# Saturation-Aware Predictive Quantization for Low-Power ECG Acquisition: A Benchmark of Taylor, Adaptive-Order, Kalman, and LSTM Predictors

arXiv: https://arxiv.org/abs/2608.00992 · HTML: https://mlsanity.com/paper/2608.00992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising](https://mlsanity.com/paper/2210.12807/) (2022)
- [Fully Digital Second-order Level-crossing Sampling ADC for Data Saving in Sensing Sparse Signals](https://mlsanity.com/paper/2211.09848/) (2022)
- [Dynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing](https://mlsanity.com/paper/2211.09901/) (2022)
- [Time-Series Analysis on Edge-AI Hardware for Healthcare Monitoring](https://mlsanity.com/paper/2504.15178/) (2025)

## Abstract (arXiv)

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.