scholarly journals Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory

2021 ◽  
Vol 7 ◽  
Author(s):  
Cheng Chen ◽  
Li Zhan ◽  
Xiaoxin Pan ◽  
Zhiliang Wang ◽  
Xiaoyu Guo ◽  
...  
Keyword(s):  
Auditory Brainstem Response ◽  
Short Term Memory ◽  
Auditory Brainstem ◽  
Automatic Recognition ◽  
Sampling Point ◽  
Short Term ◽  
Term Memory ◽  
Sampling Points ◽  
Brainstem Response ◽  
Long Short Term Memory

Background: Auditory brainstem response (ABR) testing is an invasive electrophysiological auditory function test. Its waveforms and threshold can reflect auditory functional changes in the auditory centers in the brainstem and are widely used in the clinic to diagnose dysfunction in hearing. However, identifying its waveforms and threshold is mainly dependent on manual recognition by experimental persons, which could be primarily influenced by individual experiences. This is also a heavy job in clinical practice.Methods: In this work, human ABR was recorded. First, binarization is created to mark 1,024 sampling points accordingly. The selected characteristic area of ABR data is 0–8 ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bidirectional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding.Results: The specific structure, related parameters, recognition effect, and noise resistance of the network were explored in 614 sets of ABR clinical data. The results show that the average detection time for each data was 0.05 s, and recognition accuracy reached 92.91%.Discussion: The study proposed an automatic recognition of ABR waveforms by using the BiLSTM-based machine learning technique. The results demonstrated that the proposed methods could reduce recording time and help doctors in making diagnosis, suggesting that the proposed method has the potential to be used in the clinic in the future.

scholarly journals Automatic Recognition of Auditory Brainstem Response Characteristic Waveform based on BiLSTM

2020 ◽  
Author(s):  
Cheng Chen ◽  
Li Zhan ◽  
Xiaoxin Pan ◽  
Zhiliang Wang ◽  
Xiaoyu Guo ◽  
...  
Keyword(s):  
Auditory Brainstem Response ◽  
Short Term Memory ◽  
Response Characteristic ◽  
Disease Diagnosis ◽  
Auditory Brainstem ◽  
Sampling Point ◽  
Sampling Points ◽  
Newborn Hearing ◽  
Brainstem Response ◽  
Characteristic Area

AbstractBackgroundAuditory brainstem response (ABR) test is widely used in newborn hearing screening and hearing disease diagnosis. Identifying and marking are challenging and repetitive tasks because of complex rules of ABR characteristic waveform and interference of background noise.MethodsThis study proposes an automatic method to recognize ABR characteristic waveform. First, binarization is created to mark 1024 sampling points accordingly. The selected characteristic area of ABR data is 0-8ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bi-directional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding.ResultsSpecific structure, related parameters, recognition effect, and noise resistance of network were explored in 614 sets of ABR clinical data, and recognition accuracy of waves I, III, and V can reach 92.91%.DiscussionThus, the proposed method can reduce the repetitive work of doctors and meet accuracy effectively. Therefore, this method has clinical potential.

scholarly journals Automatic Recognition of Arabic Poetry Meter from Speech Signal using Long Short-term Memory and Support Vector Machine

2020 ◽  
Vol 8 (1) ◽  
pp. 50
Author(s):  
Abdulbasit K. Al-Talabani
Keyword(s):  
Support Vector Machine ◽  
Short Term Memory ◽  
Automatic Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Short Term ◽  
Processing Application ◽  
Term Memory ◽  
Arabic Poetry ◽  
Long Short Term Memory

The recognition of the poetry meter in spoken lines is a natural language processing application that aims to identify a stressed and unstressed syllabic pattern in a line of a poem. Stateof-the-art studies include few works on the automatic recognition of Arud meters, all of which are text-based models, and none is voice based. Poetry meter recognition is not easy for an ordinary reader, it is very difficult for the listener and it is usually performed manually by experts. This paper proposes a model to detect the poetry meter from a single spoken line (“Bayt”) of an Arabic poem. Data of 230 samples collected from 10 poems of Arabic poetry, including three meters read by two speakers, are used in this work. The work adopts the extraction of linear prediction cepstrum coefficient and Mel frequency cepstral coefficient (MFCC) features, as a time series input to the proposed long short-term memory (LSTM) classifier, in addition to a global feature set that is computed using some statistics of the features across all of the frames to feed the support vector machine (SVM) classifier. The results show that the SVM model achieves the highest accuracy in the speakerdependent approach. It improves results by 3%, as compared to the state-of-the-art studies, whereas for the speaker-independent approach, the MFCC feature using LSTM exceeds the other proposed models.

scholarly journals Integrating Sequence Learning and Game Theory to Predict Design Decisions Under Competition

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Alparslan Emrah Bayrak ◽  
Zhenghui Sha
Keyword(s):  
Game Theory ◽  
Sequence Learning ◽  
Short Term Memory ◽  
Function Optimization ◽  
Sequential Design ◽  
Sampling Point ◽  
Short Term ◽  
Design Decisions ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Design can be viewed as a sequential and iterative search process. Fundamental understanding and computational modeling of human sequential design decisions are essential for developing new methods in design automation and human–AI collaboration. This paper presents an approach for predicting designers’ future search behaviors in a sequential design process under an unknown objective function by combining sequence learning with game theory. While the majority of existing studies focus on analyzing sequential design decisions from the descriptive and prescriptive point of view, this study is motivated to develop a predictive framework. We use data containing designers’ actual sequential search decisions under competition collected from a black-box function optimization game developed previously. We integrate the long short-term memory networks with the Delta method to predict the next sampling point with a distribution, and combine this model with a non-cooperative game to predict whether a designer will stop searching the design space or not based on their belief of the opponent’s best design. In the function optimization game, the proposed model accurately predicts 82% of the next design variable values and 92% of the next function values in the test data with an upper and lower bound, suggesting that a long short-term memory network can effectively predict the next design decisions based on their past decisions. Further, the game-theoretic model predicts that 60.8% of the participants stop searching for designs sooner than they actually do while accurately predicting when the remaining 39.2% of the participants stop. These results suggest that a majority of the designers show a strong tendency to overestimate their opponents’ performance, leading them to spend more on searching for better designs than they would have, had they known their opponents’ actual performance.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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