Use of carbon nanotube sensor for detecting postoperative abnormal respiratory waveforms

Abstract

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.

Complex-Valued Wavelet Spectrum Analysis of Respiratory Conditions and Its Feasibility in the Detection of Low-Functional Respiration

“…A normal respiratory signal repeats around every 4-5 s [19]. A repetitive signal can also appear around every 40-50 s [9] or at a longer interval, particularly in low-functional respiration Considering wavelet scale s and time-shift phase t, η is given as ζ−t 0 s with pseudotime ζ for wavelet functions. Thus, the wavelet function is given as…”

“…A normal respiratory signal repeats around every 4-5 s [19]. A repetitive signal can also appear around every 40-50 s [9] or at a longer interval, particularly in low-functional respiration according to empirical knowledge in the clinic. This leads to a signal-processing strategy that is the correlation of time-periodic spectral series being able to distinguish normal and low-functional respirations.…”

“…The respiration data of 19 patients, who were 61 to 80 years old, were evaluated. They were classified by a respiratory specialist or medical doctor with a criterion on featured wave shapes written in [9]. Data conditions are shown in Table 1.…”

“…Other approaches addressed this with wearable or ultrasound-based surface-measuring sensors but could have problems f looseness or instability [4][5][6][7]. To directly measure lung volume, although this represents one dimension of the volume, Kobayashi et al detected lung volume over time by measuring changes in the intercostal distance by using a displacement sensor attached across the sixth to eighth ribs [8,9]. They applied Hilbert and Fourier transforms to the measured signals and confirmed a signal-pattern difference between normal and low-functional lung phases.…”