A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals
The seminal work on Affective Computing in 1995 by Picard set the base for computing that
relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field …
relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field …
Evolution, current challenges, and future possibilities in ECG biometrics
Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising
reliable recognition in diverse applications. Commercial products using these traits for …
reliable recognition in diverse applications. Commercial products using these traits for …
A novel deep learning technique for morphology preserved fetal ECG extraction from mother ECG using 1D-CycleGAN
The non-invasive fetal electrocardiogram (fECG) enables easy detection of developing heart
abnormalities, leading to a significant reduction in infant mortality rate and post-natal …
abnormalities, leading to a significant reduction in infant mortality rate and post-natal …
Study of the few-shot learning for ECG classification based on the PTB-XL dataset
The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists
of P, QRS, and T waves. Information provided from the signal based on the intervals and …
of P, QRS, and T waves. Information provided from the signal based on the intervals and …
Deep learning techniques in the classification of ECG signals using R-peak detection based on the PTB-XL dataset
Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the
application of which in electrocardiographic signals is gaining importance. So far, limited …
application of which in electrocardiographic signals is gaining importance. So far, limited …
Robust ECG R-peak detection using LSTM
Detecting QRS complexes or R-peaks from the electrocardiogram (ECG) is the basis for
heart rate determination and heart rate variability analysis. Over the years, multiple different …
heart rate determination and heart rate variability analysis. Over the years, multiple different …
Check your biosignals here: A new dataset for off-the-person ECG biometrics
Abstract The Check Your Biosignals Here initiative (CYBHi) was developed as a way of
creating a dataset and consistently repeatable acquisition framework, to further extend …
creating a dataset and consistently repeatable acquisition framework, to further extend …
Augmenting ECG data with multiple filters for a better emotion recognition system
MA Hasnul, NA Ab. Aziz, A Abd. Aziz - Arabian Journal for Science and …, 2023 - Springer
A physiological-based emotion recognition system (ERS) with a unimodal approach such as
an electrocardiogram (ECG) is not as popular compared to a multimodal approach …
an electrocardiogram (ECG) is not as popular compared to a multimodal approach …
ECG signals for biometric applications-are we there yet?
The potential of the electrocardiographic (ECG) signal as a biometric trait has been
ascertained in the literature over the past decade. The inherent characteristics of the ECG …
ascertained in the literature over the past decade. The inherent characteristics of the ECG …
A deep learning–based ppg quality assessment approach for heart rate and heart rate variability
Photoplethysmography (PPG) is a non-invasive optical method to acquire various vital signs,
including heart rate (HR) and heart rate variability (HRV). The PPG method is highly …
including heart rate (HR) and heart rate variability (HRV). The PPG method is highly …