Ultra-low-power ECG front-end design based on compressed sensing

Ultra-low-power design has been a challenging area for design of the sensor front-ends especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power budget and hardware resources are available. Since introduction of Compressed Sensing, there has been a challenge to design CS-based low-power readout devices for different applications and among all for biomedical signals. Till now, different proposed realizations of the digital CS prove the suitability of using CS as an efficient low-power compression technique for compressible biomedical signals. However, these works mainly take advantages of only one aspect of the benefits of the CS. In this type of works, CS is usually used as a very low cost and easy to implement compression technique. This means that we should acquire the signal with traditional limitations on the bandwidth (BW) and later compresses it. However, the main power of the CS, which lies on the efficient data acquisition, remains untouched. Building on our previous work [1], where the suitability of the CS is proven for the compression of the ECG signals, and our investigation on ultra-low-power CS-based A2I devices [2] , here in this paper we propose a fully redesigned complete CS-based “Analog-to-information” (A/I) front-end for ECG signals. Our results show that proposed hybrid design easily outperforms the traditional implementation of CS with more than 11 times fold reduction in power consumption compared to standard implementation of CS. Moreover our design shows a very promising performance specially in high compression ratio

Real-Time Compressed Sensing-Based Electrocardiogram Compression on Energy Constrained Wireless Body Sensors

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric tele-cardiology systems. A WBSNenabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the tele-health provider. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be significantly improved through embedded ECG compression, which reduces airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low complexity energy-aware ECG compression on the state-of-the-art ShimmerTM WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in terms of overall energy efficiency and Shimmer node lifetime extension

Power-Efficient Joint Compressed Sensing of Multi-Lead ECG Signals

Compressed Sensing (CS) is a new acquisition- compression paradigm for low-complexity energy-aware sensing and compression. By merging both sampling and compression, CS is very promising to develop practical ultra-low power read- out systems for wireless bio-signal monitoring devices, where large amounts of sensor data need to be transferred through power-hungry wireless links. Lately CS has been successfully applied for real-time energy- aware single-lead ECG compression on resource-constrained Wireless Body Sensor Network (WBSN) motes. Building on our previous work, in this paper we propose a new and promising approach for joint compression of multi-lead ECG signals, where strong correlations exist between them. This situation that exhibit strong correlations, can be exploited to reduce even further amount of data to be transmitted wirelessly, thus addressing the important challenge of ultra-low-power embedded monitoring of multi-lead ECG signals

A Real-Time Compressed Sensing-Based Personal Electrocardiogram Monitoring System

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric tele-cardiology systems. A WBSN-enabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the tele-health provider. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be significantly improved through embedded ECG compression, which reduces airtime over energy-hungry wireless links. In this paper, we propose a novel real-time energy-aware ECG monitoring system based on the emerging compressed sensing (CS) signal acquisition/compression paradigm for WBSN applications. For the first time, CS is demonstrated as an advantageous real-time and energy-efficient ECG compression technique, with a computationally light ECG encoder on the state-of-the-art ShimmerTM wearable sensor node and a realtime decoder running on an iPhone (acting as a WBSN coordinator). Interestingly, our results show an average CPU usage of less than 5% on the node, and of less than 30% on the iPhone

Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric tele-cardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility and safety. However, state-of-the-art WBSN-enabled electrocardiogram (ECG) monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for ”good” reconstruction quality

Hardware-Software Inexactness in Noise-aware Design of Low-Power Body Sensor Nodes

Wireless Body Sensor Nodes (WBSNs) are miniaturized and ultra-low-power devices, able to acquire and wirelessly trans- mit biosignals such as electrocardiograms (ECG) for extended periods of times and with little discomfort for subjects [1]. Energy efficiency is of paramount importance for WBSNs, because it allows a higher wearability (by requiring a smaller battery) and/or an increased mean time between charges. In this paper, we investigate how noise-aware design choices can be made to minimize energy consumption in WBSNs. Noise is unavoidable in biosignals acquisitions, either due to external factors (in case of ECGs, muscle contractions and respiration of subjects [2]) or to the design of the front- end analog acquisition block. From this observation stems the opportunity to apply inexact strategies such as on-node lossy compression to minimize the bandwidth over the energy- hungry wireless link [3], as long as the output quality of the signal, when reconstructed on the receiver side, is not constrained by the performed compression. To maximize gains, ultra-low-power platforms must be employed to perform the above-mentioned Digital Signal Processing (DSP) techniques. To this end, we propose an under-designed (but extremely efficient) architecture that only guarantees the correctness of operations performed on the most significant data (i.e., data most affecting the final results), while allowing sporadic errors for the less significant data

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

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 pilot

Ultra low power design of hardware efficient CS-Based compression scheme in WBSN

Ultra-low power optimization is a challenging research topic in the de- sign of the sensor front-ends, especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power and hardware resources are available. In this talk, I analyze the potential of the emerging compressed sensing (CS) paradigm for low-complexity and energy-efficient ECG sensing and data compression for storage or transmission, considering both software and hardware aspects. First, I discuss the power efficiency of digital CS, when implemented as a compression technique on a WBSN, illustrating novel optimization techniques to enhance the performance of reconstruction algorithms. These new techniques fully leverage the prior information (beyond simple sparsity) from the underlying signal, improving the compression results for both single lead and joint multi-lead ECG compression. Then, I present novel hardware optimization approaches targeting the design of ultra-low power CS-based analog front-ends in order to make them suitable for WBSN applications. Furthermore, I propose a new hybrid front- end design based on CS that can effectively reduce the power consumption by merging the sensing and compression phases as a single step. Finally, I overview the effects of technology scaling in the design of low- cost processing integrated circuits for CS compression in WBSNs; and ad- vocate the use of a novel robust CS technique to successfully recover the compressed data in presence of unbounded error levels in ultra-low power memories due to aggressive voltage scaling. Moreover, this proposed tech- nique achieves significant energy savings on WBSNs with respect to state-of- the-art designs in nano-scale technologies

Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

Abstract—We have recently quantified and validated the potential of the emerging compressed sensing (CS) paradigm for real-time energy-efficient electrocardiogram (ECG) compression on resource-constrained sensors. In the present work, we investigate applying sparsity models to exploit underlying structural information in recovery algorithms. More specifically, re-visiting well-known sparse recovery algorithms, we propose novel modelbased adaptations for the robust recovery of compressible signals like ECG. Our results show significant performance gains for the recovery algorithms exploiting the underlying sparsity models. I