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Now showing items 11-14 of 14
Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study
(
Elsevier B.V.
, 2016 , Article)
Time-frequency (TF) based machine learning methodologies can improve the design of classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF feature extraction is performed on multi-channel ...
Principles of time-frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection
(
Elsevier Ltd
, 2015 , Article)
This paper considers the general problem of detecting change in non-stationary signals using features observed in the time-frequency (t,f) domain, obtained using a class of quadratic time-frequency distributions (QTFDs). ...
A review of time-frequency matched filter design with application to seizure detection in multichannel newborn EEG
(
Elsevier Inc.
, 2014 , Article)
This paper presents a novel design of a time-frequency (t-f) matched filter as a solution to the problem of detecting a non-stationary signal in the presence of additive noise, for application to the detection of newborn ...
An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
(
Hindawi Limited
, 2017 , Article)
The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor ...