Predicting postoperative pain in lung cancer patients using preoperative peak alpha frequency

Millard, Samantha K; Furman, Andrew J.; Kerr, Amy; Seminowicz, David A.; Gao, Fang; Naidu, Babu; Mazaheri, Ali

doi:10.1016/j.bja.2022.03.006

Cited by 16 publications

(24 citation statements)

References 9 publications

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“…This has been corroborated using a "prolonged pain model," which combined the administration of capsaicin and thermal heat pain to the forearm and pressure pain thresholds before and after injections of nerve growth factors in the forearm. 28 Millard et al 44 translated this work to a patient cohort by collecting rEEG data from 16 individuals before thoracotomy surgery, with an average age of 67 years (range 5 59-73 years). 44 Baseline PAF predicted pain sensitivity post thoracotomy; a negative correlation was found between PAF and present, average, and worst postoperative pain.…”

Section: Discussionmentioning

confidence: 99%

“…28 Millard et al 44 translated this work to a patient cohort by collecting rEEG data from 16 individuals before thoracotomy surgery, with an average age of 67 years (range 5 59-73 years). 44 Baseline PAF predicted pain sensitivity post thoracotomy; a negative correlation was found between PAF and present, average, and worst postoperative pain. Secondary analyses that split groups based on worst postoperative pain revealed that individuals with higher worst postoperative pain had a PAF between 7.81 and 8.82 Hz.…”

Section: Discussionmentioning

confidence: 99%

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Lower individual alpha frequency in individuals with chronic low back pain and fear of movement

Ho,

Park,

Wang

et al. 2023

Pain

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Significant progress has been made in linking measures of individual alpha frequency (IAF) and pain. A lower IAF has been associated with chronic neuropathic pain and with an increased sensitivity to pain in healthy young adults. However, the translation of these findings to chronic low back pain (cLBP) are sparse and inconsistent. To address this limitation, we assessed IAFs in a cohort of 70 individuals with cLBP, implemented 3 different IAF calculations, and separated cLBP subjects based on psychological variables. We hypothesized that a higher fear movement in cLBP is associated with a lower IAF at rest. A total of 10 minutes of resting data were collected from 128 electroencephalography channels. Our results offer 3 novel contributions to the literature. First, the high fear group had a significantly lower peak alpha frequency. The high fear group also reported higher pain and higher disability. Second, we calculated individual alpha frequency using 3 different but established methods; the effect of fear on individual alpha frequency was robust across all methods. Third, fear of movement, pain intensity, and disability highly correlated with each other and together significantly predicted IAF. Our findings are the first to show that individuals with cLBP and high fear have a lower peak alpha frequency.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Lower individual alpha frequency in individuals with chronic low back pain and fear of movement

Ho,

Park,

Wang

et al. 2023

Pain

View full text Add to dashboard Cite

show abstract

“…Millard et al. [ 97 ] recorded individual peak alpha frequency (PAF) with cEEGrid sensors to predict future pain severity in patients undergoing thoracotomy. Segaert et al.…”

Section: Ear-eeg-based Bcimentioning

confidence: 99%

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Zhang,

Li,

Huang

et al. 2023

Health Data Sci

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Importance: Brain–computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.

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“…These techniques have limited temporal resolution, making it difficult to capture the dynamic nature of pain perception and experience ( Wager et al, 2013 ). Therefore, some study groups have shifted focus to explore less invasive and more cost-effective alternatives with a higher temporal resolution, such as electroencephalography (EEG) ( Pinheiro et al, 2016 ; Ploner et al, 2017 ; Levitt and Saab, 2019 ; Van Der Miesen et al, 2019 ; Xu and Huang, 2020 ; Millard et al, 2022 ; Chowdhury et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

et al. 2023

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Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

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Predicting postoperative pain in lung cancer patients using preoperative peak alpha frequency

Cited by 16 publications

References 9 publications

Lower individual alpha frequency in individuals with chronic low back pain and fear of movement

Lower individual alpha frequency in individuals with chronic low back pain and fear of movement

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

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