17 research outputs found
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)
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
An Arrhythmia Classification-Guided Segmentation Model for Electrocardiogram Delineation
Accurate delineation of key waveforms in an ECG is a critical initial step in
extracting relevant features to support the diagnosis and treatment of heart
conditions. Although deep learning based methods using a segmentation model to
locate P, QRS and T waves have shown promising results, their ability to handle
signals exhibiting arrhythmia remains unclear. In this study, we propose a
novel approach that leverages a deep learning model to accurately delineate
signals with a wide range of arrhythmia. Our approach involves training a
segmentation model using a hybrid loss function that combines segmentation with
the task of arrhythmia classification. In addition, we use a diverse training
set containing various arrhythmia types, enabling our model to handle a wide
range of challenging cases. Experimental results show that our model accurately
delineates signals with a broad range of abnormal rhythm types, and the
combined training with classification guidance can effectively reduce false
positive P wave predictions, particularly during atrial fibrillation and atrial
flutter. Furthermore, our proposed method shows competitive performance with
previous delineation algorithms on the Lobachevsky University Database (LUDB)
Deep learning methods for screening patients' S-ICD implantation eligibility.
Acknowledgments The work of Anthony J. Dunn is jointly funded by Decision Analysis Services Ltd. and EPSRC through the Studentship with Reference EP/R513325/1. The work of Alain B. Zemkoho is supported by the EPSRC grant EP/V049038/1 and the Alan Turing Institute under the EPSRC grant EP/N510129/1. The feedback provided by Sion Cave (DAS Ltd) on the initial draft of the paper is gratefully acknowledged.Peer reviewedPublisher PD
Deep learning-based insights on T:R ratio behaviour during prolonged screening for S-ICD eligibility.
Peer reviewedPublisher PD
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It is
associated with an increased risk of stroke, heart failure, and other
cardiovascular complications, but can be clinically silent. Passive AF
monitoring with wearables may help reduce adverse clinical outcomes related to
AF. Detecting AF in noisy wearable data poses a significant challenge, leading
to the emergence of various deep learning techniques. Previous deep learning
models learn from a single modality, either electrocardiogram (ECG) or
photoplethysmography (PPG) signals. However, deep learning models often
struggle to learn generalizable features and rely on features that are more
susceptible to corruption from noise, leading to sub-optimal performances in
certain scenarios, especially with low-quality signals. Given the increasing
availability of ECG and PPG signal pairs from wearables and bedside monitors,
we propose a new approach, SiamAF, leveraging a novel Siamese network
architecture and joint learning loss function to learn shared information from
both ECG and PPG signals. At inference time, the proposed model is able to
predict AF from either PPG or ECG and outperforms baseline methods on three
external test sets. It learns medically relevant features as a result of our
novel architecture design. The proposed model also achieves comparable
performance to traditional learning regimes while requiring much fewer training
labels, providing a potential approach to reduce future reliance on manual
labeling
Deep learning methods for screening patients' S-ICD implantation eligibility
Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for
prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave
Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to
inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio
between the amplitudes of the T and R waves). Currently patients'
Electrocardiograms (ECGs) are screened over 10 seconds to measure the T:R
ratio, determining the patients' eligibility for S-ICD implantation. Due to
temporal variations in the T:R ratio, 10 seconds is not long enough to reliably
determine the normal values of a patient's T:R ratio. In this paper, we develop
a convolutional neural network (CNN) based model utilising phase space
reconstruction matrices to predict T:R ratios from 10-second ECG segments
without explicitly locating the R or T waves, thus avoiding the issue of TWOS.
This tool can be used to automatically screen patients over a much longer
period and provide an in-depth description of the behaviour of the T:R ratio
over that period. The tool can also enable much more reliable and descriptive
screenings to better assess patients' eligibility for S-ICD implantation