Deployment-Oriented Quantization of an ECGWavePuzzle-Based Personalization Pipeline for Arrhythmia Classification - Silva, Guilherme; Negrão, Arthur; Silva, Pedro; Luz, Eduardo (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21746, 2026)
This paper evaluates INT8 quantization and mixed-precision execution in an ECGWavePuzzle-based personalization pipeline for arrhythmia classification. The SSL framework follows prior work, and the contribution is a deployment-oriented analysis under reduced numerical precision. On MITBIH with ANSI/AAMI patient-wise evaluation, head int8 achieves 0.9013 ± 0.0198 Accuracy and 0.5364± 0.0416 Macro-F1 on H200. FP16 also improves Macro-F1 over BF16 on H200 (0.5252 vs. 0.4969) with slightly lower runtime (2920 s vs. 3003 s). However, Jetson Nano execution remains expensive, requiring about 60048 s. The results show that selective quantization is feasible, but online personalization remains the main bottleneck for embedded deployment.