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Multimodal deep learning approach for Joint EEG-EMG Data compression and classification
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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 ...
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 ...
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 ...
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 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 ...
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 ...
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 ...
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 ...
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 ...
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 ...