ArticleSensors (Basel, Switzerland)2022
Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- Article
- Non-Invasive Wearable Technology to Predict Heart Failure Decompensation.Journal of clinical medicine · 2025Review
- Wearable Single-Electrode Capacitive Sensor with Large Penetration Depth for Intelligent Deep Tissue and Hemorrhage Monitoring.Advanced sensor research · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- A scoping review on advancements in noninvasive wearable technology for heart failure management.NPJ digital medicine · 2024Article
- Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level.IEEE open journal of engineering in medicine and biology · 2024Article
- Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Heart failure (HF) is a disease related to impaired performance of the heart and is a significant cause of mortality and treatment costs in the world. During its progression, HF causes worsening (decompensation) periods which generally require hospital care. In order to reduce the suffering of the patients and the treatment cost, avoiding unnecessary hospital visits is essential, as hospitalization can be prevented by medication. We have developed a data-collection device that includes a high-quality 3-axis accelerometer and 3-axis gyroscope and a single-lead ECG. This allows gathering ECG synchronized data utilizing seismo- and gyrocardiography (SCG, GCG, jointly mechanocardiography, MCG) and comparing the signals of HF patients in acute decompensation state (hospital admission) and compensated condition (hospital discharge). In the MECHANO-HF study, we gathered data from 20 patients, who each had admission and discharge measurements. In order to avoid overfitting, we used only features developed beforehand and selected features that were not outliers. As a result, we found three important signs indicating the worsening of the disease: an increase in signal RMS (root-mean-square) strength (across SCG and GCG), an increase in the strength of the third heart sound (S3), and a decrease in signal stability around the first heart sound (S1). The best individual feature (S3) alone was able to separate the recordings, giving 85.0% accuracy and 90.9% accuracy regarding all signals and signals with sinus rhythm only, respectively. These observations pave the way to implement solutions for patient self-screening of the HF using serial measurements.
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Registered trials
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