As cardiovascular diseases and arrhythmias rise globally, pacemakers have become a critical therapeutic option for managing cardiac rhythm disorders. Accurate identification of pacemaker implantation sites is essential for personalized pacing therapy and optimal clinical outcomes. While 12-lead electrocardiogram (ECG) signals provide a non-invasive means to infer implantation locations, they are susceptible to noise and morphological variability, posing challenges for high-accuracy localization. To advance data-driven solutions in this domain, we present PILDE, the first publicly available dataset specifically designed for pacemaker implantation site identification, comprising 12-lead ECG recordings from 733 patients across four distinct implantation locations. Based on this dataset, we propose STELLAR, a novel deep learning framework that integrates a Spatio-Temporal Lead-Harmonic Mechanism to model both the temporal dynamics of ECG waveforms and the spatial coherence across leads. Extensive experiments demonstrate that STELLAR outperforms conventional deep models-including CNN, LSTM, and Transformer baselines-on both the PILDE and PTB-XL datasets. Specifically, STELLAR achieves an average accuracy improvement of 10.45 % on PILDE and 14.19 % on PTB-XL, with significant gains in sensitivity and F1-score for minority classes. These results highlight the robustness and precision of STELLAR in automating implantation site identification, offering a promising tool for pre-procedural planning and clinical decision support. The source code and dataset access information will be made publicly available.

Electroencephalography (EEG) analysis plays a crit-ical role in applications such as brain-computer interfaces, epilepsy detection, and cognitive state recognition. However, EEG data are often limited in volume due to high acquisition costs and exhibit complex spatio-temporal coupling across multiple channels. Convolutional Neural Networks (CNNs) have become the predominant approach for EEG signal analysis, owing to their effectiveness in local feature extraction and compatibility with grid-like sensor topologies. Nevertheless, the locality as-sumption inherent in conventional CNN s restricts their ability to capture functional connectivity and dynamic dependencies between spatially distant channels. To address this limitation, we propose NLCTCN, a novel non-local Temporal Convolutional Network that leverages a hierarchical greedy strategy to identify and exploit long-range correlations in multi-channel time series. We further introduce a new fusion scheme, integrated into an end-to-end lightweight CNN architecture to effectively combine these non-local interactions and optimize their configurations for improved predictive performance. Experimental results are presented on 10 real-world EEG datasets. These datasets cover human physiology, cognitive tasks, and clinical applications. The results show that NLCTCN significantly outperforms state-of-the-art methods. On average, NLCTCN achieves an accuracy improvement of 7.5 %. These results validate the effectiveness and superiority of the proposed approach in modeling non-local spatio-temporal dynamics under data-scarce and multi-channel conditions.