A Review of Atrial Fibrillation Detection Methods as a Service

Faust, Oliver; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.3390/ijerph17093093

Cited by 36 publications

(24 citation statements)

References 206 publications

(184 reference statements)

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“…These signals are easy to measure, cost efficient to communicate, as well as resource efficient to store and process. Hence, this refinement addresses the cost efficiency requirement for the proposed service [ 31 ]. Using HR signals provides the foundation for the functional specification.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Lei

Kareem

Moon

et al. 2021

IJERPH

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In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.

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Section: Methodsmentioning

confidence: 99%

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Lei

Kareem

Moon

et al. 2021

IJERPH

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“…Many of the computer-aided ECG signals proposed for AF detection over the past 50 years are based on machine learning (ML) [14] and have been used in commercial ECG medical devices [15]. Two significant bottlenecks that still hinder early autodetection are the energy limitations of the continuous monitoring equipment and the lack of efficient ML-based models for AF prediction.…”

Section: Introductionmentioning

confidence: 99%

AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

Tutuko

Nurmaini

Tondas³

et al. 2021

Preprint

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Background: Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R-R intervals to determine the Heart Rate Variability (HRV). An accurate HRV is the gold standard for predicting the AF condition. Hence, we propose a DL approach to analyze massive amounts of ECG raw data in a broad range of devices to overcome a current challenge.Results: This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8,232 records based on three datasets and tested with 3,610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. When tested with unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity in 0.02 seconds for one instance when processed in theDL-Cloud System.Conclusions: These findings demonstrate that the proposed model approach can used in a broad range of devices and validated to unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment.

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“…The information content of HR is similar to the information content of other physiological signals which have higher requirements for measurement setup as well as communication and processing infrastructure [ 5 ]. For example, Electrocardiogram (ECG) signals record more details of the human heart’s electrical activity when compared to HR [ 6 ]. Hence, the ECG measurement setup is more complex, and more resources are required to communicate and process the signal.…”

Section: Introductionmentioning

confidence: 99%

“…AF changes the beat to beat interval of the human heart [ 9 ]. However, it is difficult and time consuming for a cardiologist to detect these changes [ 6 ]. Therefore, computer support is essential for HR based AF diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

Faust

Lei

Chew

et al. 2020

IJERPH

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Aim: In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. Subject and Methods: There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. Results: Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. Conclusion: Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.

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A Review of Atrial Fibrillation Detection Methods as a Service

Cited by 36 publications

References 206 publications

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

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