Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

Surrel, Gregoire; Teijeiro, Tomás; Aminifar, Amir; Atienza, David; Chevrier, Matthieu

doi:10.1109/mdat.2019.2951126

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Let us consider two other applications, which belong to different scenarios than our case study of the epileptic seizure detection system. The first case study is the obstructive sleep apnea monitoring system proposed in [28]. In this application, the system operates more efficiently locally on the edge sensor, i.e., the first discussed scenario, mainly due to the highly optimized and lightweight algorithms adopted.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, they do not leverage the notion of self-awareness to distribute workload over the fog/cloud infrastructure with higher processing power. In [28], a high-quality and low-power cardiovascular monitoring system is proposed which communicates with cloud using Bluetooth Low Energy (BLE) and LoRA. This work provides a comparison between BLE and LoRa, but does not provide a general formulation for the optimization of energy and latency in different distribution scenarios.…”

Section: A Edge Fog and Cloud In Biomedical Domainmentioning

confidence: 99%

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Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

Forooghifar

Aminifar

Atienza

2019

IEEE Trans. Biomed. Circuits Syst.

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The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applications, while representing a major challenge considering batterypowered wearable technologies. Transferring the complex and energy-consuming computations to fogs or clouds can significantly reduce the energy consumption of wearable devices and result in a longer lifetime of these systems with a single battery charge. In this work, we aim to distribute the complex and energy-consuming machine-learning computations between the edge, fog, and cloud, based on the notion of self-awareness that takes into account the complexity and reliability of the algorithm. We also model and analyze the trade-offs in terms of energy consumption, latency, and performance of different Internet of Things (IoT) solutions. We consider the epileptic seizure detection problem as our realworld case study to demonstrate the importance of our proposed self-aware methodology.

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Section: Resultsmentioning

confidence: 99%

Section: A Edge Fog and Cloud In Biomedical Domainmentioning

confidence: 99%

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

Forooghifar

Aminifar

Atienza

2019

IEEE Trans. Biomed. Circuits Syst.

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“…Thus, seizure detection and forecasting algorithms should ideally be integrated into the same system. All of the above developments require significant improvement in several core aspects of wearable hardware and software technologies, including low power requirements. The energy consumption of high‐frequency sampling processes can be reduced by novel event‐triggered 63 and adapted compressed sensing paradigms 64 . Alternatively, emerging technologies might distribute the complex and energy‐consuming machine‐learning computations among distributed levels of machine learning, combining both smart wearables or edge artificial intelligence and intermediate server levels at home (ie, fog computing).…”

Section: The Future Of Fs Detectionmentioning

confidence: 99%

Noninvasive detection of focal seizures in ambulatory patients

et al. 2020

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Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions.

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“…has not undergone such a significant evolution. Event-based sampling has demonstrated a large potential for energy savings in various wearable-based applications [7], [8], but in these cases the design of the acquisition component is highly dependent on the target application, making it less modular than a classical ADC.…”

Section: Introductionmentioning

confidence: 99%

An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors

Zanoli¹,

Ponzina²,

Teijeiro³

et al. 2021

Preprint

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Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and storage. However, almost all state-of-the-art systems are still built upon the classical Nyquist-based periodic signal acquisition. In this work, we design and validate the Polygonal Approximation Sampler (PAS), a novel circuit to implement a generalpurpose event-based sampler using a polygonal approximation algorithm as the underlying sampling trigger. The circuit can be dynamically reconfigured to produce a coarse or a detailed reconstruction of the analog input, by adjusting the error threshold of the approximation. The proposed circuit is designed at the Register Transfer Level and processes each input sample received from the ADC in a single clock cycle. The PAS has been tested with three different types of archetypal signals captured by wearable devices (electrocardiogram, accelerometer and respiration data) and compared with a standard periodic ADC. These tests show that single-channel signals, with slow variations and constant segments (like the used single-lead ECG and the respiration signals) take great advantage from the used sampling technique, reducing the amount of data used up to 99% without significant performance degradation. At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation. These results open the door to new types of wearable sensors with reduced size and higher battery lifetime.

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Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

Cited by 6 publications

References 12 publications

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

Noninvasive detection of focal seizures in ambulatory patients

An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors

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