Data preprocessing for heart disease classification: A systematic literature review
H Benhar, A Idri, JL Fernández-Alemán - Computer Methods and Programs …, 2020 - Elsevier
Context Early detection of heart disease is an important challenge since 17.3 million people
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
[HTML][HTML] Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade
Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …
biological processes and electrical activity of heart muscles. Machine learning and deep …
ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network
J Huang, B Chen, B Yao, W He - IEEE access, 2019 - ieeexplore.ieee.org
The classification of electrocardiogram (ECG) signals is very important for the automatic
diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of …
diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of …
A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification
Ö Yildirim - Computers in biology and medicine, 2018 - Elsevier
Long-short term memory networks (LSTMs), which have recently emerged in sequential data
analysis, are the most widely used type of recurrent neural networks (RNNs) architecture …
analysis, are the most widely used type of recurrent neural networks (RNNs) architecture …
[HTML][HTML] ECG-based heartbeat classification for arrhythmia detection: A survey
An electrocardiogram (ECG) measures the electric activity of the heart and has been widely
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection …
This study proposes a new model which is fully specified for automated seizure onset
detection and seizure onset prediction based on electroencephalography (EEG) …
detection and seizure onset prediction based on electroencephalography (EEG) …
[HTML][HTML] EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier
Emotion recognition by artificial intelligence (AI) is a challenging task. A wide variety of
research has been done, which demonstrated the utility of audio, imagery, and …
research has been done, which demonstrated the utility of audio, imagery, and …
ECG classification using wavelet packet entropy and random forests
T Li, M Zhou - Entropy, 2016 - mdpi.com
The electrocardiogram (ECG) is one of the most important techniques for heart disease
diagnosis. Many traditional methodologies of feature extraction and classification have been …
diagnosis. Many traditional methodologies of feature extraction and classification have been …
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
In this study, three popular signal processing techniques (Empirical Mode Decomposition,
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Ensemble SVM method for automatic sleep stage classification
E Alickovic, A Subasi - IEEE Transactions on Instrumentation …, 2018 - ieeexplore.ieee.org
Sleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep
disorders. Automated sleep scoring is crucial, since the large volume of data should be …
disorders. Automated sleep scoring is crucial, since the large volume of data should be …