Browsing Faculty Contributions by Subject "Biomedical signal processing"
Now showing items 1-14 of 14
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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 ... -
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 ... -
Calibration of time features and frequency features in the time-frequency domain for improved detection and classification of seizure in newborn EEG signals
( IEEE Computer Society , 2012 , Conference Paper)This paper presents new time-frequency features for seizure detection in newborn EEG signals. These features are obtained by calibrating relevant time features and frequency features in the joint time-frequency domain. The ... -
Long-term epileptic EEG classification via 2D mapping and textural features
( Elsevier Ltd , 2015 , Article)Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic ... -
Multimodal deep learning approach for Joint EEG-EMG Data compression and classification
( Institute of Electrical and Electronics Engineers Inc. , 2017 , Conference Paper)In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep autoencoder architecture which is designed ... -
Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other ... -
On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals
( Institute of Electrical and Electronics Engineers Inc. , 2014 , Conference Paper)This paper proposes new time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals. These features are obtained by translating and combining the most relevant time-domain ... -
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). ... -
Real-time phonocardiogram anomaly detection by adaptive 1D Convolutional Neural Networks
( Elsevier B.V. , 2020 , Article)The heart sound signals (Phonocardiogram ? PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnostic test in outpatient monitoring to assess ... -
Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network
( IEEE Computer Society , 2022 , Article)Objective: Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak ... -
Sleep stage classification using sparse rational decomposition of single channel EEG records
( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference Paper)A sparse representation of ID signals is proposed based on time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands ... -
Time-frequency features for pattern recognition using high-resolution TFDs: A tutorial review
( Elsevier Inc. , 2015 , Article)This paper presents a tutorial review of recent advances in the field of time-frequency (t, f) signal processing with focus on exploiting (t, f) image feature information using pattern recognition techniques for detection ... -
Time-frequency image descriptors-based features for EEG epileptic seizure activities detection and classification
( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference Paper)This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived ...