scholarly journals Automated real-time detection of tonic-clonic seizures using a wearable EMG device

Neurology ◽  
2018 ◽  
Vol 90 (5) ◽  
pp. e428-e434 ◽  
Author(s):  
Sándor Beniczky ◽  
Isa Conradsen ◽  
Oliver Henning ◽  
Martin Fabricius ◽  
Peter Wolf
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Real Time ◽  
Seizure Detection ◽  
Surface Emg ◽  
Detection Algorithm ◽  
Recording Time ◽  
Detection Latency ◽  
Clonic Seizures ◽  
Video Eeg

ObjectiveTo determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device.MethodsWe prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings by trained experts, who were blinded to data from the device. Reading the seizure logs from the device was done blinded to all other data.ResultsThe mean recording time per patient was 53.18 hours. Total recording time was 3735.5 hours, and device deficiency time was 193 hours (4.9% of the total time the device was turned on). No adverse events occurred. The sensitivity of the wearable device was 93.8% (30 out of 32 GTCS were detected). Median seizure detection latency was 9 seconds (range −4 to 48 seconds). False alarm rate was 0.67/d.ConclusionsThe performance of the wearable EMG device fulfilled the requirements of patients: it detected GTCS with a sensitivity exceeding 90% and detection latency within 30 seconds.Classification of evidenceThis study provides Class II evidence that for people with a history of GTCS, a wearable EMG device accurately detects GTCS (sensitivity 93.8%, false alarm rate 0.67/d).

scholarly journals 4209 Early Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG

2020 ◽  
Vol 4 (s1) ◽  
pp. 28-29
Author(s):  
Safa Kaleem ◽  
Jennifer H. Kang ◽  
Alok Sahgal ◽  
Christian E. Hernandez ◽  
Christa B. Swisher
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Real Time ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Training Session ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Seizure Detection ◽  
List Type

OBJECTIVES/GOALS: 1.Determine test characteristics of Neuro ICU nurse interpretation of real-time bedside qEEG for seizure detection2.Determine difference in time to detection of seizures between qEEG interpretation and raw cEEG reads3.Determine how seizure characteristics affect accuracy of qEEG readsMETHODS/STUDY POPULATION: Subjects: Nurses caring for patients admitted to the Neuro ICU at Duke University Hospital who are initiated on cEEG.Nurses evaluate qEEG display at the bedside on an hourly basis after undergoing a standardized qEEG training session. The standard practice of independent review of cEEG and treatment by the Neuro ICU team remains unchanged.Post-hoc review of cEEG data by two blinded, board-certified neurophysiologists will be performed for each patient. The raw cEEG data will be scored for the number of seizures present per hour, background, seizure duration, and seizure spatial extent.The time from first seizure occurrence to clinical recognition will be recorded.RESULTS/ANTICIPATED RESULTS: Thus far, 91 patients with 583 1-hour blocks of nurse interpretations have been studied, with 6 patients experiencing seizures while on study. Enrollment will be completed on 1/17/20Preliminary data show a sensitivity of 95.8% (79.9%, 99.9%), specificity of 95.2 (93.1%, 96.8%), positive predictive value of 46.0% (36.9%, 55.4%), negative predictive value of 99.8% (98.7%, 99.9%), positive likelihood ratio of 19.8 (13.6, 28.9), negative likelihood ratio (0.04 (0.01, 0.3). All confidence intervals are 95%. False alarm rate is 0.05/hour.Further analyses are pending completion of enrollment in January 2020.DISCUSSION/SIGNIFICANCE OF IMPACT: Nurse interpretation of real-time bedside qEEG for seizure detection is feasible in the Duke Neuro ICU. QEEG functions well as a screening tool with good specificity and low false alarm rate. Use of qEEG by nurses could lead to shorter time to seizure detection, which may improve patient outcomes. CONFLICT OF INTEREST DESCRIPTION: Safa Kaleem, BS: Research reported in this publication was supported by a Pfizer Foundation grant and the Duke Clinical Translational Science Institute (CTSI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Pfizer Foundation or the Duke CTSI. Jennifer H. Kang, MD: None to declare. Alok Sahgal, MD: None to declare. Christa B. Swisher, MD: Received speaker’s honorarium from EISAI and UCB.

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
P. Manoj Kumar ◽  
M. Parvathy ◽  
C. Abinaya Devi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Performance Metrics ◽  
Classification Model ◽  
Real Time Traffic

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

scholarly journals Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2008 ◽  
Author(s):  
Bruna G. Palm ◽  
Dimas I. Alves ◽  
Mats I. Pettersson ◽  
Viet T. Vu ◽  
Renato Machado ◽  
...  
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Reference Image ◽  
Sar Images ◽  
Image Stack ◽  
Military Vehicles ◽  
Wavelength Resolution

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0.11 / km 2 , when considering military vehicles concealed in a forest.

scholarly journals FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network

2021 ◽  
Vol 38 (5) ◽  
pp. 1495-1501
Author(s):  
Hui Huang ◽  
Zhe Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
Real Time ◽  
Large Scale ◽  
Real Life ◽  
Detection Algorithm ◽  
False Alarms ◽  
License Plate ◽  
Embedded Devices ◽  
License Plate Detection

The license plate detection technology has been widely applied in our daily life, but it encounters many challenges when performing license plate detection tasks in special scenarios. In this paper, a license plate detection algorithm is proposed for the problem of license plate detection, and an efficient false alarm filter algorithm, namely the FAFNet (False-Alarm Filter Network) is proposed for solving the problem of false alarms in license plate location scenarios in China. At first, this paper adopted the YOLOv5 target detection algorithm to detect license plates, and used the FAFNet to re-identify the images to avoid false detection. FAFNet is a lightweight convolutional neural network (CNN) that can solve the false alarm problem of real-time license plate recognition on embedded devices, and its performance is good. Next, this paper proposed a model generalization method for the purpose of making the proposed FAFNet be applicable to the license plate false alarm scenarios in other countries without the need to re-train the model. Then, this paper built a large-scale false alarm filter dataset, all samples in the dataset came from the industries and contained a variety of complex real-life scenarios. At last, experiments were conducted and the results showed that, the proposed FAFNet can achieve high-accuracy false alarm filtering and can run in real-time on embedded devices.

scholarly journals A prediction framework with time-frequency localization feature for detecting the onset of seismic events

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250008
Author(s):  
Kanchan Aggarwal ◽  
Siddhartha Mukhopadhya ◽  
Arun K. Tangirala
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Early Warning Systems ◽  
P Wave ◽  
Onset Detection ◽  
Time Frequency ◽  
Low Snr ◽  
Time Frequency Localization

Onset detection of P-wave in seismic signals is of vital importance to seismologists because it is not only crucial to the development of early warning systems but it also aids in estimating the seismic source parameters. All the existing P-wave onset detection methods are based on a combination of statistical signal processing and time-series modeling ideas. However, these methods do not adequately accommodate some advanced ideas that exist in fault detection literature, especially those based on predictive analytics. When combined with a time-frequency (t-f) / temporal-spectral localization method, the effectiveness of such methods is enhanced significantly. This work proposes a novel real-time automatic P-wave detector and picker in the prediction framework with a time-frequency localization feature. The proposed approach brings a diverse set of capabilities in accurately detecting the P-wave onset, especially in low signal-to-noise ratio (SNR) conditions that all the existing methods fail to attain. The core idea is to monitor the difference in squared magnitudes of one-step-ahead predictions and measurements in the time-frequency bands with a statistically determined threshold. The proposed framework essentially accommodates any suitable prediction methodology and time-frequency transformation. We demonstrate the proposed framework by deploying auto-regressive integrated moving average (ARIMA) models for predictions and the well-known maximal overlap discrete wavelet packet transform (MODWPT) for the t-f projection of measurements. The ability and efficacy of the proposed method, especially in detecting P-waves embedded in low SNR measurements, is illustrated on a synthetic data set and 200 real-time data sets spanning four different geographical regions. A comparison with three prominently used detectors, namely, STA/LTA, AIC, and DWT-AIC, shows improved detection rate for low SNR events, better accuracy of detection and picking, decreased false alarm rate, and robustness to outliers in data. Specifically, the proposed method yields a detection rate of 89% and a false alarm rate of 11.11%, which are significantly better than those of existing methods.

scholarly journals Performance Reassessment of a Real‐time Seizure‐detection Algorithm on Long ECoG Series

Epilepsia ◽  
2002 ◽  
Vol 43 (12) ◽  
pp. 1522-1535 ◽  
Author(s):  
Ivan Osorio ◽  
Mark G. Frei ◽  
Jon Giftakis ◽  
Tom Peters ◽  
Jeff Ingram ◽  
...  
Keyword(s):  
Real Time ◽  
Seizure Detection ◽  
Detection Algorithm

scholarly journals Performance Analysis of an Environmental Adaptive CFAR Detector

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Sung Won Hong ◽  
Dong Seog Han
Keyword(s):  
Performance Analysis ◽  
False Alarm ◽  
False Alarm Rate ◽  
Computer Simulations ◽  
Mathematical Analysis ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Constant False Alarm Rate ◽  
Sonar Systems

The constant false alarm rate (CFAR) is a detection algorithm that is generally used in radar or sonar systems, but its performance depends greatly on the environment. This means that the detection performance cannot be satisfied with only a single CFAR detector. This paper evaluates mathematically a proposed environmental adaptive (EA) CFAR detector. The proposed CFAR detector selects an optimal CFAR detector depending on the environment. Computer simulations validate the mathematical analysis and robustness of the detector in homogeneous and nonhomogeneous backgrounds.

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