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Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge.
Koivisto, Tero; Lahdenoja, Olli; Hurnanen, Tero; Koskinen, Juho; Jafarian, Kamal; Vasankari, Tuija; Jaakkola, Samuli; Kiviniemi, Tuomas O; Airaksinen, K E Juhani.
Afiliação
  • Koivisto T; Department of Computing, University of Turku, 20500 Turku, Finland.
  • Lahdenoja O; Department of Computing, University of Turku, 20500 Turku, Finland.
  • Hurnanen T; Department of Computing, University of Turku, 20500 Turku, Finland.
  • Koskinen J; Department of Computing, University of Turku, 20500 Turku, Finland.
  • Jafarian K; CardioSignal Inc., 20100 Turku, Finland.
  • Vasankari T; Heart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.
  • Jaakkola S; Heart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.
  • Kiviniemi TO; Heart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.
  • Airaksinen KEJ; Heart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.
Sensors (Basel) ; 22(24)2022 Dec 13.
Article em En | MEDLINE | ID: mdl-36560149
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Alta do Paciente / Insuficiência Cardíaca Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Alta do Paciente / Insuficiência Cardíaca Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article