Understanding Atrial Fibrillation: The Signal Processing Contribution, Part II

Mainardi, Luca; Cerutti, S.; Srnmo, Leif

doi:10.2200/s00153ed1v01y200809bme025

Cited by 21 publications

(10 citation statements)

References 190 publications

(16 reference statements)

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“…For the testing purpose, we considered an unbalanced dataset in favor of arrhythmia data (76.5%) to improve the testing generalization capabilities of the NN classifier to recognizing cardiac abnormalities. Lead V1 was chosen for the whole analysis; because it has the largest ratio of atrial to ventricular signal amplitude and therefore can offer more representative characteristics for identifying the common heart diseases [37,38]. The final test set consisted of 884 ECG traces built from 4 heartbeats per individual.…”

Section: Datasetmentioning

confidence: 99%

Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

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Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, are still unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, the propose approach is a useful tool for the detection and diagnosis of cardiac abnormalities.Povzetek: V članku je opisana metoda modularnih nevronskim mrež za prepoznavanje šumnih ECG signalov. Materials and methodsThe NN classification comprises five stages: (i) simulation of ECG signal, (ii) extraction of features that indicate cardiac abnormalities, (iii) computer generation of normal and abnormal heartbeat patterns, (iv) artificial noise injection, and (v) cardiac rhythm classification on simulated and real ECG signals.

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Section: Datasetmentioning

confidence: 99%

Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

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“…Signal processing tools have been developed to extract information about atrial activity, e.g. dominant frequency or degree of organization [31]. The validation of these tools is facilitated by computer modeling, since models can provide a gold standard (e.g.…”

Section: Applicationsmentioning

confidence: 99%

Modeling left and right atrial contributions to the ECG: A dipole-current source approach

Jacquemet

2015

Computers in Biology and Medicine

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“…However, the AA during AF is characterized by low-amplitude fibrillatory waves, called f-waves, that are masked by the QRS complex responsible for the ventricular activity (VA) in each heartbeat. In addition, the AA sometimes presents an amplitude lower than the noise, hampering its analysis [2].…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Noninvasive Atrial Fibrillation Complexity Index For Catheter Ablation

Abdalah

Oliveira

Freitas

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Due to the cost-efficiency of the ECG, the interest in noninvasive techniques to assess atrial fibrillation (AF) electrophysiological complexity is increasingly high. Still, ECG-based methods to measure AF complexity are limited in clinical practice and need estimation of the atrial activity (AA) signal from sufficiently long ECG recordings. The present work proposes an algorithm for tensor decomposition called constrained alternating group lasso (CAGL) as a noninvasive tool to quantify AF complexity. Experiments with a database of 59 ECG recordings from 20 patients suffering from persistent AF show that CAGL is able to both extract the AA and quantify its complexity from very short ECG recordings (1.06 ± 0.20 s). All the patients had undergone step-wise catheter ablation (CA) that ended in procedural AF termination. CAGL is applied on the ECG recording before CA and at each step procedure, measuring the rank of the tensor that provides the AA signal. It is observed that such rank decreases at each step of the CA procedure, showing a less complex AA signal as the ablation is performed. A statistical correlation between AA complexity measured by the new index and AF recurrence after CA is observed. The proposed index is a potential tool to guide CA procedures in real time.

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Understanding Atrial Fibrillation: The Signal Processing Contribution, Part II

Cited by 21 publications

References 190 publications

Noise-tolerant modular neural network system for classifying ECG signal

Noise-tolerant modular neural network system for classifying ECG signal

Modeling left and right atrial contributions to the ECG: A dipole-current source approach

Tensor-Based Noninvasive Atrial Fibrillation Complexity Index For Catheter Ablation

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