A preprocessing tool for removing artifact from cardiac RR interval recordings using three‐dimensional spatial distribution mapping. (11th January 2016)

• Record Type: Journal Article
• Authors: Stapelberg, Nicolas J. C.; Neumann, David L.; Shum, David H. K.; McConnell, Harry; Hamilton‐Craig, Ian
• Is Part Of: Psychophysiology. Volume 53:Number 4(2016:Apr.)
• Page Start: 482
• Page End: 492
• Publication Date: 2016-01-11
• Subjects: RR interval recordings -- Artifact removal -- Preprocessing; Psychophysiology -- Periodicals; 612.8
• DOI: 10.1111/psyp.12598
• Languages: English
• ISSNs: 0048-5772

Abstract

Abstract: Artifact is common in cardiac RR interval data that is recorded for heart rate variability (HRV) analysis. A novel algorithm for artifact detection and interpolation in RR interval data is described. It is based on spatial distribution mapping of RR interval magnitude and relationships to adjacent values in three dimensions. The characteristics of normal physiological RR intervals and artifact intervals were established using 24‐h recordings from 20 technician‐assessed human cardiac recordings. The algorithm was incorporated into a preprocessing tool and validated using 30 artificial RR (ARR) interval data files, to which known quantities of artifact (0.5%, 1%, 2%, 3%, 5%, 7%, 10%) were added. The impact of preprocessing ARR files with 1% added artifact was also assessed using 10 time domain and frequency domain HRV metrics. The preprocessing tool was also used to preprocess 69 24‐h human cardiac recordings. The tool was able to remove artifact from technician‐assessed human cardiac recordings (sensitivity 0.84, SD = 0.09, specificity of 1.00, SD = 0.01) and artificial data files. The removal of artifact had a low impact on time domain and frequency domain HRV metrics (ranging from 0% to 2.5% change in values). This novel preprocessing tool can be used with human 24‐h cardiac recordings to remove artifact while minimally affecting physiological data and therefore having a low impact on HRV measures of that data.