A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge

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/

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

Machine learning for the classification of atrial fibrillation utilizing seismo- and gyrocardiogram

A significant number of deaths worldwide are attributed to cardiovascular diseases (CVDs), accounting for approximately one-third of the total mortality in 2019, with an estimated 18 million deaths. The prevalence of CVDs has risen due to the increasing elderly population and improved life expectancy. Consequently, there is an escalating demand for higher-quality healthcare services. Technological advancements, particularly the use of wearable devices for remote patient monitoring, have significantly improved the diagnosis, treatment, and monitoring of CVDs. Atrial fibrillation (AFib), an arrhythmia associated with severe complications and potential fatality, necessitates prolonged monitoring of heart activity for accurate diagnosis and severity assessment. Remote heart monitoring, facilitated by ECG Holter monitors, has become a popular approach in many cardiology clinics. However, in the absence of an ECG Holter monitor, other remote and widely available technologies can prove valuable. The seismo- and gyrocardiogram signals (SCG and GCG) provide information about the mechanical function of the heart, enabling AFib monitoring within or outside clinical settings. SCG and GCG signals can be conveniently recorded using smartphones, which are affordable and ubiquitous in most countries. This doctoral thesis investigates the utilization of signal processing, feature engineering, and supervised machine learning techniques to classify AFib using short SCG and GCG measurements captured by smartphones. Multiple machine learning pipelines are examined, each designed to address specific objectives. The first objective (O1) involves evaluating the performance of supervised machine learning classifiers in detecting AFib using measurements conducted by physicians in a clinical setting. The second objective (O2) is similar to O1, but this time utilizing measurements taken by patients themselves. The third objective (03) explores the performance of machine learning classifiers in detecting acute decompensated heart failure (ADHF) using the same measurements as O1, which were primarily collected for AFib detection. Lastly, the fourth objective (O4) delves into the application of deep neural networks for automated feature learning and classification of AFib. These investigations have shown that AFib detection is achievable by capturing a joint SCG and GCG recording and applying machine learning methods, yielding satisfactory performance outcomes. The primary focus of the examined approaches encompassed (1) feature engineering coupled with supervised classification, and (2) iv automated end-to-end feature learning and classification using deep convolutionalrecurrent neural networks. The key finding from these studies is that SCG and GCG signals reliably capture the heart’s beating pattern, irrespective of the operator. This allows for the detection of irregular rhythm patterns, making this technology suitable for monitoring AFib episodes outside of hospital settings as a remote monitoring solution for individuals suspected to have AFib. This thesis demonstrates the potential of smartphone-based AFib detection using built-in inertial sensors. Notably, a short recording duration of 10 to 60 seconds yields clinically relevant results. However, it is important to recognize that the results for ADHF did not match the state-of-the-art achievements due to the limited availability of ADHF data combined with arrhythmias as well as the lack of a cardiopulmonary exercise test in the measurement setting. Finally, it is important to recognize that SCG and GCG are not intended to replace clinical ECG measurements or long-term ambulatory Holter ECG recordings. Instead, within the scope of our current understanding, they should be regarded as complementary and supplementary technologies for cardiovascular monitoring

Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers” has been accepted for publication in Biomedical Signal Processing and Control, 47, 41–48. The Version of Record is available online at: https://doi.org/10.1016/j.bspc.2018.08.007.[Abstract]: A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art.This work was partially supported by the Research Project RTC-2016-5143-1, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). Also, this work has received financial support from the ERDF and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04