139 research outputs found
Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data
A model to enhance the atrial fibrillations’ risk detection using deep learning
Atrial fibrillation (AF) is a complex arrhythmia linked to a variety of common cardiovascular illnesses and conventional cardiovascular risk factors. Although awareness and improved detection of AF have improved over the last decade as the incidence and prevalence of AF has increased, current trends in using machine learning approaches to diagnose AF are still lacking in precision. To determine the true nature of the Electrocardiography (ECG) signal segments, a Convolutional Neural Network (CNN) model was employed to discover hidden information. Fully Connected (FC) layers were then utilized to categorize the ECG data segments as normal or abnormal. The suggested algorithm's findings were compared to state-of-the-art arrhythmia identification algorithms in the literature for the MIT-BIH ECG database. The methodology proved not only to yield high classification performance (98.5%) but also low processing computational advantage where the CNN was the most accurate algorithm used for atrial fibrillation detection hence. To conclude the findings of the research, a model was prepared to test the accuracy of the most common ML algorithms used for AF detection. After comparing the results of the experiment, it was clear that CNN algorithm is the best approach compared to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)
Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects
Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors
Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/
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
Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector
Cardiovascular diseases (CVDs) are the number one cause of death worldwide.
While there is growing evidence that the atrial fibrillation (AF) has strong
associations with various CVDs, this heart arrhythmia is usually diagnosed
using electrocardiography (ECG) which is a risk-free, non-intrusive, and
cost-efficient tool. Continuously and remotely monitoring the subjects' ECG
information unlocks the potentials of prompt pre-diagnosis and timely
pre-treatment of AF before the development of any life-threatening
conditions/diseases. Ultimately, the CVDs associated mortality could be
reduced. In this manuscript, the design and implementation of a personalized
healthcare system embodying a wearable ECG device, a mobile application, and a
back-end server are presented. This system continuously monitors the users' ECG
information to provide personalized health warnings/feedbacks. The users are
able to communicate with their paired health advisors through this system for
remote diagnoses, interventions, etc. The implemented wearable ECG devices have
been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable
inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%).
To boost the battery life of the wearable devices, a lossy compression schema
utilizing the quasi-periodic feature of ECG signals to achieve compression was
proposed. Compared to the recognized schemata, it outperformed the others in
terms of compression efficiency and distortion, and achieved at least 2x of CR
at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable
automated AF diagnosis/screening in the proposed system, a ResNet-based AF
detector was developed. For the ECG records from the 2017 PhysioNet CinC
challenge, this AF detector obtained an average testing F1=85.10% and a best
testing F1=87.31%, outperforming the state-of-the-art
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