Inter-patient automated arrhythmia classification: A new approach of weight capsule and sequence to sequence combination

Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination

“…In most recordings, one channel is the Modified Limb Lead II (MLII), obtained by placing electrodes on the chest, which is standard practice for hologram recordings, and the other is usually V1 (sometimes V2, V4, or V5, depending on the subject). Usually, the lead II is used to detect heartbeats in the literature ( Mousavi and Afghah, 2019 ; Li et al, 2022 ; Wu et al, 2022 ; Xu et al, 2022 ; Zhu et al, 2022 ). Similarly, here in all experiments, we have applied ECG lead II.…”

“…To this end, in the past we used the method of combining weight capsules ( Li et al, 2022 ) with Seq2Seq to solve the sample imbalance problem. The weight capsule network is an optimization of the capsule network, which alleviates the saturation of the compression function of the capsule network and the problem of considering the probability of the output vector in the dynamic routing.…”

“…Some scholars have put forward the capsule variable model successively and achieved good results ( Wang and Liu, 2018 ; Choi et al, 2019 ), but their model fails to alleviate the saturation of compression function and consider the probability of the existence of output vector in dynamic routing. To alleviate these problems, we proposed Sigmoid-squash (S-S) compression function and weight capsule (WCapsule) model ( Li et al, 2022 ). The weight capsule can better extract the features of time series data for classification.…”

“…This paper aims to alleviate the problem of sample imbalance in arrhythmia classification and construct an accurate and effective cardiac beat classification method without SMOTE. Inspired by the existing work ( Li et al, 2022 ), we propose a new and effective arrhythmia classification method named MWCapsuleNet, which uses a weight capsule network with MLP to extract the characteristics of heartbeat beats. Our contributions are as follows: (1) We propose an arrhythmia classification method combining MWCapsuleNet and Seq2seq, which not only improves the performance of the algorithm model in arrhythmia classification, but also provides a new scheme for alleviating sample imbalance.…”

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

“…On the other hand, synthetic-based methods generate synthetic ECG data either based on the linear combination of real data samples or the construction of ECG signals by imitating real ECG features. The synthetic minority oversampling technique (SMOTE) and its variants, such as SMOTENN [42], Borderline SMOTE [43], and SVM-SMOTE [42][43][44][45], are often used to extend the minority categories. Just recently, DL techniques have also been used for synthetic data generation, e.g., the convolutional neural style transfer network [46], the generative adversarial network (GAN) [47], and the ACGAN consists of variational auto-encoder model [14].…”