Atrial Fibrillation Detection Using Electrocardiogram Signal Input to LMD and Ensemble Classifier

Gupta, Kapil; Bajaj, Varun; Ansari, Irshad Ahmad

doi:10.1109/lsens.2023.3281129

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…Bashar et al (2020) and Bashar, Han, et al (2021) combined features from P-wave characteristics and ECG R-R interval variability to detect AF. García-Isla et al (2021) and Gupta et al (2023), employed local mean decomposition to decompose ECG data and extract entropy-based features, then they used an ensemble boosted trees classifier for AF detection. Hartikainen et al (2019) used ECG data from Holter recordings using entropy measures to detect AF.…”

Section: Atrial Fibrillation Detection With Machine Learningmentioning

confidence: 99%

“…. (2020); Buscema et al (2020); Domazetoski et al (2022); He et al (2022); Huang et al (2021); Nickelsen et al (2017); Pérez-Valero et al (2019); Rad et al (2021); Rouhi et al (2021); Schaefer et al (2014); Wesselius et al (2022); Yao et al (2021), Abdul-Kadir et al (2016),Asgari et al (2015),Daqrouq et al (2014),Fuadah and Lim (2022),Bashar et al (2020), Bashar, Han, et al, 2021, García-Isla et al (2021,Gupta et al (2023),Stergiou et al (2018),Li, Feng, et al (2019),Kalidas and Tamil (2019),Keidar et al (2021),Lee et al (2013), Li et al (2021), Mateo and Joaquín Rieta (2013), Dörr et al (2019), Brüser et al (2013), Zalabarria et al (2020), Baalman et al (2020), Bu s et al (2022), Lown et al (2020), Duan et al (2022), Luongo et al (2022), Eerikainen et al (2020), Liao et al (2022), Lahdenoja et al (2018), Navoret et al (2013), Piekarski et al (2016), Mehrang et al (2019) DL 56 37% Biton et al (2023); Christopoulos et al (2022); Fern andez-Ruiz (2019); Lai et al (2020); Ma and Xia (2023); Mittal et al (2021); Mousavi et al (2021); Orchard et al (2016); Rahul and Sharma (2022); Seo et al (2021); M. U. Yang et al (2022), Pourbabaee et al (2018). H. Wu et al (2022), X. Zhang, Jiang, et al (2023), Prabhakararao and Dandapat (2022), Gahungu et al (2021), X. Chen et al (2021), M. Jiang et al (2021); Z. Li, Feng, et al (2019); Yu et al (2022); O. Zhang, Ding, et al (2021), Fang et al (2023), Ukil et al (2022), Kumar et al (2022), Kong et al (2019), Baek et al (2021); Cai et al (2020), Cao et al (2019), Ribeiro et al (2020), Petroni et al (2023), P. Zhang et al (2022); X. Zhang, Jiang, et al (2023), Fiorina et al (2022), Ríos-Muñoz et al (2022), Diamant et al (2022), Shi et al (2020), Weimann and Conrad (2021), Xiong et al (2022), B. Chen et al (2023), Asadi et al (2023), Jo et al (2021), Mousavi et al (2020), Salinas-Martínez et al (2021), B. Chen et al (2022), Ivaturi et al (2021), Parmar et al (2023), C amara-V azquez et al (2021), S. Liu, Wang, et al (2022), Kwon et al (2020), Kudo et al (2023), Kwon et al (2020), Cheng et al (2020), O. Zhang, Ding, et al (2021), Wasserlauf e...…”

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confidence: 99%

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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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Section: Atrial Fibrillation Detection With Machine Learningmentioning

confidence: 99%

mentioning

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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show abstract

“…and EEG signals (stress level, fatigue, etc.). [ 11 , 12 , 13 ] There are also some disorders related to the heart (heart arrhythmia, sleep disorder, etc.) and brain (epilepsy, sleep disorder, etc.)…”

Section: Introductionmentioning

confidence: 99%

Heart–brain Rhythmic Synchronization during Meditation: A Nonlinear Signal Analysis

Anurag,

Singh,

Krishna

et al. 2023

International Journal of Yoga

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Background: Heart–brain synchronization is the integration of mind, body, and spirit. It occurs when the electrical activity of the heart and brain is synchronized. In recent years, there has been mounting curiosity to investigate the effects of meditation on heart–brain synchronization with respect to mental and emotional health and well-being. The current investigation aims to explore the rhythmic synchronicity between the brain and the heart during heartfulness meditation (HM) practice. Materials and Methods: The study was performed on 45 healthy volunteers who were categorized into three equal groups: long-term meditators (LTMs), short-term meditators (STMs), and nonmeditators (NMs). The electroencephalogram (EEG) signals were recorded to measure the prefrontal activity, and electrocardiogram (ECG) signals were recorded to measure the cardiac activity. The data were recorded in four states: baseline, meditation, transmission, and posttransmission. The detrended fluctuation analysis (DFA) method was used for the analysis of EEG and ECG signals. Results: The result indicates that DFA values of EEG and ECG declined during meditation and transmission states as compared to pre- and postmeditation states. Significant results were obtained for the LTM group in all the states. A positive correlation was also observed between DFA of the heart and brain for the LTM group and no significant correlations were observed for the STM and NM groups. Conclusion: The shreds of evidence suggest that heart–brain synchronization facilitates mental and emotional stability. HM practice has the potential to regulate the fluctuation of the mind. Regular meditation practice may result in physiological synchrony between cardiac and neural behavior, which can be considered a quality index for meditation practice.

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Detection of Atrial Fibrillation from ECG Signal Using Efficient Feature Selection and Classification

Anbalagan,

Nath,

Anbalagan

2024

Circuits Syst Signal Process

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Atrial Fibrillation Detection Using Electrocardiogram Signal Input to LMD and Ensemble Classifier

Cited by 12 publications

References 19 publications

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

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

Heart–brain Rhythmic Synchronization during Meditation: A Nonlinear Signal Analysis

Detection of Atrial Fibrillation from ECG Signal Using Efficient Feature Selection and Classification

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