Use of carbon nanotube sensor for detecting postoperative abnormal respiratory waveforms

BACKGROUND: This feasibility study aimed to detect respiratory waveforms from thoracic movements and evaluate if postoperative complications could be predicted using a carbon nanotube sensor. METHODS: Fifty patients who underwent lung resection for lung tumors were enrolled. The lung monitoring system of the carbon nanotube sensor was placed on bilateral chest walls across the 6(th)–9(th) ribs to measure chest wall motion. We examined the respiratory waveform in relation to surgical findings, postoperative course, and complications using Hilbert transform and Fast Fourier Transform (FFT). RESULTS: Of 50 patients (37 males, 13 females), 22 were included in the normal lung function group and 28 were included in the low lung function group. The respiratory rate and waveform indicated a regular pattern in the normal lung function group and the respiratory rate could be detected. Conversely, irregular respiratory pattern was detected in 70% of patients in the low lung function group. There was no significant different overall envelope peak value between operated side and non-operated side (0.195±0.05 and 0.18±0.06). In contrast, there was significantly high peak value in the presence of postoperative complications (P<0.05). And there was a significantly higher peak value in air leakage presence than air leakage absence in operated side (P=0.045). CONCLUSIONS: The present study confirmed the feasibility of the sensor. It is promising in visualizing the respiratory state and detecting respiratory changes postoperatively.