Correlation analysis for abnormal ECG signal features extraction

2003 ◽  
Author(s):  
A.B. Ramli ◽  
P.A. Ahmad
Keyword(s):  
Correlation Analysis ◽  
Features Extraction ◽  
Ecg Signal ◽  
Signal Features

ECG signal features extraction

2016 ◽  
Author(s):  
Marco V. Gualsaqui Miranda ◽  
Ivan P. Vizcaino Espinosa ◽  
Marco J. Flores Calero
Keyword(s):  
Features Extraction ◽  
Ecg Signal ◽  
Signal Features

scholarly journals ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Carlos Lastre-Domínguez ◽  
Yuriy S. Shmaliy ◽  
Oscar Ibarra-Manzano ◽  
Jorge Munoz-Minjares ◽  
Luis J. Morales-Mendoza
Keyword(s):  
Linear Prediction ◽  
Finite Impulse Response ◽  
Features Extraction ◽  
P Wave ◽  
Ecg Signal ◽  
Linear Predictor ◽  
Ecg Signals ◽  
Signal Features ◽  
Ecg Signal Processing ◽  
Standard Linear

Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

scholarly journals Evaluation of speech signal features extraction methods

2020 ◽  
Vol 2 (1) ◽  
pp. 69-78
Author(s):  
Ismail Shayeb ◽  
Naseem Asad ◽  
Ziad Alqadi ◽  
Qazem Jaber
Keyword(s):  
Speech Signal ◽  
High Speed ◽  
Research Paper ◽  
Extraction Methods ◽  
Features Extraction ◽  
Experimental Results ◽  
Extraction Time ◽  
Digital Signals ◽  
Human Speech ◽  
Signal Features

Human speech digital signals are famous and important digital types, they are used in many vital applications which require a high speed processing, so creating a speech signal features is a needed issue. In this research paper we will study more widely used methods of features extraction, we will implement them, and the obtained experimental results will be compared, efficiency parameters such as extraction time and throughput will be obtained and a speedup of each method will be calculated. Speech signal histogram will be used to improve some methods efficiency.

Wireless Heart Abnormality Monitoring Kit Based on Raspberry Pi

2018 ◽  
Vol 35 ◽  
pp. 96-108
Author(s):  
Khudhur A. Alfarhan ◽  
Mohd Yusoff Mashor ◽  
Abdul Rahman Mohd Saad ◽  
Mohammad Iqbal Omar
Keyword(s):  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Features Extraction ◽  
Raspberry Pi ◽  
Support Vector ◽  
Ecg Signal ◽  
Statistical Features ◽  
Classification Approach ◽  
Ecg Signals ◽  
Heart Monitoring

Heart monitoring kits are only available for bedridden patients and the traditional heart monitoring kits have many wires that are obstacle patients’ mobility. Most of the existing heart monitoring kits can not detect heart diseases. Thus, the current study proposed a wireless heart monitoring kit to monitor patients with a heart abnormality. The proposed kit can detect and classify four arrhythmia types as well as normal ECG with high accuracy. The design and development of the wireless heart abnormality monitoring kit (WHAMK) in this research were divided into three stages. These stages are the development of an arrhythmias detection and classification method using artificial intelligence approach, design and implementation of the kit hardware, and design and coding of the kit software. Arrhythmias classification approach is divided into four stages, namely obtaining the electrocardiograph (ECG) signals, preprocessing, features extraction and classification. The features extraction method are based on statistical features. The library support vector machine (LIBSVM) was used to classify the ECG signals. The hardware of the kit is divided into two parts, namely ECG body sensor (EBS), and processing and displaying unit (PDU). EBS working on acquiring the ECG signal from patient's body. PDU working on processing the collected ECG signal, plotting it and detecting the arrhythmias. Arrhythmias classification approach was developed by using statistical features and LIBSVM. They were implemented in the software of the kit to enable it to detect the arrhythmias in the real-time and fully automatically. The kit can detect and classify four arrhythmia types as well as normal sinus rhythm (NSR). These types of arrhythmia are premature atrial contraction (PAC), premature ventricles contraction (PVC), Bradycardia and Tachycardia. The proposed kit gave a good accuracy for detecting and classifying Arrhythmia with the overall accuracy of 96.2%.

Analysis of Digital Signal Features Extraction Based on LBP Operator

IJARCCE ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1-7
Author(s):  
Dr. Ghazi M. Qaryouti ◽  
Prof. Yousf Eltous ◽  
Prof. Mohammad Abuzalata
Keyword(s):  
Digital Signal ◽  
Features Extraction ◽  
Signal Features

scholarly journals A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhaowen Chen ◽  
Ning Gao ◽  
Wei Sun ◽  
Qiong Chen ◽  
Fengying Yan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Vibration Signal ◽  
Classification Performance ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Structuring Element ◽  
Signal Features ◽  
Cause Of Failure ◽  
Nonlinear Signal

Mathematical morphology (MM) is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE). Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.

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