Heart Rate assessment by means of a novel approach applied to signals of different nature

23rd AIVELA (the Italian Association of Laser Velocimetry and non invasive diagnostics) Annual Meeting 2015

Cosoli, G.^1 ; Casacanditella, L.^1 ; Tomasini, E.P.^1 ; Scalise, L.^1

Department of Industrial Engineering and Mathematical Sciences, Università Politecnica Delle Marche, via Brecce Bianche, Ancona

60033, Italy^1

关键词: Biomedical sensors;    Healthy subjects;    Measurement campaign;    Mechanical principles;    Morphological features;    Relevant features;    Standard algorithms;    Traditional approaches;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/778/1/012001/pdf
DOI  :  10.1088/1742-6596/778/1/012001

来源: IOP

【 摘 要 】

Electrocardiographic (ECG) signal presents many clinically relevant features (e.g. QT-interval, that is the duration of the ventricular depolarization). A novel processing technique has been demonstrated to be capable to measure some important characteristics according to the morphology of the waveform. Basing on that, the aim of this work is to propose an improved algorithm and to prove its efficacy in the assessment of the subject's Heart Rate (HR) in comparison to standard algorithms (i.e. Pan & Tompkins). Results obtained in experimentally collected ECG signals for the identification of the main feature (R-peak) are comparable to those obtained with the traditional approach (sensitivity of 99.55% and 99.95%, respectively). Moreover, the use of this algorithm has been broaden to signals coming from different biomedical sensors (based on optical, acoustical and mechanical principles), all related to blood flow, for the computation of HR. In particular, it has been employed to PCG (Phonocardiography), PPG (Photoplethysmography) and VCG (Vibrocardiography), where standard algorithms could not be widely applied. HR results from a measurement campaign on 8 healthy subjects have shown, with respect to ECG, deviations (calculated as 2σ) of ±3.3 bpm, ±2.3 bpm and ±1.5 bpm for PCG, PPG and VCG, respectively. In conclusion, it is possible to state that the adopted algorithm is able to measure HR accurately from different biosignals. Future work will involve the extraction of additional morphological features in order to characterise the waveforms more deeply and to better describe the subject's health status.