BioCS-Node: Enabling Ultra-Low-Power Ambulatory Monitoring of Cardiac and Neurological Bioelectrical Signals Using Compressed Sensing
Our modern society is today threatened by an incipient healthcare delivery crisis caused by current demographic and lifestyle trends. On the one hand the world�s population is aging quickly, resulting in an increased prevalence of cardiac and neurological disorders. On the other hand, our busy lifestyles leave little time and motivation for fitness, healthy diet management and mental wellness, and are fuelling the rise of the number of people unsuspectingly developing, or living with, chronic cardiovascular and neurological conditions for decades.
As a matter of fact, according to the World Health Organization, cardiovascular diseases (CVD) 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 an economic fallout in the billions. Moreover, neurological diseases including strokes, neuromotor ailments and sleep disorders affect up to 1 billion people globally, and are a significant cause of morbidity and mortality (i.e., 12% of all deaths globally). These increasingly prevalent cardiac and neurological diseases require escalating levels of supervision and medical management, which are contributing to skyrocketing healthcare costs and, more importantly, are unsustainable for traditional healthcare infrastructures. Wireless body sensor network (WBSN) technologies promise to offer large-scale and cost-effective solutions to this problem. Outfitting patients with wearable, miniaturized and wireless sensors able to measure, pre-process and wirelessly report cardiac and neurological signals to telehealth providers would enable the required personalized, long-term and real-time remote monitoring of chronic patients, its seamless integration with the patient�s medical record and its coordination with nursing/medical support.
To successfully deploy WBSNs able to perform long-term, remote and clinically relevant monitoring of chronic patients in free-living conditions, it is critical that sensor devices become vanishingly small and autonomous, while retaining their embedded intelligence and wireless capabilities. Devices in use today operate on Li-on battery that provides about 1 Watt-hour of energy, and were shown to exhibit, for instance, an autonomy of less than a day for single-lead cardiac bioelectrical signal (i.e., electrocardiogram or ECG) sensing and wireless streaming. This ridiculously low autonomy figure is due to the transmission of uncompressed ECG data over power-hungry wireless links. The autonomy figures would be even more compelling for multi-lead ECG and electroencephalogram (EEG) monitoring. Clearly, significant research contributions remain to be made in terms of ultra-low-power embedded compression of ECG and EEG signals and ultra-low-power wireless WBSN connectivity. Within this project, we propose a novel and promising approach to tackle the former challenge. More specifically, we devise low-complexity, yet, powerful multi-lead cardiac and neurological bioelectrical compression techniques and design their supporting ultra-low-power sensor digital processing platform.
Capitalizing on the largely sparse nature of ECG and EEG, we propose to apply the emerging approach to joint sensing and compression for this class of signals, so-called compressed sensing (CS), which promises significant compression ratios while using computationally light linear encoders. This approach is particularly attractive and promising for our target ultra-low-power WBSN-based monitoring systems because the sensor node can very efficiently jointly compress the acquired ECG/EEG signals through a small number of linear signal-independent measurements while preserving their underlying information; only this small number of measurements will be wirelessly transmitted to the remote telehealth center, where the full multi-lead records can be accurately reconstructed using complex non-linear decoding. More importantly, we propose to design a new sensor embedded platform that effectively implements the compressed sensing of cardiac and neurological bioelectrical signals. If successful, this project could lead to a new way of thinking and designing wireless sensing platforms, and would be a the first to demonstrate the ultra-low-power benefits of compressed sensing for cardiac and neurological bioelectrical signals.
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