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Method for automatic online delineation of an electrocardiogram (ECG) bio signal, said method comprising the detection of said bio signal through several leads followed by the combination of those multiple acquisitions into a single root-mean-squared (RMS) curve, said RMS curve being then undergoing a real-time single-lead delineation based on a mathematical processing.

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

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.

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.

Technology scaling enables the design of low cost biosignal processing chips suited for emerging wireless body-area sensing applications. Energy consumption severely limits such applications and memories are becoming the energy bottleneck to achieve ultra-low-power operation. When aggressive voltage scaling is used, memory operation becomes unreliable due to the lack of sufficient Static Noise Margin. This paper introduces an approximate biosignal Compressed Sensing approach. We propose a digital architecture featuring a hybrid memory (6T-SRAM/SCMEM cells) designed to control perturbations on specific data structures. Combined with a statistically robust reconstruction algorithm, the system tolerates memory errors and achieves significant energy savings with low area overhead.