Baseline normalisation of ECG signals using empirical mode decomposition and mathematical morphology

2008 ◽  
Vol 44 (2) ◽  
pp. 82 ◽  
Author(s):  
T.Y. Ji ◽  
Z. Lu ◽  
Q.H. Wu ◽  
Z. Ji
Keyword(s):  
Empirical Mode Decomposition ◽  
Mathematical Morphology ◽  
Ecg Signals ◽  
Mode Decomposition

APPLICATION OF EMPIRICAL MODE DECOMPOSITION–BASED FEATURES FOR ANALYSIS OF NORMAL AND CAD HEART RATE SIGNALS

2016 ◽  
Vol 16 (01) ◽  
pp. 1640002 ◽  
Author(s):  
SURABHI SOOD ◽  
MOHIT KUMAR ◽  
RAM BILAS PACHORI ◽  
U. RAJENDRA ACHARYA
Keyword(s):  
Heart Rate ◽  
Empirical Mode Decomposition ◽  
P Value ◽  
Computer Assisted ◽  
Intrinsic Mode Functions ◽  
Mean Frequency ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Artery Disease ◽  
Assisted Diagnosis

Coronary Artery Disease (CAD) is a heart disease caused due to insufficient supply of nutrients and oxygen to the heart muscles. Hence, reduced supply of nutrients and oxygen causes heart attack or stroke and may cause death. Also significant number of people are suffering from CAD around the world so timely diagnosis of CAD can save the life of patients. In this work, we have proposed computer assisted diagnosis of CAD using Heart Rate (HR) signals obtained from Electrocardiogram (ECG) signals. We have used the Empirical Mode Decomposition (EMD) technique to process the HR signals. The features namely: Second-Order Difference Plot (SODP) area, Analytic Signal Representation (ASR) area, Amplitude Modulation (AM) bandwidth, Frequency Modulation (FM) bandwidth and Fourier–Bessel expansion (FBE)- based mean frequency computed from the Intrinsic Mode Functions (IMFs) are extracted to discriminate normal and CAD subjects. Thereafter, Kruskal–Wallis statistical test is performed on these features. The features having p-value less than 0.05 are considered to be significant. Our results show that three features namely: AM bandwidth, FM bandwidth and FBE-based mean frequency are more suitable than ASR area and SODP area features for discrimination of normal and CAD subjects.

scholarly journals Learnable Empirical Mode Decomposition based on Mathematical Morphology

2022 ◽  
Vol 15 (1) ◽  
pp. 23-44
Author(s):  
Santiago Velasco-Forero ◽  
R. Pagès ◽  
Jesus Angulo
Keyword(s):  
Empirical Mode Decomposition ◽  
Mathematical Morphology ◽  
Mode Decomposition

scholarly journals Follicle Detection in Digital Ultrasound Images using Bi-Dimensional Empirical Mode Decomposition and Mathematical Morphology

2018 ◽  
Vol 8 (4) ◽  
Author(s):  
M. Jayanthi Rao ◽  
Dr. R. Kiran Kumar
Keyword(s):  
Empirical Mode Decomposition ◽  
Mathematical Morphology ◽  
Human Body ◽  
Polycystic Ovary ◽  
Human Reproduction ◽  
Diagnostic Process ◽  
Ultrasound Images ◽  
Body Parts ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

Ultrasound Imaging is one of the technique used to study inside human body with images generated using high frequency sounds waves. The applications of ultrasound images include examination of human body parts such as Kidney, Liver, Heart and Ovaries. This paper mainly concentrates on ultrasound images of ovaries. The detection of follicles in ultrasound images of ovaries is concerned with the follicle monitoring during the diagnostic process of infertility treatment of patients.Monitoring of follicle is important in human reproduction. This paper presents a method for follicle detection in ultrasound images using Bidimensional Empirical Mode Decomposition and Mathematical morphology. The proposed algorithm is tested on sample ultrasound images of ovaries for identification of follicles and classifies the ovary into three categories, normal ovary, cystic ovary and polycystic ovary. The experiment results are compared qualitatively with inferences drawn by medical expert manually and this data can be used to classify the ovary images.

Time-frequency analysis of a noised ECG signals using empirical mode decomposition and Choi-Williams techniques

2013 ◽  
Vol 5 (3/4) ◽  
pp. 231
Author(s):  
Azzedine Dliou ◽  
Rachid Latif ◽  
Mostafa Laaboubi ◽  
Fadel Mrabih Rabou Maoulainine ◽  
Samir Elouaham
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Ecg Signals ◽  
Mode Decomposition
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