IEEE J Biomed Health Inform - Automatic annotation of seismocardiogram with high-frequency precordial accelerations.

Tópicos

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Resumo

Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis applications. However, annotation of these peaks is not a trivial task and complicated in some subjects. In this paper, an automated method is proposed to annotate these peaks. The high-frequency accelerations obtained from the same accelerometer, used to record SCG with, were used to facilitate the annotation of the SCG. Algorithms were developed for detection of isovolumic moment (IM) and aortic valve closure (AC) points of SCG. Four different envelope calculation methods were used: cardiac sound characteristic waveform (CSCW), Shannon, absolute, and Hilbert. The algorithms were evaluated based on a dataset including 18 subjects undergoing lower body negative pressure and were further tested with another dataset, which included 67 subjects. These datasets had been previously manually annotated. The algorithm based on CSCW envelope calculation produced the highest detection accuracy for both IM and AC. The overall CSCW algorithm detection accuracy for the test dataset was 98.7% and 99.1% for the IM and AC points, respectively.

Resumo Limpo

seismocardiogram scg lowfrequ vibrat signal record chest use acceleromet peak dorsoventr sternal scg correspond specif cardiac event prior research work shown potenti extract peak various type monitor diagnosi applic howev annot peak trivial task complic subject paper autom method propos annot peak highfrequ acceler obtain acceleromet use record scg use facilit annot scg algorithm develop detect isovolum moment im aortic valv closur ac point scg four differ envelop calcul method use cardiac sound characterist waveform cscw shannon absolut hilbert algorithm evalu base dataset includ subject undergo lower bodi negat pressur test anoth dataset includ subject dataset previous manual annot algorithm base cscw envelop calcul produc highest detect accuraci im ac overal cscw algorithm detect accuraci test dataset im ac point respect

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