A Granular Resampling Method and Adaptive Speculative Mechanism-Based Energy-Efficient Architecture for Multiclass Heartbeat Classification

This brief presents an energy-efficient design with cascaded structure aiming at multiclass heartbeat classification. support vector machine-based granular resampling method is put forward to obtain a hybrid classifier which includes a low-complexity model (LCM) to identify most easy-to-learn heartbeats and a high-accuracy classifier to discriminate the remained. The hybrid classifier combined with one-versus-all strategy is employed to achieve a multiclass classification model. An adaptive speculative mechanism based on the occurrence regularity of electrocardiogram abnormities is proposed to lower the complexity and computation burden of the multiclass classification model. The corresponding energy-efficient hardware architecture is designed and its architecture optimizations include memory segmentation to reduce energy consumption and time domain reuse to save resources. Implemented in 40-nm CMOS process, the design occupies 0.135 mm 2 area. It consumes 2.60-48.99 nJ/classification under 1-V voltage supply and 1 MHz operating frequency. Results show that the design provides an average prediction speedup by 60.66% and a significant energy dissipation reduction by 55.26% per beat compared with a high-accuracy model without LCMs.