Title A novel seismocardiogram mathematical model for simplified adjustment of adaptive filter /
Authors Uskovas, Gediminas ; Valinevicius, Algimantas ; Zilys, Mindaugas ; Navikas, Dangirutis ; Frivaldsky, Michal ; Prauzek, Michal ; Konecny, Jaromir ; Andriukaitis, Darius
DOI 10.3390/electronics11152444
Full Text Download
Is Part of Electronics.. Basel : MDPI. 2022, vol. 11, iss. 15, art. no. 2444, p. 1-17.. ISSN 2079-9292
Keywords [eng] mathematic model ; cardiovascular system ; seismography ; modeling ; adaptive digital filter ; noninvasive method ; heart rate
Abstract [eng] Nonclinical measurements of a seismocardiogram (SCG) can diagnose cardiovascular disease (CVD) at an early stage, when a critical condition has not been reached, and prevents unplanned hospitalization. However, researchers are restricted when it comes to investigating the benefits of SCG signals for moving patients, because the public database does not contain such SCG signals. The analysis of a mathematical model of the seismocardiogram allows the simulation of the heart with cardiovascular disease. Additionally, the developed mathematical model of SCG does not totally replace the real cardio mechanical vibration of the heart. As a result, a seismocardiogram signal of 60 beats per min (bpm) was generated based on the main values of the main artefacts, their duration and acceleration. The resulting signal was processed by finite impulse response (FIR), infinitive impulse response (IRR), and four adaptive filters to obtain optimal signal processing settings. Meanwhile, the optimal filter settings were used to manage the real SCG signals of slowly moving or resting. Therefore, it is possible to validate measured SCG signals and perform advanced scientific research of seismocardiogram. Furthermore, the proposed mathematical model could enable electronic systems to measure the seismocardiogram with more accurate and reliable signal processing, allowing the extraction of more useful artefacts from the SCG signal during any activity.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2022
CC license CC license description