scholarly journals Signal Quality Enhancement of Esophageal Pressure Signals in Mechanically Ventilated Patients

2021 ◽  
Author(s):  
Michael Zara
Keyword(s):  
Respiratory Mechanics ◽  
Esophageal Pressure ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Quality ◽  
Mechanically Ventilated ◽  
Mode Decomposition ◽  
Amplitude Fluctuations ◽  
Ventilation Strategies ◽  
Face Validation ◽  
Improve Patient

The Esophageal Pressure (Peso) signal can be used to monitor the respiratory mechanics of critically ill patients in the Intensive Care Unit (ICU), and has been successfully used in guiding mechanical ventilation strategies to improve patient outcomes. However, cardiogenic oscillations (CGOs) are a major source of interference, which not only makes it challenging in interpreting the patient’s respiratory mechanics, but can also cause false triggers in the mechanical ventilator resulting in a patient-ventilator asynchrony. In this thesis, we present a Peso enhancement scheme using Ensemble Empirical Mode Decomposition (EEMD) to suppress CGO interference. The proposed method was applied to synthetically generated Peso signals as well as real-world Peso signals from mechanically ventilated ICU patients. The proposed technique has been shown to significantly reduce the amplitude fluctuations caused by CGOs. The technique’s performance has been assessed through Face Validation by our collaborating clinicians, and is found to be suitable in not only suppressing CGO, but also extracting CGO from clinically acquired Peso signals.

scholarly journals Signal Quality Enhancement of Esophageal Pressure Signals in Mechanically Ventilated Patients

2021 ◽  
Author(s):  
Michael Zara
Keyword(s):  
Respiratory Mechanics ◽  
Esophageal Pressure ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Quality ◽  
Mechanically Ventilated ◽  
Mode Decomposition ◽  
Amplitude Fluctuations ◽  
Ventilation Strategies ◽  
Face Validation ◽  
Improve Patient

The Esophageal Pressure (Peso) signal can be used to monitor the respiratory mechanics of critically ill patients in the Intensive Care Unit (ICU), and has been successfully used in guiding mechanical ventilation strategies to improve patient outcomes. However, cardiogenic oscillations (CGOs) are a major source of interference, which not only makes it challenging in interpreting the patient’s respiratory mechanics, but can also cause false triggers in the mechanical ventilator resulting in a patient-ventilator asynchrony. In this thesis, we present a Peso enhancement scheme using Ensemble Empirical Mode Decomposition (EEMD) to suppress CGO interference. The proposed method was applied to synthetically generated Peso signals as well as real-world Peso signals from mechanically ventilated ICU patients. The proposed technique has been shown to significantly reduce the amplitude fluctuations caused by CGOs. The technique’s performance has been assessed through Face Validation by our collaborating clinicians, and is found to be suitable in not only suppressing CGO, but also extracting CGO from clinically acquired Peso signals.

Auto-PEEP effects on respiratory mechanics and blood gases in mechanically ventilated patients

1998 ◽  
Vol 74 (4) ◽  
pp. 275-83
Author(s):  
Antônio C. P. Ferreira ◽  
Benjamin I. Kopelman ◽  
Werther Brunow de Carvalho ◽  
Jorge Bonassa
Keyword(s):  
Respiratory Mechanics ◽  
Blood Gases ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Ventilated Patients

scholarly journals Analysis method of the cavitation vibration signals in poppet valve based on EEMD

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

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