Battery-aware approximate wireless telemetry framework for resilient wearable ECG monitoring under extreme power constraints.

Authors

Mohamed Naeem

Published in

Scientific reports. Volume 16. Issue 1. Jul 19, 2026. Epub Jul 19, 2026.

Abstract

Wearable electrocardiogram (ECG) patches utilizing Bluetooth Low Energy (BLE) face a critical, yet under- characterized, failure mode: as battery state of charge (SoC) depletes, firmware-mandated reductions in RF transmission power elevate the bit error rate (BER) in Rayleigh-fading channels, causing conventional QRS detection to fail during the most clinically important periods of continuous cardiac monitoring. This paper presents the Battery-Aware Approximate Wireless Telemetry (BAWT) framework, a co-designed solution that jointly optimizes a Peukert-corrected Li-ion discharge model, a six-state adaptive RF power controller, and a Rayleigh-fading indoor channel model. At the receiver, the proposed Bayesian Adaptive Feature Estimator (BAFE) fuses a Wiener-optimal morphological bandpass prior with a channel-SNR-derived MMSE-Wiener weight, enabling reliable QRS extraction from severely corrupted bit streams without forward error correction hardware. Validated on the MIT-BIH Arrhythmia and PTB-XL clinical databases against the ANSI/AAMI EC57 standard, BAWT demonstrates substantial performance gains: at the clinically critical 20% SoC operating point, BAFE raises mean QRS sensitivity from 35.1% to 91.9% on MIT-BIH and from 32.9% to 82.8% on PTB-XL, with the majority of individual records satisfying the Se ≥ 95% clinical threshold. The adaptive power controller extends the critical operating window by 355% over fixed full-power operation, and a fully characterized Pareto-optimal frontier enables system designers to navigate the trade-off between battery lifetime extension (up to 144.5%) and clinical QRS detection accuracy across the full SoC range. These results establish a rigorous co-design framework for robust, power-aware wearable cardiac monitoring compliant with ANSI/AAMI EC57. .

PMID:
42472893
Bibliographic data and abstract were imported from PubMed on 20 Jul 2026.

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