Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes

Rincon, Francisco; Recas, J.; Khaled, N.; Atienza, David

doi:10.1109/titb.2011.2163943

Cited by 95 publications

(97 citation statements)

References 19 publications

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“…The following paragraphs present each of these applications. [8]. In our implementation, the DWT takes as input a vector of ECG samples and performs on it several scales of low-pass and high-pass filtering.…”

Section: Biomedical Applications Case Studiesmentioning

confidence: 99%

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Energy vs. Reliability Trade-offs Exploration in Biomedical Ultra-Low Power Devices

Duch

Valle

Ganapathy

et al. 2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-State-of-the-art wearable devices such as embedded biomedical monitoring systems apply voltage scaling to lower as much as possible their energy consumption and achieve longer battery lifetimes. While embedded memories often rely on Error Correction Codes (ECC) for error protection, in this paper we explore how the characteristics of biomedical applications can be exploited to develop new techniques with lower power overhead. We then introduce the Dynamic eRror compEnsation And Masking (DREAM) technique, that provides partial memory protection with less area and power overheads than ECC. Different tradeoffs between the error correction ability of the techniques and their energy consumption are examined to conclude that, when properly applied, DREAM consumes 21% less energy than a traditional ECC with Single Error Correction and Double Error Detection (SEC/DED) capabilities.

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Section: Biomedical Applications Case Studiesmentioning

confidence: 99%

“…Typically used to perform an analysis of ECG signals to detect heartbeat fiducial points [8]. Our versions of such applications use the DWT algorithm presented in Section II-1 to generate, as output, the list of P, Q, R, S and T heartbeat points found.…”

Section: ) Wavelet Delineationmentioning

confidence: 99%

Energy vs. Reliability Trade-offs Exploration in Biomedical Ultra-Low Power Devices

Duch

Valle

Ganapathy

et al. 2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

View full text Add to dashboard Cite

show abstract

“…In addition to acquisition and wireless transmission of bio-signals, state-of-the-art WBSNs embed advanced processing applications, able to automatically retrieve diagnostic information from the acquired data [5]. By employing on-node processing, only the analysis results (as opposed to acquired data) has to be transmitted on the power-hungry wireless link, substantially increasing the energy efficiency [6], which is a crucial factor for WBSNs. …”

Section: Previous Workmentioning

confidence: 99%

“…The ECG signal is separated from the ICG and processed on device to remove muscular noise. Several different noise filtering methods have been implemented in our earlier work [6]. The signal is further processed to delineate the different P, QRS and T waveforms.…”

Section: Electrocardiogrammentioning

confidence: 99%

A wearable device for physical and emotional health monitoring

Murali¹,

Rincon²,

Atienza

2015

2015 Computing in Cardiology Conference (CinC)

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Personal health monitoring systems are emerging as promising solutions to develop ultra-small, portable devices that can continuously monitor and process several vital body parameters. In this work, we present a wearable device for physical and emotional health monitoring. The device obtains user's key physiological signals: ECG, respiration, Impedance Cardiogram (ICG), blood pressure and skin conductance and derives the user's emotion states as well. We have developed embedded algorithms that process the bio-signals in real-time to detect any abnormalities (cardiac arrhythmias and morphology changes) in the ECG and to detect key parameters (such as the Pre-Ejection Period and fluid status level) from the ICG. We present a novel method to detect continuous beat-by-beat blood pressure from the ECG and ICG signals, as well as a realtime embedded emotion classifier that computes the emotion levels of the user. Emotions are classified according to their attractiveness (positive valence) or their averseness (negative valence) in the horizontal valence dimension. The excitement level induced by the emotions is represented by high to low positions in the vertical arousal dimension of the valence-arousal space. The signals are measured either intermittently by touching the metal electrodes on the device (for point-of-care testing) or continuously, using a chest strap for long term monitoring. The processed data from device is sent to a mobile phone using a Bluetooth Low Energy protocol. Our results show that the device can monitor the signals continuously, providing accurate detection of the motion state, for over 72 hours on a single battery charge.

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“…We also acknowledge the support of the Swiss NSF under the project number PP002-119052. 978-3-9810801-8-6/DATE12/ c 2012 EDAA or limited in overall autonomy due to their limited energy efficiency for advanced biopotentials processing [5].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-core architecture design for ultra-low-power wearable health monitoring systems

Dogan¹,

Constantin²,

Ruggiero³

et al. 2012

2012 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-Personal health monitoring systems can offer a costeffective solution for human healthcare. To extend the lifetime of health monitoring systems, we propose a near-threshold ultralow-power multi-core architecture featuring low-power cores, yet capable of executing biomedical applications, with multiple instruction and data memories, tightly coupled through flexible crossbar interconnects. This architecture also includes broadcasting mechanisms for the data and instruction memories to optimize system energy consumption by tailoring memory sharing to the target application. Moreover, the architecture enables power gating of the unused memory banks to lower leakage power. Our experimental results show that compared to the state-of-the-art, the proposed architecture achieves 39.5% power savings at high workload requirements (637 MOps/s), and 38.8% savings at low workload requirements (5 kOps/s), whereby leakage power consumption dominates.

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Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes

Cited by 95 publications

References 19 publications

Energy vs. Reliability Trade-offs Exploration in Biomedical Ultra-Low Power Devices

Energy vs. Reliability Trade-offs Exploration in Biomedical Ultra-Low Power Devices

A wearable device for physical and emotional health monitoring

Multi-core architecture design for ultra-low-power wearable health monitoring systems

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