Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation

Martínez, Antonio Cebrián; Abásolo, Daniel; Alcaraz, Raúl; Rieta, J.J.

doi:10.1016/j.medengphy.2015.03.021

Cited by 12 publications

(8 citation statements)

References 33 publications

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“…Figure 4 shows the ROC curve of the linear SVM for the patient-specific selection of the best feature set. Finally, considering all the patients in the dataset, our method is able to predict PAF onset with an F1 score of 97.1%, sensitivity of 96.2%, and specificity of 98.1%, which is higher than other approaches that only consider inter-patient variability (79% [2] and 93% [4]).…”

Section: Evaluation On Test Setmentioning

confidence: 87%

“…Then, they extract the probability that a specific degree of P-wave variability is associated with a PAF episode. Other approaches consider the P-wave non-linear dynamics to achieve higher accuracy in the prediction [4]. However, AF is caused by heterogeneous mechanisms in different patients and the therapeutic strategies should derive from the individual conditions.…”

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confidence: 99%

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A Patient-Specific Methodology for Prediction of Paroxysmal Atrial Fibrillation Onset

Giovanni

Aminifar

Luca

et al. 2017

Computing in Cardiology Conference (CinC)

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In spite of the progress in management of Atrial Fibrillation (AF), this arrhythmia is one of the major causes of stroke and heart failure. The progression of this pathology from a silent paroxysmal form (PAF) into a sustained AF can be prevented by predicting the onset of PAF episodes. Moreover, since AF is caused by heterogeneous mechanisms in different patients, as we demonstrate in this paper, a patient-specific approach offers a promising solution. In this work, we consider two ECG recordings, one close to PAF onset and one far away from any PAF episode. For each patient, we extract two 5-minute ECG segments approximately 20 minutes apart. Next, we train a linear Support Vector Machine (SVM) classifier using patientspecific sets of time-and amplitude-domain features. In particular, we consider the P-waves and the QRS complexes in short windows of 5 consecutive heart beats. Finally, we validate the method on the PAF Prediction Challenge (2001) PhysioNet database predicting the onset with an F1 score of 97.1%, sensitivity of 96.2% and specificity of 98.1%.

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Section: Evaluation On Test Setmentioning

confidence: 87%

mentioning

confidence: 99%

A Patient-Specific Methodology for Prediction of Paroxysmal Atrial Fibrillation Onset

Giovanni

Aminifar

Luca

et al. 2017

Computing in Cardiology Conference (CinC)

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“…Therefore, if subtle traces of AF that appear on a sinus rhythm ECG are detected, patients who are at high risk for AF can be stratified early. Studies have investigated the possibility of an early diagnosis of AF by using a long PR interval and the P-wave amplitude ( 11 , 14 – 16 ). Furthermore, an AF prediction model that used heartrate variability (HRV) in ECG data obtained for at least 5 min before AF documentation showed high specificity and sensitivity ( 17 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation

Kim,

Joo,

Jeon

et al. 2023

Front. Cardiovasc. Med.

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Background and aimsIt is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent atrial fibrillation (non-PeAF). There is limited understanding of whether an AI prediction algorithm could predict the occurrence of non-PeAF from the information of normal sinus rhythm (SR) of a 12-lead ECG. This study aimed to derive a precise predictive AI model for screening non-PeAF using SR ECG within 4 weeks.MethodsThis retrospective cohort study included patients aged 18 to 99 with SR ECG on 12-lead standard ECG (10 seconds) in Ewha Womans University Medical Center for 3 years. Data were preprocessed into three window periods (which are defined with the duration from SR to non-PeAF detection) – 1 week, 2 weeks, and 4 weeks from the AF detection prospectively. For experiments, we adopted a Residual Neural Network model based on 1D-CNN proposed in a previous study. We used 7,595 SR ECGs (extracted from 215,875 ECGs) with window periods of 1 week, 2 weeks, and 4 weeks for analysis.ResultsThe prediction algorithm showed an AUC of 0.862 and an F1-score of 0.84 in the 1:4 matched group of a 1-week window period. For the 1:4 matched group of a 2-week window period, it showed an AUC of 0.864 and an F1-score of 0.85. Finally, for the 1:4 matched group of a 4-week window period, it showed an AUC of 0.842 and an F1-score of 0.83.ConclusionThe AI prediction algorithm showed the possibility of risk stratification for early detection of non-PeAF. Moreover, this study showed that a short window period is also sufficient to detect non-PeAF.

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“…In Table I we compare the accuracy of the two inter-patient variability approaches presented by Martínez et al [21] and Ebrahimzadeh et al [22], the offline personalized algorithm presented in our previous work [26], our real-time optimized personalized single-core approach considering the waveletbased R peak detection [31], and the optimized version with the REWARD algorithm [32] presented in Section IV-B. Moreover, we report sensitivity and specificity of the personalized approaches.…”

Section: A Accuracy Of the Paf Event Predictionmentioning

confidence: 99%

“…The scalability is driven by the adaptive algorithm and architecture parameters, which affect the design in single-and multi-core platforms to reduce energy consumption for each individual patient. In order to draw a comparison with our method, we report two key cases of the recent literature [21], [22], describing offline methodologies, which include inter-patient variability and achieve higher accuracy compared to other methods [23]- [25]. Finally, we compare the accuracy results with our previous work on offline PAF prediction [26].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

Giovanni

Valdés

Peon

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel computing capabilities that can enable prevention diagnosis and treatment of diseases in ambulatory or home-based setups. In this paper, we explore the benefits of the parallelization capabilities and computing heterogeneity of new wearable sensors in the context of a personalized online atrial fibrillation (AF) prediction method for daily monitoring. First, we apply optimizations to a single-core design to reduce energy, based on patient-specific training models. Second, we explore multi-core and memory banks configuration changes to adapt the computation and storage requirements to the characteristics of each patient. We evaluate our methodology on the Physionet Prediction Challenge (2001) publicly available database, and assess the energy consumption of single-core (ARM Cortex-M3 based) and new ultra-low power multi-core architectures (opensource RISC-V based) for next-generation of wearable platforms. Overall, our exploration at the application level highlights that a parallelization approach for personalized AF in multi-core wearable sensors enables energy savings up to 24 % with respect to single-core sensors. Moreover, including the adaptation of the memory subsystem (size and number of memory banks), in combination with deep sleep energy saving modes, can overall provide total energy savings up to 34 %, depending on the specific patient.

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Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation

Cited by 12 publications

References 33 publications

A Patient-Specific Methodology for Prediction of Paroxysmal Atrial Fibrillation Onset

A Patient-Specific Methodology for Prediction of Paroxysmal Atrial Fibrillation Onset

Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

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