Heart Sound Processing Model for a Mat-Shaped Device

The development and spread of human-centered products that are increasingly simple and affordable has seen their application areas increase over the years, as well as their effectiveness and reliability. The study of a model based on the interpretation of biosignals is able to provide real-time monitoring of physiological conditions and to offer useful support in both clinical and driving safety fields. Heart rate variability (HRV) analysis is useful in assessing the dynamics of the autonomic nervous system (ANS) and detecting the effects of numerous systemic diseases as well as changes related to the normal daily biological rhythm or to accidental situations of stress or fatigue, as well as comfort or vigilance. The conventional system for the acquisition of the electrocardiographic (ECG) signal uses a methodology that provides for the contact of electrodes with the human skin. This research proposes a detection platform without direct contact with the skin capable of acquiring cardiac signals in real time: through a digital signal processing algorithm able of filtering noise and identifying peaks, heart rate (HR), the interval between beats (RRI) and the characteristic indices of HRV in the time and frequency domain are determined. The parameters were compared with those of a conventional ECG using the Pearson correlation coefficient which produced an index ranging from 0.30 to 0.78 for the tachogram, managing to provide, in the cases less affected by noise, a correspondence in the results of the spectral analysis useful for the evaluation of sympatho-vagal balance.

Automated summary

The development and spread of human-centered products have led to increased applications in various fields. This study focuses on the interpretation of biosignals to provide real-time monitoring of physiological conditions, offering support in clinical and driving safety areas. Heart rate variability analysis helps assess the autonomic nervous system dynamics and detect systemic diseases and changes related to daily biological rhythms, stress, fatigue, comfort, and vigilance. The research proposes a contactless detection platform that can acquire cardiac signals in real time, filtering noise and identifying important parameters.