Postoperative Pain Assessment Indices Based on Photoplethysmography Waveform Analysis

Yang, Yoon La; Seok, Hyeon Seok; Noh, Gyu Jeong; Choi, Byung‐Moon; Shin, Hyunki

doi:10.3389/fphys.2018.01199

Cited by 30 publications

(21 citation statements)

References 36 publications

(54 reference statements)

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“…Because existing pain assessment methods, such as the SPI, use only certain features of a PPG that show significant changes in surgical stimuli, numerous pain-related information reflected in the PPG may be overlooked. To overcome these limitations, research has been conducted on new pain-related features derived from sophisticated PPG waveform analysis in addition to the heart rate interval and PPG amplitude reflected in the SPI [5][6][7]. However, these pain indicators still depend on complex processes, such as peak detection and feature extraction, which require accurate peak extraction algorithms and are vulnerable to signal quality degradation.…”

Section: Discussionmentioning

confidence: 99%

“…A photoplethysmogram (PPG) is a biosignal that can be obtained continuously and noninvasively using a pulse oximeter. Because a PPG conveys much information about a patient's condition, many attempts have been made to quantify pain by analyzing PPG signals [3,[5][6][7]. The surgical pleth index (SPI; GE Healthcare), developed for quantifying nociception during general anesthesia, only considers the amplitude and heartbeat interval of a PPG [3].…”

Section: Introductionmentioning

confidence: 99%

“…The surgical pleth index (SPI; GE Healthcare), developed for quantifying nociception during general anesthesia, only considers the amplitude and heartbeat interval of a PPG [3]. In addition to these 2 parameters, other pain-related features are present in PPG signals [6,7]. Therefore, the application of a new analytical method has the potential to improve the performance of analgesic indices.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Background Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new analgesic index with improved performance to quantify postoperative pain in conscious patients. Objective This study aimed to develop a new analgesic index using photoplethysmogram (PPG) spectrograms and a convolutional neural network (CNN) to objectively assess pain in conscious patients. Methods PPGs were obtained from a group of surgical patients for 6 minutes both in the absence (preoperatively) and in the presence (postoperatively) of pain. Then, the PPG data of the latter 5 minutes were used for analysis. Based on the PPGs and a CNN, we developed a spectrogram–CNN index for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic curve was measured to evaluate the performance of the 2 indices. Results PPGs from 100 patients were used to develop the spectrogram–CNN index. When there was pain, the mean (95% CI) spectrogram–CNN index value increased significantly—baseline: 28.5 (24.2-30.7) versus recovery area: 65.7 (60.5-68.3); P<.01. The AUC and balanced accuracy were 0.76 and 71.4%, respectively. The spectrogram–CNN index cutoff value for detecting pain was 48, with a sensitivity of 68.3% and specificity of 73.8%. Conclusions Although there were limitations to the study design, we confirmed that the spectrogram–CNN index can efficiently detect postoperative pain in conscious patients. Further studies are required to assess the spectrogram–CNN index’s feasibility and prevent overfitting to various populations, including patients under general anesthesia. Trial Registration Clinical Research Information Service KCT0002080; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…The photoplethysmographic waveform from which dPOP is calculated is complex, reflecting absorption of light from arterial, capillary and venous blood. Thus it is affected by both the cardiovascular, respiratory and autonomic nervous systems [28], and has been used to extract information about both fluid responsiveness [29], respiration [30] and pain [31]. dPOP appears to reflect volume status less consistently than dPP during mechanical ventilation, which is generally explained by the complexity of the signal as well as proprietary processing and filtering algorithms [29, 32].…”

Section: Discussionmentioning

confidence: 99%

Respiratory variations in pulse pressure and photoplethysmographic waveform amplitude during positive expiratory pressure and continuous positive airway pressure in a model of progressive hypovolemia

et al. 2019

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PurposeRespiratory variations in pulse pressure (dPP) and photoplethysmographic waveform amplitude (dPOP) are used for evaluation of volume status in mechanically ventilated patients. Amplification of intrathoracic pressure changes may enable their use also during spontaneous breathing. We investigated the association between the degree of hypovolemia and dPP and dPOP at different levels of two commonly applied clinical interventions; positive expiratory pressure (PEP) and continuous positive airway pressure (CPAP).Methods20 healthy volunteers were exposed to progressive hypovolemia by lower body negative pressure (LBNP). PEP of 0 (baseline), 5 and 10 cmH2O was applied by an expiratory resistor and CPAP of 0 (baseline), 5 and 10 cmH2O by a facemask. dPP was obtained non-invasively with the volume clamp method and dPOP from a pulse oximeter. Central venous pressure was measured in 10 subjects. Associations between changes were examined using linear mixed-effects regression models.ResultsdPP increased with progressive LBNP at all levels of PEP and CPAP. The LBNP-induced increase in dPP was amplified by PEP 10 cmH20. dPOP increased with progressive LBNP during PEP 5 and PEP 10, and during all levels of CPAP. There was no additional effect of the level of PEP or CPAP on dPOP. Progressive hypovolemia and increasing levels of PEP were reflected by increasing respiratory variations in CVP.ConclusiondPP and dPOP reflected progressive hypovolemia in spontaneously breathing healthy volunteers during PEP and CPAP. An increase in PEP from baseline to 10 cmH2O augmented the increase in dPP, but not in dPOP.

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“…Similar to heart-rate variability, autonomic nervous system changes can be estimated from pulse-rate variations induced at pulse-topulse interval in PPG [18][19][20]. In addition, autonomic nervous system fluctuations can be observed based on changes of PPG amplitude, length, width, and height [13,[21][22][23][24] in diastolic and systolic sections. Furthermore, blood pressure [25,26], cardiac output [27][28][29], arterial occlusion [30,31], surgical stress [32,33], vascular aging and arterial compliance [21], and hemodynamic and physiological analyses using PPG have also been studied.…”

Section: Introductionmentioning

confidence: 99%

Recommendation for Measuring Digital Volume Pulse in Mobile Application: For Healthy Normal Subject

Seok

Shin

2021

IEEE Access

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The aim of this study was to identify changes in photoplethysmogram waveform with decreasing sampling frequency and quantization bit depth and suggest appropriate criteria for measurements. We performed down-sampling and re-quantization of phoplethysmogram measured at high resolution. Changes of waveform were quantitatively observed. Photoplethysmograms recorded at a 1-kHz sampling frequency and 16-bit quantization bit depth were converted to signals with sampling frequencies of 500-, 250-, 100-, 50-, 25-, and 10-Hz by means of down-sampling. We then performed re-quantization to convert the quantization bit depth into 16, 14, 12, 10, 8, and 6 bits for each down-sampled signal. Degradation of signal was quantified in terms of morphological change and feature-point deviation. Morphological analysis revealed that the correlation coefficient was decreased while normalized root mean square error was increased with decreasing sampling frequency and quantization bit depth. Feature-point analysis revealed that the mean absolute time error and amplitude error of feature points tended to increase with decreasing sampling frequency and quantization bit depth. Although there were differences according to pulse onset and systolic peak, sampling frequency ≥ 250-Hz and 16-bit quantization bits were required in order to have ≤ 1 ms of timing error and a normalized amplitude error ≤ 1%. In addition, sampling frequency ≥ 100-Hz and 12-bit quantization bits are recommended to have feature-point time errors and amplitude errors < 10 ms and a normalized amplitude error < 10%.

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Postoperative Pain Assessment Indices Based on Photoplethysmography Waveform Analysis

Cited by 30 publications

References 36 publications

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

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

Respiratory variations in pulse pressure and photoplethysmographic waveform amplitude during positive expiratory pressure and continuous positive airway pressure in a model of progressive hypovolemia

Recommendation for Measuring Digital Volume Pulse in Mobile Application: For Healthy Normal Subject

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