Embedded real-time ECG delineation methods: A comparative evaluation

Braojos, Rubén; Ansaloni, Giovanni; Atienza, David; Rincon, Francisco

doi:10.1109/bibe.2012.6399715

Cited by 14 publications

(15 citation statements)

References 15 publications

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“…Then, an initial noise filtering is performed to remove artifacts caused, for example, by power lines, electrode parasitic motions, or baseline drift, as recommended by Webster and Huhta [48], [49]. Towards this, erosion and dilation morphological filters developed by Sun et al, as well as Braojos et al are used [50], [51], which can be implemented in an efficient way in energy-constrained wearable systems. After filtering the signal, the ECG delineation (extraction of fiducial points in the signal related to the physiological behavior) is done based on wavelet transforms following the method of Boichat et al [52], relying on the fact that the different waves are made of different frequency components.…”

Section: B Software Structurementioning

confidence: 99%

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

Surrel

Aminifar

Rincon³

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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105

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Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of OSA since current systems are rather bulky, expensive, intrusive, and cannot be used for long-term monitoring in ambulatory settings. In this paper, we propose a wearable, accurate, and energy efficient system for monitoring obstructive sleep apnea on a long-term basis. As an embedded system for Internet of Things, it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram signal. We develop an efficient time-domain analysis to meet the stringent resources constraints of embedded systems to compute the sleep apnea score. Our system, for a publicly available database (PhysioNet Apnea-ECG), has a classification accuracy of up to 88.2% for our new online and patient-specific analysis, which takes the distinct profile of each patient into account. While accurate, our approach is also energy efficient and can achieve a battery lifetime of 46 days for continuous screening of OSA.

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Section: B Software Structurementioning

confidence: 99%

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

Surrel

Aminifar

Rincon³

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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105

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“…Today's Wireless Body Sensor Nodes (WBSNs) embed complex Digital Signal Processing (DSP) routines to extract high-level features from bio-signal acquisitions [5]. These "smart" WBSNs transmit only features (as opposed to samples) through the energy-hungry wireless link, resulting in large efficiency gains, thus enabling longer, less obtrusive and more clinically-relevant acquisitions.…”

Section: Introductionmentioning

confidence: 99%

A multi-core reconfigurable architecture for ultra-low power bio-signal analysis

Duch

Basu

Braojos

et al. 2016

2016 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Abstract-This paper introduces a novel computing architecture devoted to the ultra-low power analysis of multiple biosignals. Its structure comprises several processors interfaced with a shared acceleration resource, implemented as a Coarse Grained Reconfigurable Array (CGRA). The CGRA supports the efficient execution of the computationally intensive kernels present in this application domain, while requiring a low reconfiguration overhead. The run-time behavior of the resulting heterogeneous system is orchestrated by a light-weight hardware mechanism, which concurrently synchronizes processors and regulates access to the reconfigurable accelerator. The architecture achieves speed-ups of up to 11x on different bio-signal processing kernels and system-level energy savings of up to 18.6%, with respect to a multi-core platform, which does not feature CGRA acceleration.

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“…The goal of the classification is to distinguish between normal or pathological beats, in order to trigger a detailed analysis for abnormal beats only, as we mentioned in Section 2. In our case, the general structure shown in Figure 1 is embodied by the system in Figure 6 , where the detailed analysis is obtained by a three-lead morphological delineation (MMD) [ 13 , 25 ]. The considered figures of merit of the classifier are the Normal Discard Rate (NDR) and Abnormal Recognition Rate (ARR).…”

Section: Methodsmentioning

confidence: 99%

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

Braojos

Beretta

Ansaloni

et al. 2014

Sensors

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Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

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Embedded real-time ECG delineation methods: A comparative evaluation

Cited by 14 publications

References 15 publications

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

A multi-core reconfigurable architecture for ultra-low power bio-signal analysis

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

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