RESUMEN

Atrial fibrillation (AF) burden is defined as the percentage of time the patient is in AF rhythm during a certain monitoring period. The accurate AF burden estimation from the long-term electrocardiogram (ECG) recordings provides improved prognostic value compared to the traditional binary AF diagnosis (present or absent) using the snapshot ECG. However, the presence of frequent ectopic beats and different noise levels pose a challenge for precise AF burden estimation. For the first time, we hypothesized that a multi-task deep convolutional neural network (MT-DCNN) could accurately estimate the AF burden from the long-term ambulatory ECG recordings. The model consists of AF detection as a primary task and reconstruction of ECG sequence as an auxiliary task using DCNNs. The auxiliary task regularizes the model to learn robust feature representations for efficient AF detection, thereby aiding accurate AF burden estimation. The MT-DCNN is compared with the state-of-the-art rhythm-based, rhythm- and morphology-based approaches. The models are developed and evaluated on a large database of n=84 patients, totaling t=1,900 h of continuous ECG recordings from the LTAF database. The generalization performance is evaluated on three independent datasets (AFDB, NSRDB and LTNSRDB) of n=48 subjects, totaling t=761 h of continuous ECG recordings. On the LTAF test set, the proposed model exhibits a lesser mean absolute AF burden estimation error of 2.8 % over the rhythm-based and the rhythm- and morphology-based approaches. In addition, the MT-DCNN provides better generalization results on independent test datasets and at different noise levels. The results demonstrate that the MT-DCNN can accurately estimate the AF burden from long-term ECG recordings; thus, it has the potential to be used in remote patient monitoring applications for improved diagnosis, phenotyping, and management of AF.

Asunto(s)

Fibrilación Atrial , Humanos , Fibrilación Atrial/diagnóstico , Electrocardiografía/métodos , Electrocardiografía Ambulatoria , Redes Neurales de la Computación , Factores de Tiempo

RESUMEN

The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and the subtle variations in the pathological ECG characteristics pose challenges in designing reliable automated methods. Existing methods mostly use single deep convolutional neural networks (DCNN) based approaches for arrhythmia classification. Such approaches may not be adequate for effectively representing diverse pathological ECG characteristics. This paper presents a novel way of using an ensemble of multiple DCNN classifiers for effective arrhythmia classification named Deep Multi-Scale Convolutional neural network Ensemble (DMSCE). Specifically, we designed multiple scale-dependent DCNN expert classifiers with different receptive fields to encode the scale-specific pathological ECG characteristics and generate the local predictions. A convolutional gating network is designed to compute the dynamic fusion weights for the experts based on their competencies. These weights are used to aggregate the local predictions and generate final diagnosis decisions. Moreover, a new error function with a correlation penalty is formulated to enable interaction and optimal diversity among experts during the training process. The model is evaluated on the PTBXL-2020 12-lead ECG and the CinC-training2017 single-lead ECG datasets and delivers state-of-the-art performance. Average F1-score of 84.5 % and 88.3 % are obtained for the PTBXL-2020 and the CinC-training2017 datasets, respectively. Impressive performance across various cardiac arrhythmias and the elegant generalization ability for different leads make the method suitable for reliable remote or in-hospital arrhythmia monitoring applications.

Asunto(s)

Arritmias Cardíacas , Redes Neurales de la Computación , Algoritmos , Arritmias Cardíacas/diagnóstico , Electrocardiografía/métodos , Humanos

RESUMEN

In this Letter, the authors propose an efficient and robust method for automatically determining the VT and VF events in the electrocardiogram (ECG) signal. The proposed method consists of: (i) discrete cosine transform (DCT)-based noise suppression; (ii) addition of bipolar sequence of amplitudes with alternating polarity; (iii) zero-crossing rate (ZCR) estimation-based VTVF detection; and (iv) peak-to-peak interval (PPI) feature based VT/VF discrimination. The proposed method is evaluated using 18,000 episodes of different ECG arrhythmias taken from 6 PhysioNet databases. The method achieves an average sensitivity (Se) of 99.61%, specificity (Sp) of 99.96%, and overall accuracy (OA) of 99.92% in detecting VTVF and non-VTVF episodes by using a ZCR feature. Results show that the method achieves a Se of 100%, Sp of 99.70% and OA of 99.85% for discriminating VT from VF episodes using PPI features extracted from the processed signal. The robustness of the method is tested using different kinds of ECG beats and various types of noises including the baseline wanders, powerline interference and muscle artefacts. Results demonstrate that the proposed method with the ZCR, PPI features can achieve significantly better detection rates as compared with the existing methods.