scholarly journals Seizure Prediction Using Bidirectional LSTM

2019 ◽  
pp. 349-356
Author(s):  
Hazrat Ali ◽  
Feroz Karim ◽  
Junaid Javed Qureshi ◽  
Adnan Omer Abuassba ◽  
Mohammad Farhad Bulbul
Keyword(s):  
Seizure Prediction ◽  
Bidirectional Lstm

A Statistical Seizure Prediction Approach Based on Savitzky-Golay Smoothing

2018 ◽  
Vol 27 (1) ◽  
pp. 53-70
Author(s):  
Ahmed Sedik ◽  
Turky Alotaiby ◽  
Heba El-Khobby ◽  
Mahmoud Atea ◽  
Saleh A. Alshebeili ◽  
...  
Keyword(s):  
Seizure Prediction ◽  
Prediction Approach

BuTTER: BidirecTional LSTM for Food Named-Entity Recognition

2020 ◽  
Author(s):  
Gjorgjina Cenikj ◽  
Gorjan Popovski ◽  
Riste Stojanov ◽  
Barbara Korousic Seljak ◽  
Tome Eftimov
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Bidirectional Lstm

scholarly journals A Review on Wearable Epileptic Seizure Prediction System

2021 ◽  
Vol 1916 (1) ◽  
pp. 012075
Author(s):  
V Seethalakshmi ◽  
P Naveenkumar ◽  
G Kavin Prabu ◽  
S Praveen Kumaar
Keyword(s):  
Epileptic Seizure ◽  
Prediction System ◽  
Seizure Prediction ◽  
Epileptic Seizure Prediction

Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region

2020 ◽  
Vol 65 (6) ◽  
pp. 705-720
Author(s):  
Aarti Sharma ◽  
Jaynendra Kumar Rai ◽  
Ravi Prakash Tewari
Keyword(s):  
Right Hemisphere ◽  
Moving Average ◽  
Pearson Correlation ◽  
Relative Energy ◽  
Automated System ◽  
Temporal Region ◽  
Eeg Signal ◽  
Seizure Prediction ◽  
Prediction Time ◽  
Average Filter

AbstractEpilepsy is characterized by uncontrollable seizure during which consciousness of patient is disturbed. Prediction of the seizure in advance will increase the remedial possibilities for the patients suffering from epilepsy. An automated system for seizure prediction is important for seizure enactment, prevention of sudden unexpected deaths and to avoid seizure related injuries. This paper proposes the prediction of an upcoming seizure by analyzing the 23 channel non-stationary EEG signal. EEG signal is divided into smaller segments to change it into quasi-stationary data using an overlapping moving window. Brain region is marked into four regions namely left hemisphere, right hemisphere, central region and temporal region to identify the epileptogenic region. The epileptogenic region shows significant variations during pre-ictal state in comparison to the other regions. So, seizure prediction is carried out by analyzing EEG signals from this region. Seizure prediction is proposed using features extracted from both time and frequency domain. Relative entropy and relative energy are extracted from wavelet transform and Pearson correlation coefficient is obtained from time domain EEG signal. Extracted features have been smoothened using moving average filter. First order derivative of relative features have been used to normalize the intervariability before deciding the threshold for marking the prediction of seizure. Isolated seizures where pre-ictal duration of more than 1 h is reported has been detected with an accuracy of 92.18% with precursory warning 18 min in advance and seizure confirmation 12 min in advance. An overall accuracy of 83.33% with false positive alarm rate of 0.01/h has been obtained for all seizure cases with average prediction time of 9.9 min.

Pedestrian Stride-Length Estimation Based on Bidirectional LSTM Network

2020 ◽  
Author(s):  
Zhang Ping ◽  
Meng Zhidong ◽  
Wang Pengyu ◽  
Deng Zhihong
Keyword(s):  
Stride Length ◽  
Length Estimation ◽  
Bidirectional Lstm ◽  
Lstm Network

BoydCut: Bidirectional LSTM-CNN Model for Thai Sentence Segmenter

2020 ◽  
Author(s):  
Sorratat Sirirattanajakarin ◽  
Duangjai Jitkongchuen ◽  
Peerasak Intarapaiboon
Keyword(s):  
Bidirectional Lstm

scholarly journals Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kilin Shi ◽  
Tobias Steigleder ◽  
Sven Schellenberger ◽  
Fabian Michler ◽  
Anke Malessa ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Dynamic Change ◽  
Cold Pressor Test ◽  
Cold Pressor ◽  
Contactless Measurement ◽  
Pressor Test ◽  
Skin Contact ◽  
Bidirectional Lstm ◽  
Lstm Network

AbstractContactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.

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