Variational autoencoders (VAEs) are increasingly used for electrocardiogram (ECG) representation learning, yet most prior work focuses on short segments or single-lead recordings and rarely evaluates reconstruction, classification, and latent-space behavior jointly on full clinical recordings. This paper presents a modular sequential VAE framework for PTB-XL, a public 12-lead ECG dataset, using standard 10-second resting ECGs that preserve multiple cardiac cycles and clinically relevant morphology. The framework explicitly models time-indexed latent trajectories and compares independent versus temporally structured priors across encoder architectures and objective functions. The results show that temporally structured latent priors improve reconstruction fidelity and latent utilization without degrading multi-label diagnostic performance. Transformer encoders combined with InfoVAE-MMD objectives provide the best balance between reconstruction and representation quality. However, unconditional generation from the learned priors remains physiologically limited. The results highlight the importance of latent dynamics for long multilead ECG modeling and provide guidance for future generative cardiac models.