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Spectrogram–CNN index: A novel analgesic index for postoperative pain assessment

Spectrogram–CNN index: A novel analgesic index for postoperative pain assessment Spectrogram–CNN index: A novel analgesic index for postoperative pain assessment
Spectrogram–CNN index: A novel analgesic index for postoperative pain assessment Spectrogram–CNN index: A novel analgesic index for postoperative pain assessment

This observational study focused on the development of a new analgesic index via photoplethysmogram (PPG) spectrograms and CNN for estimating pain in conscious patients.

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Key take away

For the postsurgery pain assessment of conscious patients, the newly developed spectrogram–convolutional neural network (CNN) index was found to outperform the commercialized surgical pleth index (SPI). 

Background

This observational study focused on the development of a new analgesic index via photoplethysmogram (PPG) spectrograms and CNN for estimating pain in conscious patients.

Method

The PPGs were procured from a total of 100 surgical patients for 6 minutes both in the no pain (pre-surgically) and in the presence (post-surgically) of pain. PPG data of the later 5 minutes were used for examination. 

A spectrogram–CNN index as per the PPGs and a CNN was made to assess pain. Utilizing the ‘area under the curve of the receiver-operating characteristic curve’ (AUC-ROC), performance of the two indices was evaluated. 

Result

Mean spectrogram–CNN index value increased significantly in case of pain. The AUC was 0.76, and the balanced accuracy was 71.4%. With a sensitivity and specificity of 68.3% and 73.8%, the spectrogram–CNN index cutoff value for detection of pain was 48. The spectrogram–CNN index illustrated improved performance measures in terms of balanced accuracy, sensitivity, and particularly specificity, as shown in Figure 1:


Conclusion

CNN, a new analgesic index, can proficiently detect pain after surgery in conscious patients. Future studies are needed to assess the practicability of this index and avoid over-fitting to several populations, together with patients under general anesthesia.

Source:

Journal of Medical Internet Research

Article:

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

Authors:

Byung-Moon Choi et al.

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