[图书][B] The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance
PS Addison - 2017 - taylorfrancis.com
This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Denoising of ECG signals using weighted stationary wavelet total variation
ECG signals capture the electrical activity of the heart and can be used to determine its
health. The P-QRS-T morphological features of the ECG may be used to identify a variety of …
health. The P-QRS-T morphological features of the ECG may be used to identify a variety of …
A new modified wavelet-based ECG denoising
Z Wang, J Zhu, T Yan, L Yang - Computer assisted surgery, 2019 - Taylor & Francis
Purpose: Wavelet denoising is one of the denoising methods commonly used for ECG
signals. However, due to the frequency overlap between the EMG and ECG, the feeble …
signals. However, due to the frequency overlap between the EMG and ECG, the feeble …
Automatic detection of epilepsy and seizure using multiclass sparse extreme learning machine classification
Y Wang, Z Li, L Feng, C Zheng… - … methods in medicine, 2017 - Wiley Online Library
An automatic detection system for distinguishing normal, ictal, and interictal
electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents …
electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents …
Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection
An automatic detection system for distinguishing healthy, ictal, and inter‐ictal EEG signals
plays an important role in medical practice. This paper presents a very large scale …
plays an important role in medical practice. This paper presents a very large scale …
Implementation of a non-linear SVM classification for seizure EEG signal analysis on FPGA
S Shanmugam, S Dharmar - Engineering Applications of Artificial …, 2024 - Elsevier
An automatic detection method for disease diagnosis plays an important role in the medical
field. A computer-aided diagnosis (CAD) system for seizure detection using …
field. A computer-aided diagnosis (CAD) system for seizure detection using …
Improved pipelined wavelet implementation for filtering ECG signals
A Milchevski, M Gusev - Pattern Recognition Letters, 2017 - Elsevier
We present a novel algorithm for digital filtering of an electrocardiogram (ECG) signal
received by both stationary and non-stationary sensors. The basic idea of digital ECG signal …
received by both stationary and non-stationary sensors. The basic idea of digital ECG signal …
Noise reduction of ECG signals through genetic optimized wavelet threshold filtering
The Electrocardiogram (ECG) is a valuable signal recording the heart's electrical activity.
The filtering quality of ECG signals directly affects the medical diagnosis. Since wavelet …
The filtering quality of ECG signals directly affects the medical diagnosis. Since wavelet …
VLSI design of multiclass classification using sparse extreme learning machine for epilepsy and seizure detection
Y Wang, Q Zhou, J Luo, Y Lu, H Wang… - IEICE Electronics …, 2022 - jstage.jst.go.jp
An automatic detection system for distinguishing healthy, ictal, and inter-ictal EEG signals is
of importance in clinical practice. This paper presents a low-complexity three-class …
of importance in clinical practice. This paper presents a low-complexity three-class …
Epileptic seizure classification using gradient tree boosting classifier
MA Tanveer, A Salman - Proceedings of the 2019 9th International …, 2019 - dl.acm.org
Analysis of electroencephalography (EEG) is widely used for the diagnosis of epilepsy in
which relevant information extraction from EEG signals poses great challenge due to noise …
which relevant information extraction from EEG signals poses great challenge due to noise …