Sympathetic nerve activity recovery from the skin recording using the modern optimal shrinkage technique

ObjectiveThe widely used bandpass filter (BPF)-based algorithm for recovering sympathetic nerve activity (SNA) from the skin sympathetic nerve activity (SKNA-I) signal, recorded via electrocardiogram electrodes or subcutaneous sympathetic nerve activity (SCNA-I) in a lead I setup, has limitations. It excludes spectral information outside the BPF range and may retain artifacts, such as cardiac activity or pacemaker interference, in the recovered SNA (rSNA) signal. This study aims to develop an algorithm that recovers the full spectral SNA information as comprehensively as possible for evaluating the autonomic nervous system (ANS). MethodsWe propose a novel algorithm, S3 (SNA from Shrink and Subtraction), which integrates the optimal shrinkage algorithm (eOptShrink) with the template subtraction (TS) method. The performance of S3 was evaluated against other algorithms using semi-real simulated SKNA-I data, a human SKNA-I database including subjects with pacemakers or atrial fibrillation, and a mouse SCNA-I database. ResultsThe S3 algorithm demonstrated numerical efficiency and outperformed existing approaches, including traditional TS, BPF and other methods, in both time and frequency domains. Notably, in addition to the traditional 500-1000Hz spectral band, S3 effectively recovers spectral information across the 50-300Hz and 300-500Hz frequency bands, making it suitable for homecare ANS evaluation. All quantitative results are supported by the rSNA tracing for visual inspections. ConclusionS3 accurately recovers the full-spectrum SNA. SignificanceBy enabling the exploration of the entire SNA spectrum, S3 offers a promising tool for ANS evaluation and applications in homecare environments.