Pain Prediction From ECG in Vascular Surgery

Rosenberg, Wilhelm von; Goverdovsky, Valentin; Powezka, Katarzyna; Jaffer, Usman; Mandic, Danilo P.

doi:10.1109/jtehm.2017.2734647

Cited by 15 publications

(14 citation statements)

References 33 publications

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“…The PE is a metric for detecting dynamical changes and for estimating the information contained in a time series based on comparing consecutive values of a time series. Compared to other entropy metrics, the PE requires less computational time and is robust to noise in the measurements; hence, the method is suited to time series with poor stationarity characteristics, such as physiological signals [44].…”

Section: Feature Extractionmentioning

confidence: 99%

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Nakamura

Alqurashi

Morrell

et al. 2020

IEEE Trans. Biomed. Eng.

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111

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Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1 % in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The 'standardised' one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

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Section: Feature Extractionmentioning

confidence: 99%

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Nakamura

Alqurashi

Morrell

et al. 2020

IEEE Trans. Biomed. Eng.

Self Cite

111

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“…The authors then incorporated HF, LF, and entropy in a model which correlated significantly with intraOP pain, when excluding premenopausal women (R 2 = 0.652) [ 52 ]. In the other study, with a similar population and anesthetic method, pain could be predicted by a mathematical model based on LF and HF, with an almost perfect ROC-curve (AUC = 0.97) [ 53 ]. From these preliminary results, preOP LF and HF could potentially be used to predict intraOP pain.…”

Section: Resultsmentioning

confidence: 99%

Preoperative heart rate variability as a predictor of perioperative outcomes: a systematic review without meta-analysis

Frandsen

Mehlsen

Foss

et al. 2022

J Clin Monit Comput

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Heart rate variability (HRV) is a predictor of mortality and morbidity after non-lethal cardiac ischemia, but the relation between preoperatively measured HRV and intra- and postoperative complications is sparsely studied and most recently reviewed in 2007. We, therefore, reviewed the literature regarding HRV as a predictor for intra- and postoperative complications and outcomes. We carried out a systematic review without meta-analysis. A PICO model was set up, and we searched PubMed, EMBASE, and CENTRAL. The screening was done by one author, but all authors performed detailed review of the included studies. We present data from studies on intraoperative and postoperative complications, which were too heterogeneous to warrant formal meta-analysis, and we provide a pragmatic review of HRV indices to facilitate understanding our findings. The review was registered in PROSPERO (CRD42021230641). We screened 2337 records for eligibility. 131 records went on to full-text assessment, 63 were included. In frequency analysis of HRV, low frequency to high frequency ratio could be a predictor for intraoperative hypotension in spinal anesthesia and lower total power could possibly predict intraoperative hypotension under general anesthesia. Detrended fluctuation analysis of HRV is a promising candidate for predicting postoperative atrial fibrillation. This updated review of the relation between preoperative HRV and surgical outcome suggests a clinically relevant role of HRV but calls for high quality studies due to methodological heterogeneity in the current literature. Areas for future research are suggested.

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“…Traditional ECG classification methods designed a number of hand-craft features. Typical hand-craft features include statistical features [4] – [6] , P-QRS-T features [7] – [9] , morphological features [9] – [12] , and wavelet features [13] – [16] . Also, mathematical transformations that transform the high-dimensional ECG signal into a lower-dimensional space can be used for extracting meaningful information, such as independent component analysis (ICA) [17] – [19] , principal component analysis (PCA) [19] – [21] , and linear discriminant analysis (LDA) [19] , [21] .…”

Section: Related Workmentioning

confidence: 99%

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

Zhang

Deng

Liu

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$F_{1}$ \end{document} score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.

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Pain Prediction From ECG in Vascular Surgery

Cited by 15 publications

References 33 publications

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Preoperative heart rate variability as a predictor of perioperative outcomes: a systematic review without meta-analysis

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

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