A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals

• Published in: IEEE journal of biomedical and health informatics 2020-04, Vol.24 (4), p.1080-1092
• Main Authors: Zia, Jonathan, Kimball, Jacob, Hersek, Sinan, Shandhi, Md Mobashir Hasan, Semiz, Beren, Inan, Omer T.
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; cardiac monitoring; Classification; Corruption; Discriminant analysis; Electrocardiography; ensemble voting; Female; Heart - physiology; Heart Function Tests - methods; Humans; Indexing; Localization; Male; Morphology; Motion artifacts; Motion segmentation; Quadrature amplitude modulation; Quality; Quality assessment; Quality assurance; seismocardiography; Signal classification; Signal Processing, Computer-Assisted; Signal quality; time warping; Young Adult
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2931348

The seismocardiogram (SCG) is a noninvasively-obtained cardiovascular bio-signal that has gained traction in recent years, however is limited by its susceptibility to noise and motion artifacts. Because of this, signal quality must be assured before data are used to inform clinical care. Common methods of signal quality assurance include signal classification or assignment of a numerical quality index. Such tasks are difficult with SCG because there is no accepted standard for signal morphology. In this paper, we propose a unified method of quality indexing and classification that uses multi-subject-based methods to overcome this challenge. Dynamic-time feature matching is introduced as a novel method of obtaining the distance between a signal and reference template, with this metric, the signal quality index (SQI) is defined as a function of the inverse distance between the SCG and a large set of template signals. We demonstrate that this method is able to stratify SCG signals on held-out subjects based on their level of motion-artifact corruption. This method is extended, using the SQI as a feature for classification by ensembled quadratic discriminant analysis. Classification is validated by demonstrating, for the first time, both detection and localization of SCG sensor misplacement, achieving an F1 score of 0.83 on held-out subjects. This paper may provide a necessary step toward automating the analysis of SCG signals, addressing many of the key limitations and concerns precluding the method from being widely used in clinical and physiological sensing applications.