Audio-Visual TV Broadcast Signal Segmentation

2019 ◽  
pp. 221-228
Author(s):  
Josef Chaloupka
Keyword(s):  
Signal Segmentation

scholarly journals Automated Heart Sound Signal Segmentation and Identification using Abrupt Changes and Peak Finding Detection

2021 ◽  
Vol 179 ◽  
pp. 260-267
Author(s):  
Norezmi Jamal ◽  
Nabilah Ibrahim ◽  
MNAH Sha’abani ◽  
Farhanahani Mahmud ◽  
N. Fuad
Keyword(s):  
Heart Sound ◽  
Sound Signal ◽  
Signal Segmentation ◽  
Peak Finding ◽  
Abrupt Changes ◽  
Heart Sound Signal

A biomedical signal segmentation algorithm for event detection based on slope tracing

2009 ◽  
Author(s):  
Jungkuk Kim ◽  
Minkyu Kim ◽  
Injae Won ◽  
Seungyhul Yang ◽  
Kiyoung Lee ◽  
...  
Keyword(s):  
Event Detection ◽  
Segmentation Algorithm ◽  
Biomedical Signal ◽  
Signal Segmentation

Aircraft class identification based on take-off noise signal segmentation in time

2013 ◽  
Vol 40 (13) ◽  
pp. 5148-5159 ◽  
Author(s):  
Luis Alejandro Sánchez-Pérez ◽  
Luis Pastor Sánchez-Fernández ◽  
Sergio Suárez-Guerra ◽  
José Juan Carbajal-Hernández
Keyword(s):  
Noise Signal ◽  
Signal Segmentation ◽  
Class Identification

EMG Signal Segmentation to Predict Driver’s Vigilance State

2021 ◽  
pp. 29-42
Author(s):  
N. A. Ab. Rahman ◽  
M. Mustafa ◽  
N. Sulaiman ◽  
R. Samad ◽  
N. R. H. Abdullah
Keyword(s):  
Signal Segmentation ◽  
Emg Signal ◽  
Vigilance State

scholarly journals Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

2020 ◽  
Vol 10 (4) ◽  
pp. 220 ◽  
Author(s):  
Nicolina Sciaraffa ◽  
Manousos A. Klados ◽  
Gianluca Borghini ◽  
Gianluca Di Flumeri ◽  
Fabio Babiloni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Double Step ◽  
Optimal Length ◽  
Signal Segmentation ◽  
High Frequency Oscillations ◽  
Specificity And Sensitivity ◽  
Step Procedure ◽  
Binary Classifiers

The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data.

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