scholarly journals Advanced Fusion and Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 368
Author(s):  
Christina Kozia ◽  
Randa Herzallah
Keyword(s):  
Empirical Mode Decomposition ◽  
Absolute Error ◽  
Invasive Technique ◽  
Breathing Rate ◽  
Non Invasive ◽  
Use Of Data ◽  
Mode Decomposition ◽  
Novel Method ◽  
The Individual ◽  
First Time

Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations transmitted to the chest-wall, can be used as a non-invasive technique to estimate the BR. This makes SCG signals a highly appealing way for estimating the BR. As such, this work proposes three novel methods for extracting the BR from SCG signals. The first method is based on extracting respiration-dependent features such as the fundamental heart sound components, S1 and S2 from the SCG signal. The second novel method investigates for the first time the use of data driven methods such as the Empirical Mode Decomposition (EMD) method to identify the respiratory component from an SCG signal. Finally, the third advanced method is based on fusing frequency information from the respiration signals that result from the aforementioned proposed methods and other standard methods. The developed methods in this paper are then evaluated on adult recordings from the combined measurement of ECG, the Breathing and Seismocardiograms database. Both fusion and EMD filter-based methods outperformed the individual methods, giving a mean absolute error of 1.5 breaths per minute, using a one-minute window of data.

Electronic Stool (e-Stool): A Novel Self-Stabilizing Video Capsule Endoscope for Reliable Non-Invasive Colonic Imaging

2011 ◽  
Author(s):  
Dobromir Filip ◽  
Marjan Eggermont ◽  
Jacquelyn Nagel ◽  
C. N. Andrews ◽  
Orly Yadid-Pecht ◽  
...  
Keyword(s):  
Final Stage ◽  
Invasive Technique ◽  
Gi Tract ◽  
Expansion Process ◽  
Natural Processes ◽  
Non Invasive ◽  
Novel Method ◽  
Biomimetic Approach ◽  
Capsule Size ◽  
Human Stool

Video capsule endoscopy (VCE) has become a popular non-invasive technique to study the small intestine. However, colonic VCE has been problematic due to capsule tumbling in the larger lumen of this organ. Self-stabilizing VCE is a novel method to visualize the colon without tumbling utilizing a biomimetic approach. The proposed design uses the free energy of the body’s natural processes employed to move chyme, and imitates the formation and propagation of stool. In its final stage, it physically and mechanically mimics natural feces. The process starts by administering the capsule orally. The capsule size, shape, and material were chosen to provide a smooth transit throughout the gastrointestinal (GI) tract. Once it reaches the colon, its special outer casing enzymatically dissolves. A stabilizing component that is attached to the back end of the capsule starts quickly expanding in the cecum by osmosis. This increase of the volumetric size of the expandable component (stabilizing component) invokes natural peristalsis by colonic mass reflex. Since the expansion process takes place very quickly, the capsule gets stabilized before the expansion-provoked peristalsis starts. At the final stage, the artificially created expanded component (behaving like an artificial stool) centralizes the capsule during its voyage in the colon, allowing a very smooth transit due to its viscosity. The aim of the present study is to present the design of the capsule from a biomimetic perspective and to comparatively quantify the mechanical properties of the design with those of actual human stool.

Empirical Mode Decomposition on Geometry Signal Based Spherical Parameterization

2014 ◽  
Vol 998-999 ◽  
pp. 860-863
Author(s):  
Jian Guo Wang ◽  
Qun E ◽  
Ke Ming Yao ◽  
Xin Long Wan
Keyword(s):  
Empirical Mode Decomposition ◽  
Main Idea ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
Spherical Parameterization ◽  
Novel Method ◽  
Key Steps

A novel method based onEmpirical Mode Decomposition(EMD) is approached to process the geometry signal. The main idea is to decompose the signal into some different detail components called Intrinsic Mode Function (IMF). The key steps are as follows: First, the signal is spherical parameterization; Second it is transformed into the plane signal and sampled regularly; Third, the translated signal is processed as an image using Bid-Empirical Mode Decomposition, getting several image IMFs; Finally, invert mapping these IMFs to geometry signal and getting the geometry signal’s IMFs.We demonstrate the power of the algorithms through a number of application examples including de-noising and enhancement.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

Purification of Axis Trace by Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 791-793 ◽  
pp. 1006-1009
Author(s):  
Jia Xing Zhu ◽  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Prior Knowledge ◽  
Empirical Mode Decomposition ◽  
Simple Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
New Method ◽  
Intrinsic Mode Functions ◽  
Mode Function ◽  
Mode Decomposition ◽  
Novel Method

Aiming at the purification of axis trace, a novel method was proposed by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a collection of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose intrinsic mode function components and reconstructed the signal. Finally the purification of axis trace was obtained. Simulation and practical results show the advantage of ensemble empirical mode decomposition. This method also has simple algorithm and high calculating speed; it provides a new method for purification of axis trace.

scholarly journals Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

2016 ◽  
Author(s):  
Jingjing He ◽  
Yibin Zhou ◽  
Xuefei Guan ◽  
Wei Zhang ◽  
Wei Fang Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Stress Responses ◽  
Numerical Study ◽  
Reconstruction Method ◽  
Fiber Sensors ◽  
Mode Decomposition ◽  
Structural Responses ◽  
Novel Method ◽  
Strain Stress ◽  
Increasing Demand

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

Deuterium labeling enables non-invasive 3D proton MR imaging of glucose and neurotransmitter metabolism in the human brain

2021 ◽  
Author(s):  
Petr Bednarik ◽  
Dario Goranovic ◽  
Alena Svátková ◽  
Fabian Niess ◽  
Lukas Hingerl ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
Brain Diseases ◽  
Gamma Aminobutyric Acid ◽  
Deuterium Labeling ◽  
Non Invasive ◽  
Positron Emission ◽  
Novel Method ◽  
Specialized Hardware ◽  
First Time ◽  
Higher Sensitivity

Abstract Impaired brain glucose metabolism characterizes most severe brain diseases. Recent studies have proposed deuterium (2H)-Magnetic Resonance Spectroscopic Imaging (MRSI) as a reliable, non-invasive, and safe method to quantify the human metabolism of 2H-labeled substrates such as glucose and their downstream metabolism (e.g., aerobic/anaerobic glucose utilization and neurotransmitter synthesis) and address the major drawbacks of positron emission tomography (PET) or carbon (13C)-MRS. Here, for the first time, we show an indirect dynamic proton (1H)-MRSI technique in humans, which overcomes four critical 2H-MRSI limitations. Our innovative approach provides higher sensitivity with improved spatial/temporal resolution and higher chemical specificity to differentiate glutamate (Glu4), glutamine (Gln4), and gamma-aminobutyric acid (GABA2) deuterated at specific molecular positions while allowing simultaneous mapping of both labeled and unlabeled metabolites without the need for specialized hardware. Our novel method demonstrated significant Glu4, Gln4, and GABA2 decreases, with 18% faster Glu4 reduction in the gray matter than white matter after ingestion of deuterated glucose. Thus, robustly detected downstream glucose metabolism utilizing clinically available MR hardware without the need for radioactive tracers and PET.

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