Classification cardiac beats using arterial blood pressure signal based on discrete wavelet transform and deep convolutional neural network

2022 ◽  
Vol 71 ◽  
pp. 103131
Author(s):  
Roghayyeh Arvanaghi ◽  
Sebelan Danishvar ◽  
Morad Danishvar
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Discrete Wavelet Transform ◽  
Arterial Blood Pressure ◽  
Deep Convolutional Neural Network ◽  
Discrete Wavelet ◽  
Pressure Signal ◽  
Arterial Blood

APPLICATION OF THE DISCRETE WAVELET TRANSFORM FOR THE ROBUST DETECTION OF THE IMPULSIVE INCIDENCES: APPLICATION TO ARTERIAL BLOOD PRESSURE CHARACTERISTIC EVENTS DETECTION–DELINEATION

2011 ◽  
Vol 09 (05) ◽  
pp. 813-842 ◽  
Author(s):  
MOHAMMAD R. HOMAEINEZHAD ◽  
MOHAMMAD AGHAEE ◽  
HAMID NAJJARAN TOOSI ◽  
ALI GHAFFARI ◽  
REZA RAHMANI
Keyword(s):  
Blood Pressure ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Arterial Blood Pressure ◽  
False Alarm Probability ◽  
Discrete Wavelet ◽  
Robust Detection ◽  
Dicrotic Notch ◽  
Arterial Blood ◽  
Events Detection

The major focus of this study is to describe the structure of a solution designed for robustly detecting and delineating the arterial blood pressure (ABP) signal events. To meet this end, first, the original ABP signal is pre-processed by application of à trous discrete wavelet transform (DWT) for extracting several dyadic scales. Then, a fixed sample size sliding window is moved on the appropriately selected scale and in each slid, six features namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first-order differentiation, summation of absolute second-order differentiation, curve length, area and variance of the excerpted segment are calculated. Then, all feature trends are normalized and utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. After application of an adaptive-nonlinear transformation for making the DS baseline stationary, the histogram parameters of the enhanced DS are used to regulate the α-level Neyman–Pearson classifier for false alarm probability (FAP)-bounded delineation of the ABP events. In order to illustrate the capabilities of the presented algorithm, it was applied to all 18 subjects of the MIT-BIH Polysomnographic Database (359,000 beats) and the end-systolic and end-diastolic locations of the ABP signal as well as dicrotic notch pressure were extracted and values of sensitivity and positive predictivity Se = 99.86% and P+ = 99.95% were obtained for the detection of all ABP events. High robustness against measurement noises, acceptable detection-delineation accuracy of the ABP events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameters dependency to the acquisition sampling frequency can be mentioned as the important merits and capabilities of the proposed PCAGI-based ABP events detection-segmentation algorithm.

CLASSIFICATION OF CARDIAC ARRHYTHMIAS USING ARTERIAL BLOOD PRESSURE BASED ON DISCRETE WAVELET TRANSFORM

2017 ◽  
Vol 29 (05) ◽  
pp. 1750034 ◽  
Author(s):  
Roghayyeh Arvanaghi ◽  
Sabalan Daneshvar ◽  
Hadi Seyedarabi ◽  
Atefeh Goshvarpour
Keyword(s):  
Blood Pressure ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Arterial Blood Pressure ◽  
Cardiac Arrhythmias ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Arterial Blood

Early and correct diagnosis of cardiac arrhythmias is an important step in the treatment of patients. In the recent decades, a wide area of bio-signal processing is allocated to cardiac arrhythmia classification. Unlike other studies, which have employed Electrocardiogram (ECG) signal as a main signal to classify the arrhythmia and sometimes they have used other vital signals as an auxiliary signal to fill missing data and robust detections. In this study, the Arterial Blood Pressure (ABP) is used to classify six types of heart arrhythmias. In other words, in this study for first time, the arrhythmias are classified according ABP signal information. Discrete Wavelet Transform (DWT) is used to de-noise and decompose ABP signal. On feature extraction stage, three types of features including frequency, power, and entropy are extracted. In classification stage, Least Square Support Vector Machine (LS-SVM) is employed as a classifier. The accuracy, sensitivity, and specificity rates of 95.75%, 96.77%, and 96.32% are achieved, respectively. Currently, the classification of cardiac arrhythmias is based on the ABP signal which has some advantages. The recording of ABP signal is done by means of one electrode and therefore it has resulted in lower costs compared with the ECG signal. Finally, it has been shown that ABP has very important and valuable information about the heart performance and can be used in arrhythmia classification.

scholarly journals Hand gesture recognition using discrete wavelet transform and convolutional neural network

2020 ◽  
Vol 9 (3) ◽  
pp. 996-1004 ◽  
Author(s):  
Muhammad Biyan Priatama ◽  
Ledya Novamizanti ◽  
Suci Aulia ◽  
Erizka Banuwati Candrasari
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Discrete Wavelet Transform ◽  
Gesture Recognition ◽  
Public Services ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Discrete Wavelet

Public services are available to all communities including people with disabilities. One obstacle that impedes persons with disabilities from participating in various community activities and enjoying the various public services available to the community is information and communication barriers. One way to communicate with people with disabilities is with hand gestures. Therefore, the hand gesture technology is needed, in order to facilitate the public to interact with the disability. This study proposes a reliable hand gesture recognition system using the convolutional neural network method. The first step, carried out pre-processing, to separate the foreground and background. Then the foreground is transformed using the discrete wavelet transform (DWT) to take the most significant subband. The last step is image classification with convolutional neural network. The amount of training and test data used are 400 and 100 images repectively, containing five classes namely class A, B, C, # 5, and pointing. This study engendered a hand gesture recognition system that had an accuracy of 100% for dataset A and 90% for dataset B.

scholarly journals Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 65-77
Author(s):  
Vadym Slyusar ◽  
Mykhailo Protsenko ◽  
Anton Chernukha ◽  
Stella Gornostal ◽  
Sergey Rudakov ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Discrete Wavelet Transform ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Discrete Wavelet ◽  
Surveillance Systems ◽  
Improved Method ◽  
Aircraft Systems

The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets. The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects

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