A Real-Time Compressed Sensing-Based Personal Electrocardiogram Monitoring System Karim Kanoun, Hossein Mamaghanian, Nadia Khaled and David Atienza School of Engineering (STI) Ecole Polytechnique F´ ed´ erale de Lausanne (EPFL) CH-1015 Lausanne, Switzerland; E-mail: {name.surname}@epfl.ch Abstract—Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric mobile- 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 Shimmer TM wearable sensor node and a real- time decoder running on an iPhone (acting as a WBSN coordi- nator). Interestingly, our results show an average CPU usage of less than 5% on the node, and of less than 30% on the iPhone. I. I NTRODUCTION According to the World Health Organization, cardiovascu- lar diseases are the number one cause of death worldwide, responsible for an estimated 17.1 million deaths in 2004 (i.e., 29% of all deaths worldwide) and economic fallout in billions [1]. These increasingly prevalent cardiac diseases are requiring escalating levels of supervision and medical manage- ment, which are contributing to skyrocketing health care costs and, more importantly, are unsustainable for traditional health care infrastructures. Wireless body sensor networks (WBSN) promise to allow inexpensive, continuous and remote health monitoring for next-generation of ambulatory personal tele- cardiology or e-cardiology systems. Outfitting patients with wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to tele-health providers would enable the required personalized, real-time and long- term ambulatory monitoring of chronic patients, its seamless integration with the patient’s medical record and its coordina- tion with nursing/medical support. While the resting electrocardiogram (ECG) monitoring is standard practice in hospitals, its ambulatory counterpart is This research has been partially funded by the Nano-Tera.ch NTF Project BioCS-Node, which is financed by the Swiss Confederation. 978-3-9810801- 7-9/DATE11/ c 2011 EDAA still facing many technical challenges. For instance, the 3- lead ECG is still nowadays recorded on data-logging (Holter) devices during 1 to 5 days of normal daily activities of a patient. These systems, currently commercialized by GE health care, Sorin Group, Mortara and Philips health care, suffer from important limitations: limited autonomy, bulki- ness and no or limited wireless connectivity. Recently, the realization of wireless-enabled ultra-low-power ECG monitors for ambulatory use has been receiving significant industrial and academic interest [2], [3], [4]. Yet, these state-of-the-art ECG monitors fall short in terms of either clinical relevance and/or autonomy figure, primarily because they transmit un- compressed ECG data over power-hungry wireless links. It is today widely acknowledged that the achievement of truly WBSN-enabled personal ECG monitoring systems requires more breakthroughs not only in terms of ultra-low-power read- out electronics and radios, but also and increasingly so, in terms of embedded ECG compression and feature extraction algorithms, and assorted ultra-low-power dedicated digital processors. ECG compression relies on the sparse (and thus compress- ible) nature of the ECG, as it can be approximated by a compact representation in the wavelet domain [5]. Capitalizing on this sparsity, we propose to apply the emerging compressed sensing (CS) approach [6], [7] for a low-complexity, real- time and energy-aware ECG signal compression on WBSN motes. This is motivated by the observation that this new signal acquisition and compression paradigm is well suited for low-power implementations since it dramatically reduces the need for resource-intensive (both processing and storage) DSP operations on the encoder side. CS is a new sensing and processing paradigm, which challenges the traditional analog-to-digital (ADC) conversion based on the Shannon sampling theorem. The latter theorem states that, given a signal of bandwidth Ω, it is sufficient to sample it at 2Ω samples per second (i.e., the Nyquist rate) to ensure faithful representation and reconstruction. For sparse signals such as the ECG, (above) Nyquist-rate sampling produces a large amount of redundant digital samples, which are costly to wirelessly transmit and require to be further compressed using non-linear digital techniques. If one sets course to design energy-aware embedded ECG sensors, it is desirable to reduce the number of acquired ECG samples by