Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation

Escribano, Pilar; Ródenas, Juan; García, Manuel; Arias, Miguel A.; Hidalgo, Víctor M.; Calero, Sofía; Rieta, José J.; Alcaraz, Raúl

doi:10.1016/j.heliyon.2024.e25295

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e25295

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Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation

Pilar Escribano,

Juan Ródenas,

Manuel García

et al.

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Cited by 2 publications

References 78 publications

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Non-invasive prediction of atrial fibrillation recurrence by recurrence quantification analysis on the fibrillation cycle length

Feng,

Chen,

Fang

et al. 2025

Biomedical Signal Processing and Control

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Non-invasive prediction of atrial fibrillation recurrence by recurrence quantification analysis on the fibrillation cycle length

Feng,

Chen,

Fang

et al. 2025

Biomedical Signal Processing and Control

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Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption

Agbehadji,

Millham,

Freeman

et al. 2024

Sensors

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Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features. An example of a human-related attribute includes the electrical signal generated from the heart, mostly referred to as an Electrocardiogram (ECG) signal. The methods to extract features from ECG signals are time domain-based; however, the challenge with relying only on the time-domain or frequency-domain method is the inability to capture the intra-leading relationship of Variational Mode Decomposition signals. In this research, fusing multiple domains ECG feature and adaptive Variational Mode Decomposition approaches are utilized to mitigate the challenge of losing the intra-leading correlations of mode decompositions, which might reduce the robustness of encryption algorithms. The features extracted using the reconstructed signal have a mean (0.0004), standard deviation (0.0391), skewness (0.1562), and kurtosis (1.2205). Among the lightweight encryption methods considered, Chacha20 has a total execution time of 27µs. The study proposes a lightweight encryption technique based on the fused vector representation of extracted features to provide an encryption scheme in addition to a bio-inspired key generation technique for data encryption.

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Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation

Cited by 2 publications

References 78 publications

Non-invasive prediction of atrial fibrillation recurrence by recurrence quantification analysis on the fibrillation cycle length

Non-invasive prediction of atrial fibrillation recurrence by recurrence quantification analysis on the fibrillation cycle length

Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption

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