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

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zimo Liu ◽  
Penghu Wei ◽  
Yiping Wang ◽  
Yanfeng Yang ◽  
Yang Dai ◽  
...  
Keyword(s):  
High Frequency ◽  
Cross Validation ◽  
Visual Analysis ◽  
Short Term Memory ◽  
Intractable Epilepsy ◽  
Automatic Detection ◽  
High Sensitivity ◽  
High Frequency Oscillation ◽  
Time Frequency ◽  
End To End

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.

scholarly journals Time-Frequency Strategies for Increasing High-Frequency Oscillation Detectability in Intracerebral EEG

2016 ◽  
Vol 63 (12) ◽  
pp. 2595-2606 ◽  
Author(s):  
Nicolas Roehri ◽  
Jean-Marc Lina ◽  
John C. Mosher ◽  
Fabrice Bartolomei ◽  
Christian-George Benar
Keyword(s):  
High Frequency ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Time Frequency ◽  
Intracerebral Eeg

scholarly journals SGM: a novel time-frequency algorithm based on unsupervised learning improves high-frequency oscillation detection in epilepsy

2020 ◽  
Vol 17 (2) ◽  
pp. 026032 ◽  
Author(s):  
Carolina Migliorelli ◽  
Alejandro Bachiller ◽  
Joan F Alonso ◽  
Sergio Romero ◽  
Javier Aparicio ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
High Frequency ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Time Frequency ◽  
Oscillation Detection

scholarly journals Feasibility of Automatic Detection of High-Frequency Oscillations in Human Tripolar Laplacian Electroencephalogram Using Exponentially Embedded Family

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 52
Author(s):  
Oleksandr Makeyev ◽  
Frederick Lee ◽  
Mark Musngi
Keyword(s):  
High Frequency ◽  
Diagnostic Yield ◽  
Signal To Noise Ratio ◽  
Automatic Detection ◽  
High Sensitivity ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Patients With Epilepsy ◽  
Disc Electrodes ◽  
Electroencephalogram Eeg

Epilepsy affects approximately 67 million people worldwide with up to 75% from developing countries. Diagnosing epilepsy using electroencephalogram (EEG) is complicated due to its poor signal-to-noise ratio, high sensitivity to various forms of artifacts, and low spatial resolution. Laplacian EEG signal via novel and noninvasive tripolar concentric ring electrodes (tEEG) is superior to EEG via conventional disc electrodes due to its unique capabilities, which allow automatic attenuation of common movement and muscle artifacts. In this work, we apply exponentially embedded family (EEF) to show feasibility of automatic detection of gamma band high-frequency oscillations (HFOs) in tEEG data from two human patients with epilepsy as a step toward the ultimate goal of using the automatically detected HFOs as auxiliary features for seizure onset detection to improve diagnostic yield of tEEG for epilepsy. Obtained preliminary results suggest the potential of the approach and feasibility of detecting HFOs in tEEG data using the EEF based detector with high accuracy. Further investigation on a larger dataset is needed for a conclusive proof.

High-Frequency Oscillation and Chronic Lung Disease in Very Low Birth Weight Infants

PEDIATRICS ◽  
2001 ◽  
Vol 108 (1) ◽  
pp. 212-214
Author(s):  
J. P. Shenai; ◽  
P. Rimensberger; ◽  
U. Thome ◽  
F. Pohlandt; ◽  
P. Rimensberger
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Lung Disease ◽  
Chronic Lung Disease ◽  
High Frequency ◽  
Very Low Birth Weight ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Low Birth Weight Infants

scholarly journals Electrophysiological aftereffects of high-frequency transcranial random noise stimulation (hf-tRNS): an EEG investigation

2021 ◽  
Author(s):  
Filippo Ghin ◽  
Louise O’Hare ◽  
Andrea Pavan
Keyword(s):  
High Frequency ◽  
Discrimination Task ◽  
Temporal Dynamics ◽  
Random Noise ◽  
Decomposition Analysis ◽  
Oscillatory Activity ◽  
Motion Direction ◽  
Time Frequency ◽  
Direction Discrimination ◽  
Transcranial Random Noise Stimulation

AbstractThere is evidence that high-frequency transcranial random noise stimulation (hf-tRNS) is effective in improving behavioural performance in several visual tasks. However, so far there has been limited research into the spatial and temporal characteristics of hf-tRNS-induced facilitatory effects. In the present study, electroencephalogram (EEG) was used to investigate the spatial and temporal dynamics of cortical activity modulated by offline hf-tRNS on performance on a motion direction discrimination task. We used EEG to measure the amplitude of motion-related VEPs over the parieto-occipital cortex, as well as oscillatory power spectral density (PSD) at rest. A time–frequency decomposition analysis was also performed to investigate the shift in event-related spectral perturbation (ERSP) in response to the motion stimuli between the pre- and post-stimulation period. The results showed that the accuracy of the motion direction discrimination task was not modulated by offline hf-tRNS. Although the motion task was able to elicit motion-dependent VEP components (P1, N2, and P2), none of them showed any significant change between pre- and post-stimulation. We also found a time-dependent increase of the PSD in alpha and beta bands regardless of the stimulation protocol. Finally, time–frequency analysis showed a modulation of ERSP power in the hf-tRNS condition for gamma activity when compared to pre-stimulation periods and Sham stimulation. Overall, these results show that offline hf-tRNS may induce moderate aftereffects in brain oscillatory activity.

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

Comparison of wavelet continuous transform and signal averaged ECG for high frequency content and late potential detection in healthy individuals

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Garcia Iglesias ◽  
J.M Rubin Lopez ◽  
D Perez Diez ◽  
C Moris De La Tassa ◽  
F.J De Cos Juez ◽  
...  
Keyword(s):  
High Frequency ◽  
Classical Method ◽  
Total Power ◽  
Frequency Content ◽  
Healthy Individuals ◽  
Time Frequency ◽  
Qt Intervals ◽  
Long Time ◽  
Power Content ◽  
High Frequency Content

Abstract Introduction The Signal Averaged ECG (SAECG) is a classical method forSudden Cardiac Death (SCD) risk assessment, by means of Late Potentials (LP) in the filtered QRS (fQRS)[1]. But it is highly dependent on noise and require long time records, which make it tedious to use. Wavelet Continuous Transform (WCT) meanwhile is easier to use, and may let us to measure the High Frequency Content (HFC) of the QRS and QT intervals, which also correlates with the risk of SCD [2,3]. Whether the HFC of the QRS and QT measured with the WCT is a possible subrogate of LP, has never been demonstrated. Objective To demonstrate if there is any relationship between the HFC measured with the WCT and the LP analyzed with the SAECG. Methods Data from 50 consecutive healthy individuals. The standard ECG was digitally collected for 3 consecutive minutes. For the WCT Analysis 8 consecutive QT complexes were used and for the SAECG Analysis all available QRS were used. The time-frequency data of each QT complex were collected using the WCT as previously described [3] and the Total, QRS and QT power were obtained from each patient. For the SAECG, bipolar X, Y and Z leads were used with a bidirectional filter at 40 to 250 Hz [1]. LP were defined as less than 0.05 z in the terminal part of the filtered QRS and the duration (SAECG LP duration) and root mean square (SAECG LP Content) of this LP were calculated. Pearson's test was used to correlate the Power content with WCT analysis and the LP in the SAECG. Results There is a strong correlation between Total Power and the SAECG LP content (r=0.621, p<0.001). Both ST Power (r=0.567, p<0.001) and QRS Power (r=0.404, p=0.004) are related with the SAECG LP content. No correlation were found between the Power content (Total, QRS or ST Power) and the SAECG LP duration. Also no correlation was found between de SAECG LP content and duration. Conclusions Total, QRS and ST Power measured with the WCT are good surrogates of SAECG LP content. No correlation were found between WCT analysis and the SAECG LP duration. Also no correlation was found between the SAECG LP content and duration. This can be of high interest, since WCT is an easier technique, not needing long recordings and being less affected by noise. Funding Acknowledgement Type of funding source: None

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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