series of algorithms for automatic cardiovascular disease (CVD) classification, emotion recognition
and seizure detection.
With the ECG signals obtained from wearable devices, the candidate developed novel signal
processing and machine learning method for continuous monitoring of heart conditions. Compared to
the traditional methods based on the devices at clinical settings, the developed method in this thesis
is much more convenient to use. To identify arrhythmia patterns from the noisy ECG signals obtained
through the wearable devices, CNN and LSTM are used, and a wavelet-based CNN is proposed to
enhance the performance.
An emotion recognition method with a single channel ECG is developed, where a novel exploitative
and explorative GWO-SVM algorithm is proposed to achieve high performance emotion
classification. The attractive part is that the proposed algorithm has the capability to learn the SVM
hyperparameters automatically, and it can prevent the algorithm from falling into local solutions,
thereby achieving better performance than existing algorithms.
A novel EEG-signal based seizure detector is developed, where the EEG signals are transformed to
the spectral-temporal domain, so that the dimension of the input features to the CNN can be
significantly reduced, while the detector can still achieve superior detection performance
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
The vast proliferation of sensor devices and Internet of Things enables the
applications of sensor-based activity recognition. However, there exist
substantial challenges that could influence the performance of the recognition
system in practical scenarios. Recently, as deep learning has demonstrated its
effectiveness in many areas, plenty of deep methods have been investigated to
address the challenges in activity recognition. In this study, we present a
survey of the state-of-the-art deep learning methods for sensor-based human
activity recognition. We first introduce the multi-modality of the sensory data
and provide information for public datasets that can be used for evaluation in
different challenge tasks. We then propose a new taxonomy to structure the deep
methods by challenges. Challenges and challenge-related deep methods are
summarized and analyzed to form an overview of the current research progress.
At the end of this work, we discuss the open issues and provide some insights
for future directions
Applying Artificial Intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review
Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may fac
Artificial Intelligence for Data Analysis and Signal Processing
Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases.
It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways.
In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods.
A human gait identification system based on the analysis of inertial signals has been developed, leading to misclassification rates smaller than 0.15%.
Advanced deep learning architectures have been also investigated to tackle the problem of atrial fibrillation detection from short length and noisy electrocardiographic signals. The results show a clear improvement provided by representation learning over a knowledge-based approach.
Another important clinical challenge, both for the patient and on-board automatic alarm systems, is to detect with reasonable advance the patterns leading to risky situations, allowing the patient to take therapeutic decisions on the basis of future instead of current information. This problem has been specifically addressed for the prediction of critical hypo/hyperglycemic episodes from continuous glucose monitoring devices, carrying out a comparative analysis among the most successful methods for glucose event prediction.
This dissertation also shows evidence of the benefits of learning algorithms for vehicular traffic anomaly detection, through the use of a statistical Bayesian framework, and for the optimization of video streaming user experience, implementing an intelligent adaptation engine for video streaming clients.
The proposed solution explores the promising field of deep learning methods integrated with reinforcement learning schema, showing its benefits against other state of the art approaches.
The great knowledge transfer capability of artificial intelligence methods and the benefits of representation learning systems stand out from this research, representing the common thread among all the presented research fields
Novel neural approaches to data topology analysis and telemedicine
1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz
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