Ultra-low power design of wearable cardiac monitoring systems

Abstract

Abstract-This paper presents the system-level architecture of novel ultra-low power wireless body sensor nodes (WBSNs) for real-time cardiac monitoring and analysis, and discusses the main design challenges of this new generation of medical devices. In particular, it highlights first the unsustainable energy cost incurred by the straightforward wireless streaming of raw data to external analysis servers. Then, it introduces the need for new cross-layered design methods (beyond hardware and software boundaries) to enhance the autonomy of WBSNs for ambulatory monitoring. In fact, by embedding more onboard intelligence and exploiting electrocardiogram (ECG) specific knowledge, it is possible to perform real-time compressive sensing, filtering, delineation and classification of heartbeats, while dramatically extending the battery lifetime of cardiac monitoring systems. The paper concludes by showing the results of this new approach to design ultra-low power wearable WBSNs in a real-life platform commercialized by SmartCardia. This wearable system allows a wide range of applications, including multi-lead ECG arrhythmia detection and autonomous sleep monitoring for critical scenarios, such as monitoring of the sleep state of airline pilots.

Heterogeneous Error-Resilient Scheme for Spectral Analysis in Ultra-Low Power Wearable Electrocardiogram Devices

“…tection of most mployed for nonon. Single error ficant words by To simulate this nsidered the two ng 1-bit parity or er read access of the formula as (1) in the protected r access in the age of considered per access in the er access and the also calculated in 5V Supply for emes maximum possible s for running the tient recording of nsumption of the ded system where power management makes the m 2.67s for the processing of a five patient. The variable S represents respect to the baseline configur protected in the Extr_buffer a protected in the DWT_buffer.…”

“…T heterogeneous structure in CACTI, we con corner cases where all data is either employi 6-bit ECC. To derive the dynamic energy p intermediate configurations, we employed described in (1).…”

“…A key element in these devices are the memory components, where a significant amount of energy is consumed [1]. To maximize the energy efficiency, an effective approach is to scale down the supply voltage (Vdd).…”

“…Functionalities of state-of-the-art wearable cardiac sensors are not limited to sensing and (wirelessly) transmitting the acquired data, but they also provide advanced Digital Signal Processing (DSP) capabilities to analyze biosignals on-node and extract clinically-relevant features [1,2,3]. Diverse applications have been proposed, ranging from the automated detection of epileptic seizures [12] to the predictive risk assessment of atrial fibrillations [13].…”

An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery

“…Herein, we focus our investigation on the energy optimization of the Digital Signal Processing (DSP) applications executing on WBSNs. Such routines analyze acquisitions, deriving compact feature sets, which are then transmi ed on the wireless link [7]. ey must be supported within a tight energy envelope, because DSP itself is o en the e ciency bo leneck in body sensor nodes.…”