Automatic detection of interictal ripples on scalp EEG to evaluate the effect and prognosis of ACTH therapy in patients with infantile spasms

Wang, Wei; Li, Hua; Yan, Jiaqing; Zhang, Herui; Li, Xiaonan; Zheng, Shuai; Wang, Jiaoyang; Xing, Yue; Cheng, Lipeng; Li, Donghong; Lai, Huanling; Qu, Junda; Loh, Horace H.; Fang, Fang; Yang, Xiaofeng

doi:10.1111/epi.17018

Cited by 17 publications

(25 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2021, Wu et al [ 29 ] proposed a novel detector based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors, which achieved a sensitivity of 92.4% in ripples and 90.3% in fast ripples and an FDR of 9.2% in ripples and 10.7% in fast ripples. Besides, in 2021, Wang et al [ 47 ] proposed an algorithm to calculate the dynamic baseline based on the maximum distributed peak points to automatically detect HFOs and achieved a sensitivity of 82.666% and a specificity of 63.352%.…”

Section: Resultsmentioning

confidence: 99%

Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation

Liu

Wei

Wang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Accurate identification of high-frequency oscillation (HFO) is an important prerequisite for precise localization of epileptic foci and good prognosis of drug-refractory epilepsy. Exploring a high-performance automatic detection method for HFOs can effectively help clinicians reduce the error rate and reduce manpower. Due to the limited analysis perspective and simple model design, it is difficult to meet the requirements of clinical application by the existing methods. Therefore, an end-to-end bi-branch fusion model is proposed to automatically detect HFOs. With the filtered band-pass signal (signal branch) and time-frequency image (TFpic branch) as the input of the model, two backbone networks for deep feature extraction are established, respectively. Specifically, a hybrid model based on ResNet1d and long short-term memory (LSTM) is designed for signal branch, which can focus on both the features in time and space dimension, while a ResNet2d with a Convolutional Block Attention Module (CBAM) is constructed for TFpic branch, by which more attention is paid to useful information of TF images. Then the outputs of two branches are fused to realize end-to-end automatic identification of HFOs. Our method is verified on 5 patients with intractable epilepsy. In intravalidation, the proposed method obtained high sensitivity of 94.62%, specificity of 92.7%, and F1-score of 93.33%, and in cross-validation, our method achieved high sensitivity of 92.00%, specificity of 88.26%, and F1-score of 89.11% on average. The results show that the proposed method outperforms the existing detection paradigms of either single signal or single time-frequency diagram strategy. In addition, the average kappa coefficient of visual analysis and automatic detection results is 0.795. The method shows strong generalization ability and high degree of consistency with the gold standard meanwhile. Therefore, it has great potential to be a clinical assistant tool.

show abstract

Section: Resultsmentioning

confidence: 99%

Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation

Liu

Wei

Wang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Wang et al. showed that, after ACTH treatment, the number and channels of high-frequency oscillations (HFOs) were significantly lower, whereas the percentage decrease in the number, spectral power, and channels of ripples was significantly higher in the seizure-free group than in the non-seizure-free group; meanwhile, the relapse subgroup showed a higher number, spectral power, and wider distribution of ripples than did the non-relapse subgroup [ 9 ]. The above studies suggest that EEG features are closely related to the relapse of IS.…”

Section: Discussionmentioning

confidence: 99%

“…EEG was recorded using the standard international 10/20 system, with 19 electrodes at a sampling frequency of 256 Hz. At least 2 h of video-EEG monitoring, including a slow-wave sleeping period, was recorded [ 9 ]. The EEG data were analyzed with a bipolar montage.…”

Section: Methodsmentioning

confidence: 99%

Assessing Risk for Relapse among Children with Infantile Spasms Using the Based Score after ACTH Treatment: A Retrospective Study

Wan

Lei²,

Liu

et al. 2022

Neurol Ther

View full text Add to dashboard Cite

Introduction Even though adrenocorticotropic hormone (ACTH) demonstrated powerful efficacy in the initially successful treatment of infantile spasms (IS), nearly one-half of patients whose spasms were once suppressed experienced relapse. There is currently no validated method for the prediction of the risk of relapse. The Burden of Amplitudes and Epileptiform Discharges (BASED) score is an electroencephalogram (EEG) grading scale for children with infantile spasms. We sought to determine whether an association exists between the BASED score after ACTH treatment and relapse after initial response with ACTH. Methods Children with IS who achieved initial response after ACTH treatment were selected as the study subjects. Those who experienced relapse within 12 months after ACTH treatment were categorized as the relapse group, and those who did not were categorized as the non-relapse group. Their general clinical data and EEG data (using BASED scoring) after ACTH treatment were collected, and compared between groups. Cox proportional hazards models were fit to determine factors associated with relapse. Results A total of 64 children with IS were enrolled in the study, of which 37 (57.8%) experienced a relapse, and the median duration after ACTH treatment was 3 (1.5, 6) months. The BASED score was significantly higher in the relapse group than in the non-relapse group. Cox modeling demonstrated that BASED score was independently associated with relapse. The patients with a score greater than or equal to 3 showed a high rate (89.3%) of relapse. The relapse group had stronger, more stable EEG functional networks than the non-relapse group, and there were obvious correlations between BASED score and functional connectivity. Conclusion This study suggests the BASED score after ACTH treatment has potential value as a predictor for relapse after initial response. Children with IS who have a BASED score greater than or equal to 3 after the initial response of ACTH carry a high risk of relapse within 1 year. Supplementary Information The online version contains supplementary material available at 10.1007/s40120-022-00347-7.

show abstract

“…However, among the 17 responders, when measured post-treatment, all three of these measurements were significantly lower for those that did not relapse within 6 months of treatment ( n = 12), compared those who relapsed ( n = 5). The decrease in ripple number, ripple spectral power, and ripple channels for each patient following treatment was also significantly greater in the non-relapse group than in the relapse group ( 56 ). A separate study showed no differences between responders ( n = 6) and non-responders ( n = 7) in pretreatment HFO modulation index at different slow-wave activity (SWA) passbands; however, group wise differences in the phase coupling angle distributions were reported ( 57 ).…”

Section: Computational Analysismentioning

confidence: 93%

“…Following ACTH treatment, the number of ripples and number of ripple channels were significantly lower in responders than non-responders; however, no difference in spectral power of ripples was noted between the two groups. Additionally, following ACTH therapy, the decrease in ripple number, ripple spectral power, and ripple channels for each patient was significantly greater for responders than non-responders ( 56 ).…”

Section: Computational Analysismentioning

confidence: 99%

EEG biomarkers for the diagnosis and treatment of infantile spasms

et al. 2022

View full text Add to dashboard Cite

Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.

show abstract

Automatic detection of interictal ripples on scalp EEG to evaluate the effect and prognosis of ACTH therapy in patients with infantile spasms

Cited by 17 publications

References 49 publications

Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation

Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation

Assessing Risk for Relapse among Children with Infantile Spasms Using the Based Score after ACTH Treatment: A Retrospective Study

EEG biomarkers for the diagnosis and treatment of infantile spasms

Contact Info

Product

Resources

About