Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection

Kumar, Devender; Peimankar, Abdolrahman; Sharma, Kamal; Domínguez, Helena; Puthusserypady, Sadasivan; Bardram, Jakob E.

doi:10.1016/j.cmpb.2022.106899

Cited by 20 publications

(8 citation statements)

References 52 publications

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“…The ECG recordings used for training were mainly NSR (normal sinus rhythm). The majority of the arrhythmias presented in MITDB and LTAF were not in the training set, which caused the model to confound AF rhythm and other arrhythmias [ 38 , 39 , 40 ]. A fact that should not be ignored is that there usually exist other arrhythmias if a patient has AF.…”

Section: Discussionmentioning

confidence: 99%

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Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model

Feng

Wang

et al. 2023

JPM

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Background and Objectives: Atrial fibrillation (AF) is one of the most common arrhythmias clinically. Aging tends to increase the risk of AF, which also increases the burden of other comorbidities, including coronary artery disease (CAD), and even heart failure (HF). The precise detection of AF is a challenge due to its intermittence and unpredictability. A method for the accurate detection of AF is still needed. Methods: A deep learning model was used to detect atrial fibrillation. Here, a distinction was not made between AF and atrial flutter (AFL), both of which manifest as a similar pattern on an electrocardiogram (ECG). This method not only discriminated AF from normal rhythm of the heart, but also detected its onset and offset. The proposed model involved residual blocks and a Transformer encoder. Results and Conclusions: The data used for training were obtained from the CPSC2021 Challenge, and were collected using dynamic ECG devices. Tests on four public datasets validated the availability of the proposed method. The best performance for AF rhythm testing attained an accuracy of 98.67%, a sensitivity of 87.69%, and a specificity of 98.56%. In onset and offset detection, it obtained a sensitivity of 95.90% and 87.70%, respectively. The algorithm with a low FPR of 0.46% was able to reduce troubling false alarms. The model had a great capability to discriminate AF from normal rhythm and to detect its onset and offset. Noise stress tests were conducted after mixing three types of noise. We visualized the model’s features using a heatmap and illustrated its interpretability. The model focused directly on the crucial ECG waveform where showed obvious characteristics of AF.

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Section: Discussionmentioning

confidence: 99%

“…Ref. [ 40 ] concluded that ectopic beats and certain non-AF rhythms caused more FPs in AF detection. Our results on LTAF were consistent with previous research.…”

Section: Discussionmentioning

confidence: 99%

Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model

Feng

Wang

et al. 2023

JPM

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“…Ukil et al (2022) developed AFSense-ECG, an intelligent single-lead ECG sensor capable of accurately detecting AF. Kumar et al (2022) proposed a DL model comprising convolutional and recurrent neural networks that analyzed temporal and morphological features from the ECG data to classify AF versus normal sinus rhythm. Faust et al (2018) applied a long short-term memory network with RR interval signals for automated detection of AF.…”

Section: Atrial Fibrillation Detection With Deep Learningmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence

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“…Such information can be made available to a cardiologist for the manual inspection of the dataset thereby providing a better insight into when and why the AF detection algorithm has identified an AF episode. The information can also be utilized to build postprocessing heuristics around these FP prone ambulatory contexts (43). With CACHET-CADB, we aim to provide the DL research community rich longitudinal contextualized ECG data that can help build and evaluate models which realistically work on patient-operated ECG from free-living ambulatory conditions.…”

Section: Context-aware Ecg For Explainable DL Modelsmentioning

confidence: 99%

CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset

Kumar

Puthusserypady

Dominguez

et al. 2022

Front. Cardiovasc. Med.

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ECG is a non-invasive tool for arrhythmia detection. In recent years, wearable ECG-based ambulatory arrhythmia monitoring has gained increasing attention. However, arrhythmia detection algorithms trained on existing public arrhythmia databases show higher FPR when applied to such ambulatory ECG recordings. It is primarily because the existing public databases are relatively clean as they are recorded using clinical-grade ECG devices in controlled clinical environments. They may not represent the signal quality and artifacts present in ambulatory patient-operated ECG. To help build and evaluate arrhythmia detection algorithms that can work on wearable ECG from free-living conditions, we present the design and development of the CACHET-CADB, a multi-site contextualized ECG database from free-living conditions. The CACHET-CADB is subpart of the REAFEL study, which aims at reaching the frail elderly patient to optimize the diagnosis of atrial fibrillation. In contrast to the existing databases, along with the ECG, CACHET-CADB also provides the continuous recording of patients' contextual data such as activities, body positions, movement accelerations, symptoms, stress level, and sleep quality. These contextual data can aid in improving the machine/deep learning-based automated arrhythmia detection algorithms on patient-operated wearable ECG. Currently, CACHET-CADB has 259 days of contextualized ECG recordings from 24 patients and 1,602 manually annotated 10 s heart-rhythm samples. The length of the ECG records in the CACHET-CADB varies from 24 h to 3 weeks. The patient's ambulatory context information (activities, movement acceleration, body position, etc.) is extracted for every 10 s interval cumulatively. From the analysis, nearly 11% of the ECG data in the database is found to be noisy. A software toolkit for the use of the CACHET-CADB is also provided.

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Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection

Cited by 20 publications

References 52 publications

Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model

Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset

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