Multi-Lead Wavelet-Based ECG Delineation on a Wearable Embedded Sensor Platform

This work is dedicated to the sensible optimization and porting of a multi-lead (ML) wavelet-transform (WT)-based electrocardiogram (ECG) wave delineator to a state-of-the-art commercial wearable embedded sensor platform with limited processing and storage resources. The original offline algorithm was recently proposed and validated in the literature, as an extension of an earlier well-established single-lead (SL) WT-based ECG delineator. Several ML ECG delineation approaches, including SL selection according to various criteria and lead combination into a single root-mean-squared (RMS) curve, are carefully optimized for real-time operation on a state-of-the-art commercial wearable embedded sensor platform. Furthermore, these ML ECG delineation approaches are contrasted in terms of their delineation accuracy, complexity and memory usage, as well as suitability for ambulatory real-time operation. Finally, the robustness and stability of the ML ECG delineation approaches are benchmarked with respect to a validated SL implementation

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

This work is devoted to the evaluation of multilead digital wavelet transform (DWT)-based electrocardiogram (ECG) wave delineation algorithms, which were optimized and ported to a commercial wearable sensor platform. More specifically, we investigate the use of root-mean squared (RMS)-based multi-lead followed by a single-lead online delineation algorithm, which is based on a state-of-the-art offline singlelead delineator. The algorithmic transformations and software optimizations necessary to enable embedded ECG delineation notwithstanding the limited processing and storage resources of the target platform are described, and the performance of the resulting implementations are analyzed in terms of delineation accuracy, execution time and memory usage. Interestingly, RMS-based multi-lead delineation is shown to perform equivalently to the best single-lead delineation for the 2-lead QT database (QTDB), within a fraction of a sample duration of the Common Standards for Electrocardiography (CSE) committee tolerances. Finally, a comprehensive evaluation of the energy consumption entailed by the considered algorithms is proposed, which allows very relevant insights into the dominant energy-draining functionalities and which suggests suitable design guidelines for longlasting wearable ECG monitoring systems

IcyHeart: Highly integrated ultra-low-power SoC solution for unobtrusive and energy efficient wireless cardiac monitoring

The objective of the IcyHeart project is to investigate and demonstrate a highly integrated and power-efficient microelectronic solution for remote monitoring of a subject’s electrocardiogram (ECG) signals. A complete System-on-a-Chip (SoC) is being developed that embarks on a single chip an ultralow- power signal acquisition front-end with analogue-to-digital converter (ADC) for ECG, a low-power digital signal processor (DSP) and a low-energy radio frequency (RF) transceiver. These features, for the first time, coexist on a single die. Energy efficient signal processing algorithms targeting ECG, and expandable to other bio-signals, are embedded and run on the on-chip DSP. The final IcyHeart product will consist of a tiny PCB embarking IcyHeart SoC and all the necessary discrete components and powering circuit. The outcome of the project is expected to generate high market value for the European SMEs developing novel cardio monitoring products in home and professional environments, and to create high societal impact for several categories of European citizens requiring miniature, comfortable and easy-to-use wireless tele-healthcare solutions

Embedded Real-Time ECG Delineation Methods: a Comparative Evaluation

Wireless sensor nodes (WSNs) have recently evolved to include a fair amount of computational power, so that advanced signal processing algorithms can now be embedded even in this extremely low-power platforms. An increasingly successful field of application of WSNs is tele-healthcare, which enables continous monitoring of subjects, even outside a medical environment. In particular, the design of solutions for automated and remote electrocardiogram (ECG) analysis have attracted considerable research interest in recent years, and different algorithms for delineation of normal and pathological heart rhythms have been proposed. In this paper, some of the most promising techniques for filtering and delinations of ECG signals are explored and comparatively evaluated, describing their implementation on the state-of-the-art IcyHeart WSN. The goal of this paper is to explore the trade-offs implied in the different settings and the impact of design choices for implementing “smart” WSNs dedicated to monitoring ECG bio-signal

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

This paper presents the system-level architecture of novel ultra-low power wireless body sensor nodes (WBSNs) for real-time cardiac monitoring and analysis, and discusses the main design challenges of this new generation of medical devices. In particular, it highlights first the unsustainable energy cost incurred by the straightforward wireless streaming of raw data to external analysis servers. Then, it introduces the need for new cross-layered design methods (beyond hardware and software boundaries) to enhance the autonomy of WBSNs for ambulatory monitoring. In fact, by embedding more onboard intelligence and exploiting electrocardiogram (ECG) specific knowledge, it is possible to perform real-time compressive sensing, filtering, delineation and classification of heartbeats, while dramatically extending the battery lifetime of cardiac monitoring systems. The paper concludes by showing the results of this new approach to design ultra-low power wearable WBSNs in a real-life platform commercialized by SmartCardia. This wearable system allows a wide range of applications, including multi-lead ECG arrhythmia detection and autonomous sleep monitoring for critical scenarios, such as monitoring of the sleep state of airline pilot

A wavelet-based ECG delineation algorithm for 32-bit integer online processing

<p>Abstract</p> <p>Background</p> <p>Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li <it>et al. </it>in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as <it>root mean square </it>(RMS) or floating point algebra, which are computationally demanding.</p> <p>Methods</p> <p>This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT.</p> <p>Results</p> <p>The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods.</p> <p>Conclusions</p> <p>The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.</p

A Wireless Body Sensor Network For Activity Monitoring With Low Transmission Overhead

Activity recognition has been a research field of high interest over the last years, and it finds application in the medical domain, as well as personal healthcare monitoring during daily home- and sports-activities. With the aim of producing minimum discomfort while performing supervision of subjects, miniaturized networks of low-power wireless nodes are typically deployed on the body to gather and transmit physiological data, thus forming a Wireless Body Sensor Network (WBSN). In this work, we propose a WBSN for online activity monitoring, which combines the sensing capabilities of wearable nodes and the high computational resources of modern smartphones. The proposed solution provides different tradeoffs between classification accuracy and energy consumption, thanks to different workloads assigned to the nodes and to the mobile phone in different network configurations. In particular, our WBSN is able to achieve very high activity recognition accuracies (up to 97.2%) on multiple subjects, while significantly reducing the sampling frequency and the volume of transmitted data with respect to other state-of-the art solutions

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

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 (IoT), it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram (ECG) 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