ArticlePhysical and engineering sciences in medicine2025
Fully automated template matching method for ECG-free heartbeat detection in cardiomechanical signals of healthy and pathological subjects.
Article in Physical and engineering sciences in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep Learning-Based Heartbeat Detection from 3D Seismocardiography for Robust Heart Rate Monitoring.Sensors (Basel, Switzerland) · 2026Article
- A triaxial accelerometer-based approach for motion noise resilient segmentation of seismocardiogram signal.Physical and engineering sciences in medicine · 2026Article
- Monitoring of respiration and cardiorespiratory interactions from multichannel seismocardiography signals.Physical and engineering sciences in medicine · 2026Article
- An Edge AI Approach for Low-Power, Real-Time Atrial Fibrillation Detection on Wearable Devices Based on Heartbeat Intervals.Sensors (Basel, Switzerland) · 2025Article
- Article
- A Forcecardiography dataset with simultaneous SCG, Heart Sounds, ECG, and Respiratory signals.Scientific data · 2025Article
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Authors and funding
5 authors.
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Abstract
Cardiomechanical monitoring techniques record cardiac vibrations on the chest via lightweight electrodeless sensors that allow long-term patient monitoring. Heartbeat detection in cardiomechanical signals is generally achieved by leveraging a simultaneous electrocardiography (ECG) signal to provide a reliable heartbeats localization, which however strongly limits long-term monitoring. A heartbeats localization method based on template matching has demonstrated very high performance in several cardiomechanical signals, with no need for a concurrent ECG recording. However, the reproducibility of that method was limited by the need for manual selection of a heartbeat template from the cardiomechanical signal by a skilled operator. To overcome that limitation, this study presents a fully automated version of the template matching method for ECG-free heartbeat detection, powered by a novel automatic template selection algorithm. The novel method was validated on 256 Seismocardiography (SCG), Gyrocardiography (GCG), and Forcecardiography (FCG) signals, from 150 healthy and pathological subjects. Comparison with all existing methods for ECG-free heartbeat detection was carried out. The method scored sensitivity and positive predictive value (PPV) of 97.8% and 98.6% for SCG, 96.3% and 94.5% for GCG, 99.2% and 99.3% for FCG, on healthy subjects, and of 85% and 95% for both SCG and GCG on pathological subjects. Statistical analyses on inter-beat intervals reported almost unit slopes (R
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