Objective

A recently introduced Spinal Cord Stimulation (SCS) system operates at 10 kHz, faster than conventional SCS systems, resulting in significantly more power delivered to tissues. Using a SCS heat phantom and bioheat multi-physics model, we characterized tissue temperature increases by this 10 kHz system. We also evaluated its Implanted Pulse Generator (IPG) output compliance and the role of impedance in temperature increases.

Materials and Methods

The 10 kHz SCS system output was characterized under resistive loads (1–10 KΩ). Separately, fiber optic temperature probes quantified temperature increases (ΔTs) around the SCS lead in specially developed heat phantoms. The role of stimulation Level (1–7; ideal pulse peak-to-peak of 1–7mA) was considered, specifically in the context of stimulation current Root Mean Square (RMS). Data from the heat phantom were verified with the SCS heat-transfer models. A custom high-bandwidth stimulator provided 10 kHz pulses and sinusoidal stimulation for control experiments.

Results

The 10 kHz SCS system delivers 10 kHz biphasic pulses (30-20-30 μs). Voltage compliance was 15.6V. Even below voltage compliance, IPG bandwidth attenuated pulse waveform, limiting applied RMS. Temperature increased supralinearly with stimulation Level in a manner predicted by applied RMS. ΔT increases with Level and impedance until stimulator compliance was reached. Therefore, IPG bandwidth and compliance dampen peak heating. Nonetheless, temperature increases predicted by bioheat multi-physic models (ΔT = 0.64°C and 1.42°C respectively at Level 4 and 7 at the cervical segment; ΔT = 0.68°C and 1.72°C respectively at Level 4 and 7 at the thoracic spinal cord)–within ranges previously reported to effect neurophysiology.

Conclusions

Heating of spinal tissues by this 10 kHz SCS system theoretically increases quickly with stimulation level and load impedance, while dampened by IPG pulse bandwidth and voltage compliance limitations. If validated in vivo as a mechanism of kHz SCS, bioheat models informed by IPG limitations allow prediction and optimization of temperature changes.