ArticleSensors (Basel, Switzerland)2020
Detection and Analysis of Heartbeats in Seismocardiogram Signals.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep Learning-Based Heartbeat Detection from 3D Seismocardiography for Robust Heart Rate Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Heartbeat detection and personal authentication using a 60 GHz Doppler sensor.Frontiers in digital health · 2025Article
- Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting.Sensors (Basel, Switzerland) · 2024Review
- Enhancing visual seismocardiography in noisy environments with adaptive bidirectional filtering for Cardiac Health Monitoring.BMC medical informatics and decision making · 2024Article
- Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms-A Feasibility Study.Bioengineering (Basel, Switzerland) · 2024Article
- Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU.Sensors (Basel, Switzerland) · 2023Article
- An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications.Sensors (Basel, Switzerland) · 2022Review
- Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks.Sensors (Basel, Switzerland) · 2022Article
- Chest-Worn Inertial Sensors: A Survey of Applications and Methods.Sensors (Basel, Switzerland) · 2021Review
- Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review.Sensors (Basel, Switzerland) · 2020Review
- Application of Modern Multi-Sensor Holter in Diagnosis and Treatment.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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).
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