Authors:
Ecem Erin
1
and
Beren Semiz
2
Affiliations:
1
Department of Physics, Bogazici University, Istanbul, Turkey
;
2
Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey
Keyword(s):
Seismocardiogram, Cardiovascular Health Monitoring, Valvular Heart Disease, Biomedical Signal Processing.
Abstract:
Cardiovascular diseases are one of the top causes of mortality, accounting for a sizeable portion of all fatalities globally. Among cardiovascular diseases, valvular heart diseases (VHDs) affect greater number of people and have higher mortality rates. Current VHD assessment methods are cost-inefficient and limited to clinical settings, therefore there is a compelling need for non-invasive and continuous VHD monitoring systems. In this work, a novel framework was proposed to distinguish between aortic stenosis (AS), aortic valve regurgitation (AR), mitral valve stenosis (MS), and mitral valve regurgitation (MR) using tri-axial seismocardiogram (SCG) signals acquired from the mid-sternum. First, seismology domain knowledge was leveraged and applied to SCG signals through ObsPy toolbox for pre-processing. From pre-processed signal segments, spectrogram, wavelet, chromagram, tempogram and zero-crossing-rate features were extracted. Following p-value analysis and variance thresholding, a
multi-label/multi-class classification framework based on gradient boosting trees was developed to distinguish between AS, AR, MS and MR cases. For all four VHDs, the accuracy, precision, recall and f1-score values were above 95%, best performing axis being the dorso-ventral direction. Overall, the results showed that spectral analysis of SCG signals can provide valuable information regarding VHDs and potentially be used in the design of continuous monitoring systems.
(More)