###### Abstract
There is less research on handling day-to-day variations in ECG-based biometrics compared to more generalized topics such as domain adaptation and generalization techniques. We investigate ensemble-based networks with the aim of improving generalization. Several studies have explored ensemble methods to classify ECG signals. [PERSON] [25] studied an ensemble classification algorithm based on morphological features to detect abnormalities in ventricular and atrial heart functions. The ECG signals were preprocessed, and waves were detected. The morphological features and principal components of wavelet decomposition were extracted from various ECG segments. Several popular classifiers, including the support vector machine (SVM), k-nearest neighbors (KNN), multilayer perceptron (MLP), AdaBoost, and XGBoost, were considered as components. These components were stacked, and XGBoost was used as the meta-classifier. [PERSON] [26] studied a deep learning-based multi-model system for classifying heart arrhythmias. Two classifiers were investigated: a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and a Long Short-Term Memory (LSTM), utilizing RR intervals and high-order statistics. These classifiers were integrated using a feedforward fully connected neural network as a meta-classifier. [PERSON] [27] studied local mean decomposition and an ensemble-boosted tree classifier for atrial fibrillation detection using an electrocardiogram. The electrocardiogram was decomposed using local mean decomposition, and four entropy-based features were extracted. The Kruskal-Wallis algorithm was used to calculate the statistical significance of the features. An ensemble-boosted tree classifier was used for classification. [PERSON] [28] studied an ensemble of multiscale convolutional neural networks called EMCNet, to classify electrocardiograms obtained from wearable devices. The EMCNet included two classifier components: an LSTM model using filtered 1D ECG signals and a VGGNet model using time-frequency spectrograms. The prediction probabilities from each classifier were integrated using a weight matrix and softmax layer, resulting in the final prediction outputs. [PERSON] [29] studied an ensemble learning algorithm for detecting atrial fibrillation using single-lead ECG signals obtained from wearable devices. The method comprised three component learners: one utilizing 1D time-series signals, another employing time-frequency spectra, and the third utilizingPoincare plots. The outputs from these learners were integrated using a weighted matrix optimized through Bayesian optimization. [PERSON] [30] studied an ensemble network for user recognition using electrocardiograms to prevent overfitting and degradation. Several ECG signals acquired from various user states were used in parallel to train several 1D convolutional neural networks. The outputs of these convolutional neural networks were then integrated and fed into another convolutional neural network to obtain the final results. [PERSON] [31] studied an ensemble multilabel ECG signal classification model using several techniques, including binary relevance, a multilabel hierarchical adaptive resonance associative map, a multilabel twin support vector machine, a classifier chain, a label powerset, an embedder, and embedding. The outputs of each classifier were combined using weights calculated using mutual information and a genetic algorithm. [PERSON] [32] studied an ensemble of multiple deep convolutional neural networks for arrhythmia classification. Multiple scale-dependent networks were designed with different receptive fields tailored to scale-specific ECG signals. The outputs of these networks were aggregated using the weights calculated using a convolutional gating network. [PERSON] [33] studied ensemble algorithms for detecting driver drowsiness using multiple sensors, including electroencephalograms and ECGs.