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Background: Heart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF., Objectives: In this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF., Methods: Both inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method., Results: A total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39% ± 15%, and median N-terminal pro-B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation., Conclusions: Smartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI; NCT04444583; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors; NCT04290091)., Competing Interests: Funding Support and Author Disclosures This work was funded by a grant from the European Innovation Council (phase 2 funding 954566), Novartis Pharma, and Cardiosignal. Drs Santalahti, Pänkäälä, Kaisti, Dehkordi, Koivisto, and Blomster are employed by CardioSignal. Dr Haddad has received research grants from CardioSignal (REFLECS study), Janssen (computational approaches in pulmonary hypertension), Verily (Project Baseline Health Study), and the National Institutes of Health. Dr Saraste has received grants from the Finnish Foundation for Cardiovascular Research, the Academy of Finland, and Turku University Hospital, and consultancy or speaker fees from Abbott, AstraZeneca, Bayer, Novartis, and Pfizer, all outside of the submitted work. Dr Knuuti has received a personal research grant from the Finnish Foundation for Cardiovascular Research, a research grant from the Finnish State Research Funding, consultancy fees from GE Healthcare and Synektik, and speaker fees from GE Healthcare, Bayer, Lundbeck, Boehringer Ingelheim, Pfizer, Siemens, and Merck, outside of the submitted work. Dr Mahaffey has received research grants or contracts from AHA, Apple, Bayer, California Institute Regenerative Medicine, CSL Behring, Eidos, Ferring, Gilead, Google (Verily), Idorsia, Johnson & Johnson, Luitpold, Novartis, PAC-12, Precordior, and Sanifit; has received consulting or other income from Applied Therapuetics, Bayer, BMS, BridgeBio, CSL Behring, Elsevier, Fosun Pharma, Human, Johnson & Johnson, Moderna, Myokardia, Novartis, Novo Nordisk, Otsuka, Phasebio, Portola, Quidel, and Theravance; and has equity in Human, Medeloop, Precordior, and Regencor. Dr Blomster has received honoraria from Novo Nordisk and Boehringer Ingelheim. Drs Blomster, Koivisto, Pänkäälä, and Santalahti have equity in Precordior/CardioSignal., (Copyright © 2024. Published by Elsevier Inc.)

Background: In the context of monitoring dogs, usually, accelerometers have been used to measure the dog's movement activity. Here, we study another application of the accelerometers (and gyroscopes)-seismocardiography (SCG) and gyrocardiography (GCG)-to monitor the dog's heart. Together, 3-axis SCG and 3-axis GCG constitute of 6-axis mechanocardiography (MCG), which is inbuilt to most modern smartphones. Thus, the objective of this study is to assess the feasibility of using a smartphone-only solution to studying dog's heart., Methods: A clinical trial (CT) was conducted at the University Small Animal Hospital, University of Helsinki, Finland. 14 dogs (3 breeds) including 18 measurements (about one half of all) where the dog's status was such that it was still and not panting were further selected for the heart rate (HR) analysis (each signal with a duration of 1 min). The measurement device in the CT was a custom Holter monitor including synchronized 6-axis MCG and ECG. In addition, 16 dogs (9 breeds, one mixed-breed) were measured at home settings by the dog owners themselves using Sony Xperia Android smartphone sensor to further validate the applicability of the method., Results: The developed algorithm was able to select 10 good-quality signals from the 18 CT measurements, and for 7 of these, the automated algorithm was able to detect HR with deviation below or equal to 5 bpm (compared to ECG). Further visual analysis verified that, for approximately half of the dogs, the signal quality at home environment was sufficient for HR extraction at least in some signal locations, while the motion artifacts due to dog's movements are the main challenges of the method., Conclusion: With improved data analysis techniques for managing noisy measurements, the proposed approach could be useful in home use. The advantage of the method is that it can operate as a stand-alone application without requiring any extra equipment (such as smart collar or ECG patch).