Multivariate time series classification (MTSC) plays a critical role in a wide range of real-world applications, such as healthcare, finance, and industrial monitoring. This paper proposes a triple-fusion network (TriFusNet), a novel convolutional network designed to address the challenges of MTSC. TriFusNet employs a specialized architecture that captures both variable-specific features and features shared across variables through the parallel use of standard, depth-wise, and shared-kernel convolutions. A hierarchical triple-fusion strategy is introduced to enhance representation learning across three stages: input-level fusion transforms raw variables, intermediate-level fusion integrates heterogeneous features, and output-level fusion improves decision robustness. Extensive experiments on 26 benchmark datasets show that TriFusNet outperforms 15 competitive baselines, achieving the best average rank (4.3077) with a Win/Draw/Loss of 5/4/17. The effectiveness of its architectural design and parameter settings is empirically validated, and a qualitative theoretical discussion is conducted to support the proposed fusion strategy. These results highlight TriFusNet's strong potential for real-world applications involving complex and high-dimensional time series data.

Nowadays, applying deep learning (DL) to electrocardiogram (ECG) analysis has become a significant topic in intelligent healthcare. DL models heavily rely on large-scale labeled ECGs in supervised learning, while labeling ECGs is a costly and time-consuming process. This article proposes a simple self-supervised learning (SSL) method to pretrain models using unlabeled ECGs, improving model performances in a low-data regime. It is termed direct lead assignment (DLA). In pretraining, DLA employs multilead and single-lead encoders to interact between global and lead-specific representations. The pretrained encoders can constitute scalable models, which can be deployed on Internet of Medical Things (IoMT) devices for ECG monitoring with different leads. According to the experiments, DLA outperforms existing SSL methods for ECGs and reduces the reliance on labels by$4\times $–$8\times $. In other words, DLA can make the model obtain better results when labeled data are scarce than the one trained from scratch, as the pretraining helps the model recognize critical ECG patterns in a low-data regime. For IoMT applications, the models are deployed on a Raspberry Pi 2 W with an ARM Cortex-A53 processor. It can run the models in real time. The maximum running time is about 388 ms/10-s record. Thus, DLA shows excellent potential for automatic ECG analysis based on IoMT, aiding cardiologists in diagnosing cardiovascular diseases.