AI 中文总结
该研究针对低功耗心电采集，对比泰勒、卡尔曼等四类预测器在饱和感知预测量化中的性能，发现卡尔曼预测器在特定条件下表现最优，为相关ADC优化提供基准参考。
AI 中文摘要
可穿戴心电图（ECG）监护仪需要高能效的模数转换器（ADC），但传统逐次逼近寄存器（SAR）ADC会重复解析变化缓慢的最高有效位。预测量化（PQ）则通过估计下一个采样值并仅对残差进行量化，从而降低所需的转换深度。其主要失效模式是残差饱和，当预测误差超过残差ADC范围时会发生这种情况，且会被不可逆削波。我们在通用10位饱和感知PQ模型下，比较了四种一步超前预测器，残差宽度范围为2至8位。该基准测试包括一阶泰勒外推法、自适应阶预测器、常速卡尔曼滤波器以及两层长短期记忆（LSTM）网络。我们采用开环协议，所有预测器均接收过去的原始采样值，该协议可分离固有预测性能与递归重构误差传播的影响。饱和率（SR）是主要指标，辅以溢出能量比（OER），该指标对每个事件按其平方溢出深度加权。在来自MIT-BIH心律失常数据库记录101的5317个采样片段上，卡尔曼预测器在Br=6时表现最佳，其信噪比（SNR）为30.88 dB，SR为2.16%，OER为12.93%；相比之下，泰勒外推法的SNR为28.39 dB，SR为2.69%，OER为25.29%。自适应阶预测器使用3个寄存器、2个比较器且无乘法器，实现了29.61 dB的SNR和2.44%的SR。LSTM达到29.28 dB的SNR，在该有限数据基准测试中未优于基于模型的预测器。在评估的片段和开环协议下，Br=6在重构保真度与转换深度之间提供了良好平衡。在系统级能量或部署相关结论得出前，还需进行闭环、多受试者及硬件验证。
英文摘要
Wearable electrocardiogram (ECG) monitors require energy-efficient analog-to-digital converters (ADCs), yet conventional successive-approximation-register (SAR) ADCs repeatedly resolve slowly varying most significant bits. Predictive quantization (PQ) instead estimates the next sample and quantizes only the residual, thereby reducing the required conversion depth. Its principal failure mode is residual saturation, which occurs when prediction error exceeds the residual ADC range and is irreversibly clipped. We compared four one-step-ahead predictors under a common 10-bit, saturation-aware PQ model with residual widths from 2 to 8 bits. The benchmark included first-order Taylor extrapolation, an adaptive-order predictor, a constant-velocity Kalman filter, and a two-layer long short-term memory (LSTM) network. We used an open-loop protocol in which all predictors received past original samples. This protocol isolates intrinsic prediction performance from recursive reconstruction-error propagation. Saturation rate (SR) was the primary metric, complemented by overflow energy ratio (OER), which weights each event by its squared overflow depth. On a 5,317-sample excerpt from MIT-BIH Arrhythmia Database Record 101, the Kalman predictor performed best at Br=6. It achieved 30.88 dB SNR, 2.16% SR, and 12.93% OER, compared with 28.39 dB, 2.69%, and 25.29% for Taylor extrapolation. The adaptive-order predictor achieved 29.61 dB SNR and 2.44% SR using three registers, two comparators, and no multiplier. The LSTM reached 29.28 dB SNR and did not outperform the model-based predictors on this limited-data benchmark. Under the evaluated excerpt and open-loop protocol, Br=6 provided a favorable balance between reconstruction fidelity and conversion depth. Closed-loop, multi-subject, and hardware validation are required before system-level energy or deployment claims can be made.
Comments10 pages , 4figures
