Muscle artifact cancellation in ECG signal using a dynamical model and particle filter

2015 ◽  
Author(s):  
Hamed Danandeh Hesar ◽  
Maryam Mohebbi
Keyword(s):  
Particle Filter ◽  
Dynamical Model ◽  
Ecg Signal

A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising

2020 ◽  
Vol 39 (5) ◽  
pp. 6773-6782
Author(s):  
Snekha Thakran
Keyword(s):  
Particle Filter ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Second Phase ◽  
Ecg Signal ◽  
Threshold Method ◽  
Two Phase ◽  
Nonlocal Means ◽  
Mode Decomposition ◽  
Fuzzy Thresholding

The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed into a true intrinsic mode function (IMFs) with the help of EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter).

scholarly journals Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Wang ◽  
Yafeng Yin ◽  
Hong Man
Keyword(s):  
Gaussian Process ◽  
Particle Filter ◽  
Dynamical Model ◽  
Human Tracking ◽  
Gaussian Process Dynamical Model

A New Approach Based on Dynamical Model of The ECG Signal to Blood Pressure Estimation

2019 ◽  
Author(s):  
Seyedeh Somayyeh Mousavi ◽  
Mostafa Charmi ◽  
Mohammad Firouzmand ◽  
Mohammad Hemmati ◽  
Maryam Moghadam
Keyword(s):  
Blood Pressure ◽  
Dynamical Model ◽  
Ecg Signal ◽  
New Approach ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising

2021 ◽  
pp. 1-11
Author(s):  
Rajiv Kapoor ◽  
Rajesh Birok
Keyword(s):  
Particle Filter ◽  
Ecg Signal

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function

Study on Cuff-Less Blood Pressure Measurement Based on Electrocardiography and Photoplethysmography Signal

2019 ◽  
Vol 2 (3) ◽  
pp. 206-214
Author(s):  
Putri Indes Oktabriani ◽  
Fuad Ughi ◽  
Aulia Arif Iskandar
Keyword(s):  
Blood Pressure ◽  
Pressure Measurement ◽  
Blood Pressure Monitoring ◽  
Blood Pressure Measurement ◽  
Ecg Signal ◽  
Optimum Location ◽  
Blood Pressure Monitor ◽  
Pressure Monitor ◽  
Left Hand ◽  
Measure Blood Pressure

The continuous blood pressure measurement research is widely known for helpingthe development of ambulatory blood pressure monitoring where it measures blood pressureevery 15 to 30 minutes throughout the day. The cuff is a problem for the patient withAmbulatory Blood Pressure Monitor. It can make a person feel uncomfortable and must staystill when the cuff starts to inflate. It is limiting and disturbing their daily activity when thedevice is starting to measure the blood pressure. Blood pressure measurement without cuff isbeing proposed in this research, called cuff-less blood pressure measurement. It will be based onPhotoplethysmography (PPG) and Electrocardiography (ECG) signal analysis. ECG (Lead 1,Lead 2, and Lead 3) with PPG signal produced from index finger on the left hand are comparedand analyzed. Then the relation of PPG and ECG signal and the optimum location for daily usecan be obtained. The optimum location will be based on the electrode’s position that producedthe optimum ECG lead Signal to measure blood pressure. Based on the result, PPG and ECGsignal have a linear relation with Blood Pressure Measurement and Lead 1 is more stable inproducing the ECG signal. The equation from Lead 1 appeared as one of the optimum equationsfor measuring Systolic Blood Pressure (SBP) or Diastolic Blood Pressure (DBP).

Sign in / Sign up

Export Citation Format

Share Document