A granular resampling method and adaptive speculative mechanism based energy-efficient architecture for multi-class heartbeat classification

This brief presents an energy-efficient design with cascaded structure aiming at multiclass heartbeat classification. SVM-based granular resampling method (GRM) 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 (HAC) to discriminate the remained. The hybrid classifier combined with one-vs-all (OvA) strategy is employed to achieve a multiclass classification model. An adaptive speculative mechanism (ASM) based on the occurrence regularity of ECG 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 40nm CMOS process, the design occupies 0.135mm² area. It consumes 2.60-48.99nJ/classification under 1V voltage supply and 1MHz 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.