Cargando…

Detection and Analysis of Heartbeats in Seismocardiogram Signals

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Re...

Descripción completa

Detalles Bibliográficos
Autores principales: Mora, Niccolò, Cocconcelli, Federico, Matrella, Guido, Ciampolini, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146295/
https://www.ncbi.nlm.nih.gov/pubmed/32192162
http://dx.doi.org/10.3390/s20061670
_version_ 1783520168147681280
author Mora, Niccolò
Cocconcelli, Federico
Matrella, Guido
Ciampolini, Paolo
author_facet Mora, Niccolò
Cocconcelli, Federico
Matrella, Guido
Ciampolini, Paolo
author_sort Mora, Niccolò
collection PubMed
description This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space).
format Online
Article
Text
id pubmed-7146295
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-71462952020-04-15 Detection and Analysis of Heartbeats in Seismocardiogram Signals Mora, Niccolò Cocconcelli, Federico Matrella, Guido Ciampolini, Paolo Sensors (Basel) Article This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space). MDPI 2020-03-17 /pmc/articles/PMC7146295/ /pubmed/32192162 http://dx.doi.org/10.3390/s20061670 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mora, Niccolò
Cocconcelli, Federico
Matrella, Guido
Ciampolini, Paolo
Detection and Analysis of Heartbeats in Seismocardiogram Signals
title Detection and Analysis of Heartbeats in Seismocardiogram Signals
title_full Detection and Analysis of Heartbeats in Seismocardiogram Signals
title_fullStr Detection and Analysis of Heartbeats in Seismocardiogram Signals
title_full_unstemmed Detection and Analysis of Heartbeats in Seismocardiogram Signals
title_short Detection and Analysis of Heartbeats in Seismocardiogram Signals
title_sort detection and analysis of heartbeats in seismocardiogram signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146295/
https://www.ncbi.nlm.nih.gov/pubmed/32192162
http://dx.doi.org/10.3390/s20061670
work_keys_str_mv AT moraniccolo detectionandanalysisofheartbeatsinseismocardiogramsignals
AT cocconcellifederico detectionandanalysisofheartbeatsinseismocardiogramsignals
AT matrellaguido detectionandanalysisofheartbeatsinseismocardiogramsignals
AT ciampolinipaolo detectionandanalysisofheartbeatsinseismocardiogramsignals