Cardiac sensing at a spinal cord stimulation lead: a promising on-device potential biomarker for pain and wellbeing

Introduction: In the search for objective measures of therapeutic outcomes for patients with spinal cord stimulation (SCS) devices, various metrics of cardiac performance have been linked to pain as well as overall health. To track such measures at home, recent studies have incorporated wearables to monitor cardiac activity over months or years. The drawbacks to wearables, such as patient compliance, would be obviated by on-device sensing that incorporates the SCS lead. This study sought to evaluate the feasibility of using SCS leads to record cardiac electrograms. Methods: The quality of signals sensed by externalized, percutaneous leads in the thoracic spine of 10 subjects at the end of their SCS trial were characterized across various electrode configurations and postures by detecting R-peaks and calculating signal-to-noise ratio (SNR). In a subset of 5 subjects, cardiac metrics were then compared to those measured simultaneously with a wearable. Results: The average signal quality was acceptable for R-peak detection (i.e., SNR > 5) for all configurations and positions across all 10 subjects, with higher signal quality achieved when recording in resting positions. Notably, the spinal lead recordings enabled more reliable beat detection compared to the wearable (n = 29 recording pairs; p < 0.001). When excluding wearable recordings with over 35% missed beats, the inter-beat intervals across devices were highly correlated (n = 22 recording pairs; Pearson correlation: R = 0.99, p < 0.001). Further comparisons in these aligned wearable and corresponding spinal-lead recordings revealed significant differences in the frequency domain metrics (i.e., absolute and normalized high and low frequency HRV power, p < 0.05), but not in time domain HRV parameters. Discussion: The ability of an implanted SCS system to record electrocardiograms, as demonstrated here, could provide the basis of automated SCS therapy by tracking potential biomarkers of the patient's overall health state without the need for additional external devices.