Abstract

This article presents an unsupervised, automated procedure for the analysis of Seismocardiogram (SCG) signals. SCG is a measure of chest vibrations, induced by the mechanical activity of the heart, which allows extracting relevant parameters, including heart rate (HR) and HR variability (HRV). An initial self-calibration is performed, solely based on the SCG traces, yielding a suitable heartbeat template (personalized for each subject). Then, beat detection and timing annotation are performed in two steps: at first, candidate beats are identified and validated, by means of suitably defined detection signals; then, precise timing annotation is achieved by best aligning such candidate beats to the previously extracted template. The algorithm has been validated on two separate data sets, featuring different acquisition setups: the first one is the publicly available Combined measurement of ECG, Breathing and Seismocardiogram (CEBS) database, reporting SCG signals from the subjects lying in supine position, whereas the second one was acquired using a custom setup, involving the sitting subjects. Results show good sensitivity and precision scores (98.5% and 98.6% for the CEBS database, and 99.1% and 97.9% for the Custom one, respectively). In addition, comparison with electrocardiogram (ECG) gold-standard is given, showing good agreement between the beat-to-beat intervals computed from SCG and the ECG gold-standard: on average, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scores of 99.3% and 98.4% are achieved on the CEBS and Custom data sets, respectively. Furthermore, a low rms error is achieved on the CEBS and Custom data sets, amounting to 4.6 and 6.2 ms, respectively (i.e., 2.3 T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> and 3.1 T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , where T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> is the sampling period): such results are well compared with related literature. Validation on two different data sets indicates the robustness of the proposed methodology.

Highlights

  • A DVANCEMENTS in the information and communication technology (ICT) and the Internet of Things (IoT) technology are driving innovation in many different fields, by allowing more devices to interact in a seamless way.Active assisted living (AAL) [1] and, in general, smartManuscript received September 3, 2019; revised December 19, 2019; accepted January 3, 2020

  • It is worth remarking here that the purpose of the present method is not to diagnose heart diseases, for which specific medical devices are better suited; instead, the aimed target includes relatively healthy senior persons, whose activities are to be continuously monitored in the context of smart AAL systems: precise heart rate (HR) and HR variability (HRV) indicators can effectively enhance the insightfulness of a behavioral analysis framework

  • isovolumic movement (IM)/aortic valve opening (AO) complexes in the SCG are selected as target for the detection and annotation phases: SCG-derived beat-to-beat intervals are defined

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Summary

Introduction

A DVANCEMENTS in the information and communication technology (ICT) and the Internet of Things (IoT) technology are driving innovation in many different fields, by allowing more devices to interact in a seamless way.Active assisted living (AAL) [1] and, in general, smartManuscript received September 3, 2019; revised December 19, 2019; accepted January 3, 2020. With IoT devices making their way into the AAL realm, a wealth of information becomes available, covering different aspects. Indirect information about a person’s general wellbeing was extracted from the presence and motion sensors in home environments [3]: anomalies or deviations (either sharp or abrupt) in habits and routines can be quantitatively assessed, potentially suggesting the underlying health issues. Such an approach is, for instance, being adopted to complement the medical protocols for persons recovering from stroke events in [4]. Indirect assessment of health and wellbeing status by means of behavioral analysis fosters care continuity

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