Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers. This paper presents a novel deep representation learning method for the efficient detection of arrhythmic beats. To mitigate the issues associated with the imbalanced data distribution, a novel re-sampling strategy is introduced. Unlike the existing oversampling methods, the proposed technique transforms majority-class samples into minority-class samples with a novel translation loss function. This approach assists the model in learning a more generalized representation of crucially important minority class samples. Moreover, by exploiting an auxiliary feature, an augmented attention module is designed that focuses on the most relevant and target-specific information. We adopted an inter-patient classification paradigm to evaluate the proposed method. The experimental results of this study on the MIT-BIH arrhythmia database clearly indicate that the proposed model with augmented attention mechanism and over-sampling strategy significantly learns a balanced deep representation and improves the classification performance of vital heartbeats.

Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intriguing due to the inter-patient variability of the ECG’s morphological characteristics. The aim of this study was to learn a balanced deep feature representation that incorporates both the short-term and long-term morphological characteristics of ECG beats. For efficient feature extraction, we designed a temporal transition module that uses convolutional layers with different kernel sizes to capture a wide range of morphological patterns. Imbalanced data are a key issue in developing an efficient and generalized model for arrhythmia detection as they cause over-fitting to minority class samples (abnormal beats) of primary interest. To mitigate the imbalanced data issue, we proposed a novel, cost-sensitive loss function that ensures a balanced deep representation of class samples by assigning effective weights to each class. The cost-sensitive loss function dynamically alters class weights for every batch based on class distribution and model performance. The proposed method acquired an overall accuracy of 99.81% for intra-patient classification and 96.36% for the inter-patient classification of heartbeats. The experimental results reveal that the proposed approach learned a balanced representation of ECG beats by mitigating the issue of imbalanced data and achieved an improved classification performance as compared to other studies.

Emotion recognition based on electroencephalography (EEG) signals has many potential applications. However, the development of an emotion recognition model for real-world deployment encounters significant setbacks in learning an invariant representation due to data distribution shift. Some prior studies adopted domain adaptation methods to tackle this issue. However, these studies perform marginally to tackle distribution shift in the source data and neglect to focus on the extraction of domain and target-specific features, which are crucial for domain invariant representation learning. In this study, we propose an assisted domain adaptive representation learning (ADARL) method that learns domain invariant and emotion-specific features for recognizing emotions across different subjects. The proposed architecture introduces novel attention modules to segregate domain-specific and emotion-specific feature maps by exploiting the inter-relationship between different channels and bands. The domain-specific residual feature maps (peripheral features) extracted by the proposed attention mechanism are used to assist the domain discriminator that forces the model to learn a generalized representation of EEG signals. To tackle the inter-subject variability in the source data, we introduce a novel class-aware compactness loss function to improve the domain adaptation transfer efficiency. The experiments conducted on three EEG datasets (SEED, SEED-IV, and DEAP) demonstrate that the ADARL method significantly improves emotion recognition performance and efficiently alleviates the domain shift problem by learning a generalized representation of EEG signals.

Physiological signal analysis has gained a lot of interest in recent years and has been used in a variety of fields including emotion recognition, activity recognition, and health monitoring. However, emotion recognition based on physiological signals is not yet explored entirely using deep learning, and there are still some exciting challenges to be handled. For example, deep representation learning for spatio-temporal feature extraction, the discrimination between adjacent emotions with entangled features, and the imbalanced distribution of data are the most prominent issues in emotion recognition. This work focuses on deep multimodal representation learning of physiological signals to alleviate the aforementioned challenges. We introduce a novel deep learning architecture for emotion classification that effectively extracts spatio-temporal information from physiological signals. We proposed a mutual attention mechanism to extract emotion-specific features for improved classification. To handle the issue of adjacent emotions and imbalanced data, we introduce a dense max-margin loss function based on Gaussian similarity measure. Our experiments on different datasets reveal that the proposed emotion classification methodology effectively learns a balanced deep representation of physiological signals, significantly maximizes the inter-class margin, and reduces intra-class variance to discriminate between different classes of emotions.

Nowadays, data are flooding into online web forums, and it is highly desirable to turn gigantic amount of data into actionable knowledge. Online web forums have become an integral part of the web and are main sources of knowledge. People use this platform to post their questions and get answers from other forum members. Usually, an initial post (question) gets more than one reply posts (answers) that make it difficult for a user to scan all of them for most relevant and quality answer. Thus, how to automatically extract the most relevant answer for a question within a thread is an important issue. In this research, we treat the task of answer extraction as classification problem. A reply post can be classified as relevant, partially relevant, or irrelevant to the initial post. To find the relevancy/similarity of a reply to the question, both lexical and nonlexical features are used. We proposed to use LinearSVC, a variant of support vector machine (SVM), for answer classification. Two selection techniques such as chi-square and univariate are employed to reduce the feature space size. The experimental results showed that LinearSVC classifier outperformed the other state-of-the-art classifiers in the context of classification accuracy for both Ubuntu and TripAdvisor (NYC) discussion forum datasets.