A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG

Zha, Xuefan; Wehbe, Leila; Sclabassi, R.J.; Mace, Zachary; Liang, Y; Yu, Alexander; Leonardo, Jody; Cheng, Boyle C.; Hillman, Todd A.; Chen, Douglas A.; Riviere, Cameron N.

doi:10.1109/tmrb.2020.3048255

Cited by 17 publications

(9 citation statements)

References 25 publications

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“…A flow-chart of the article selection process is shown in Figure 1 . The 13 studies included in this review were published from 2016 to 2022 [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], with the majority published during or after 2020. Table 1 shows an overview of the methodologies used, adverse events analyzed, AI algorithms, type of validation, outcomes, and comparative metrics from the 13 included articles.…”

Section: Resultsmentioning

confidence: 99%

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Eppler

Sayegh

Maas

et al. 2023

JCM

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Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7–46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.

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Section: Resultsmentioning

confidence: 99%

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Eppler

Sayegh

Maas

et al. 2023

JCM

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“…In this dataset, in order to facilitate subsequent research such as training neural networks with continuous and complex electromyographic signals collected during surgery for classification or prediction tasks, we proposed an innovative data annotation method after extensive discussions and explorations with clinical physicians. As shown in Table 2 , we classified electromyographic signals into seven categories according to previous studies [ [5] , [6] ], where Event “6” represents the usual healthy EMG baseline activity which we defined as "quiet". We focused more on the six signal categories with special clinical significances, labelled as Event “0” to “5”.…”

Section: Data Descriptionmentioning

confidence: 99%

“…The robustness of the signal recognition and the subjectivity of the alarms are yet to be solved. Although relevant studies [ 4 , 5 ] found that some special EMG discharge patterns are highly correlated with postoperative cranial nerve palsy [6] , further research is still needed. To help more researchers solve these problems, we compiled this six-channel EMG dataset with the technology of cIONM, aiming to recognize EMG discharge patterns and predict postoperative outcomes.…”

Section: Introductionmentioning

confidence: 99%

A multichannel electromyography dataset for continuous intraoperative neurophysiological monitoring of cranial nerve

Ma,

Chen,

Pang

et al. 2024

Data in Brief

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“…Unlike EEG, both classical and deep Machine Learning techniques have been used to correct motion artifacts from other physiological signals such as photoplethysmography (PPG) [45][46][47][48][49][50][51][52], electrocardiogram (ECG) [45,[53][54][55][56][57][58][59][60], electromyogram (EMG) [61,62], and phonocardiogram (PCG) [63]. To fill this void, this study presents a novel 1D convolutional neural network (CNN)-based signal synthesis or reconstruction approach to correct motion artifacts from motion-corrupted EEG recordings.…”

Section: Introductionmentioning

confidence: 99%

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Mahmud

Hossain

Chowdhury

et al. 2022

Neural Comput & Applic

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Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement.

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A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG

Cited by 17 publications

References 25 publications

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

A multichannel electromyography dataset for continuous intraoperative neurophysiological monitoring of cranial nerve

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

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