A feasibility study evaluating seismocardiography for the detection of heart failure
5 affiliations
- 1Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
- 2Department of Clinical Medicine, Aalborg University, AAU, Aalborg, Denmark
- 3Department of Health Science and Technology, Aalborg University AAU, Aalborg, Denmark
- 4Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
- 5Department of Cardiology, Odense University Hospital, Odense, Denmark
Abstract
The study aimed to develop a seismocardiograph (SCG)- based algorithm and assess its diagnostic performance in the detection of heart failure (HF). A total of 218 subjects were included: 198 with suspected HF and 20 with known HF with reduced ejection fraction (HFrEF) were included for testing only. Assessments were conducted using SCG, N-terminal pro b-type natriuretic peptide (NT-proBNP), electrocardiogram, NYHA classification and echocardiography. SCG-based algorithms, “AnyHF score” were developed to identify all subtypes of HF and “HFrEF-score” to identify HFrEF. Diagnostic accuracy was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver-operating characteristic curve (AUC-ROC). The AnyHF score demonstrated an AUC of 82%, sensitivity of 90.9%, specificity of 43.8%, NPV of 87.5% and PPV of 52.6% in detecting HF versus no HF. A balanced comparative analysis was performed between NT-proBNP and the HFrEF-score for detecting HFrEF versus no HF. NT-proBNP demonstrated an AUC of 94.7%, sensitivity 94.1%, specificity 68.8%, NPV 99%, and PPV 27.1%. The HFrEF-score showed an AUC of 92.9%, sensitivity 88.2%, specificity 92% (p < 0.001), NPV 98.4%, and PPV 57.7% (p = 0.007). The SCG-scores also categorized 71 patients (51%) from the no HF group, referred on suspicion of HF, as minimal risk of HF. This study suggests that SCG has potential to aid in the diagnostic process of HF. The SCG-based algorithms “AnyHF-score” and “HFrEF-score” demonstrated high diagnostic performance in identifying HF. Author summary: In this study, we explored a new way to detect heart failure using a small device that can measure the vibrations of the chest caused by the beating heart. Heart failure is a common condition in which the heart cannot pump blood effectively, and it can be difficult to diagnose early with standard tests. We developed a computer-based method that analyzes these chest vibrations to identify signs of heart failure and to distinguish between different types of condition.
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BibTeX
@article{agam2026feasibility,
title = {{A feasibility study evaluating seismocardiography for the detection of heart failure}},
author = {Ahmad Agam and Emil Korsgaard and Troels Yding Haugstrup and Kasper Janus Grønn Emerek and Maria Weinkouff Pedersen and Massar Omar and Jacob Eifer Møller and Kristian Kragholm and Samuel Emil Schmidt and Peter Søgaard},
journal = {PLOS Digital Health},
year = {2026},
month = sep,
doi = {10.1371/journal.pdig.0001685},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001685},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A feasibility study evaluating seismocardiography for the detection of heart failure
AU - Ahmad Agam
AU - Emil Korsgaard
AU - Troels Yding Haugstrup
AU - Kasper Janus Grønn Emerek
AU - Maria Weinkouff Pedersen
AU - Massar Omar
AU - Jacob Eifer Møller
AU - Kristian Kragholm
AU - Samuel Emil Schmidt
AU - Peter Søgaard
PY - 2026
DA - 2026/09/10
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001685
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001685
AB - The study aimed to develop a seismocardiograph (SCG)- based algorithm and assess its diagnostic performance in the detection of heart failure (HF). A total of 218 subjects were included: 198 with suspected HF and 20 with known HF with reduced ejection fraction (HFrEF) were included for testing only. Assessments were conducted using SCG, N-terminal pro b-type natriuretic peptide (NT-proBNP), electrocardiogram, NYHA classification and echocardiography. SCG-based algorithms, “AnyHF score” were developed to identify all subtypes of HF and “HFrEF-score” to identify HFrEF. Diagnostic accuracy was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver-operating characteristic curve (AUC-ROC). The AnyHF score demonstrated an AUC of 82%, sensitivity of 90.9%, specificity of 43.8%, NPV of 87.5% and PPV of 52.6% in detecting HF versus no HF. A balanced comparative analysis was performed between NT-proBNP and the HFrEF-score for detecting HFrEF versus no HF. NT-proBNP demonstrated an AUC of 94.7%, sensitivity 94.1%, specificity 68.8%, NPV 99%, and PPV 27.1%. The HFrEF-score showed an AUC of 92.9%, sensitivity 88.2%, specificity 92% (p < 0.001), NPV 98.4%, and PPV 57.7% (p = 0.007). The SCG-scores also categorized 71 patients (51%) from the no HF group, referred on suspicion of HF, as minimal risk of HF. This study suggests that SCG has potential to aid in the diagnostic process of HF. The SCG-based algorithms “AnyHF-score” and “HFrEF-score” demonstrated high diagnostic performance in identifying HF. Author summary: In this study, we explored a new way to detect heart failure using a small device that can measure the vibrations of the chest caused by the beating heart. Heart failure is a common condition in which the heart cannot pump blood effectively, and it can be difficult to diagnose early with standard tests. We developed a computer-based method that analyzes these chest vibrations to identify signs of heart failure and to distinguish between different types of condition.
SN - 2767-3170
ER -