Analysis of ECG Signal Using WP-HH Transform

This paper introduces a method of ECG signal de noising using Hilbert Huang and Wavelet Packet Transform. Both HHT and WPT are signal processing method. Wavelet Packet transforms is used to decompose the electrocardiogram signal into a set of narrow band signals. Later the suitable threshold value is selected for each decomposed components. For those components for which wavelet coefficients are larger than the threshold value, HHT is applied. This method removes the noise as well as base line wonder effect from ECG signal and reduces the computing quantities and the decomposition layers of EMD. Paper includes features extraction method using ECG signal (IF, mean frequency, phase) which are useful to discriminate normal and abnormal signal. The simulation result indicates the proposed method is very effective as compared to other methods

Statistical Properties and Applications of Empirical Mode Decomposition

Signal analysis is key to extracting information buried in noise. The decomposition of signal is a data analysis tool for determining the underlying physical components of a processed data set. However, conventional signal decomposition approaches such as wavelet analysis, Wagner-Ville, and various short-time Fourier spectrograms are inadequate to process real world signals. Moreover, most of the given techniques require \emph{a prior} knowledge of the processed signal, to select the proper decomposition basis, which makes them improper for a wide range of practical applications. Empirical Mode Decomposition (EMD) is a non-parametric and adaptive basis driver that is capable of breaking-down non-linear, non-stationary signals into an intrinsic and finite components called Intrinsic Mode Functions (IMF). In addition, EMD approximates a dyadic filter that isolates high frequency components, e.g. noise, in higher index IMFs. Despite of being widely used in different applications, EMD is an ad hoc solution. The adaptive performance of EMD comes at the expense of formulating a theoretical base. Therefore, numerical analysis is usually adopted in literature to interpret the behavior. This dissertation involves investigating statistical properties of EMD and utilizing the outcome to enhance the performance of signal de-noising and spectrum sensing systems. The novel contributions can be broadly summarized in three categories: a statistical analysis of the probability distributions of the IMFs and a suggestion of Generalized Gaussian distribution (GGD) as a best fit distribution; a de-noising scheme based on a null-hypothesis of IMFs utilizing the unique filter behavior of EMD; and a novel noise estimation approach that is used to shift semi-blind spectrum sensing techniques into fully-blind ones based on the first IMF. These contributions are justified statistically and analytically and include comparison with other state of art techniques

Low strain pile testing based on synchrosqueezing wavelet transformation analysis

Low strain detection, an indirect and nondestructive testing method, is one of the main pile integrity testing methods. We propose low strain testing analysis based on a synchrosqueezing wavelet transformation (SST). Through a typical model pile test, the SST is applied to identify pile bottom signal reflection time and to separate signal from noise. It is also compared with the conventional wavelet de-noising and the empirical mode decomposition (EMD) de-noising method. Results show that the SST technique can be used to identify the reflected signal of the pile bottom, achieve signal and noise separation, and improve signal-to-noise ratio. The method has significant advantage in low strain detection signal processing compared to other methods

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs

A Review Of R Peak Detection Techniques Of Electrocardiogram (ECG)

Heart disease is one of the trivial issues regarding health problem over the last few decades in India. Numerous methods have been developed with still-ongoing modifications and ideas to observe and evaluate ECG signals based on each heart beat. Majority of research revolves around arrhythmia classification, heart rate monitoring and blood pressure measurements that require highly accurate assessments of rhythm disorders which can be possible by measuring QRS complex of ECG signal, so accurate QRS detection methods are very important to be utilized. There have been proposed many approaches to find out the R peak detection to analyze the ECG signals in past few years. Most recent and efficient techniques of R peak detection have been reviewed in this paper. Techniques which have been reviewed in this paper are Pan and Tompkins, Wavelet Transform, Empirical Mode Decomposition, Hilbert-Huang Transform, Fuzzy logic systems, Artificial neural networks

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

Real-Time, Hardware Efficient Ocular Artifact Removal From Single Channel EEG data Using a Hybrid Algebraic and Wavelet Algorithm

Electroencephalography (EEG) is a promising technique to record brain activities in natural settings. EEG signal usually gets contaminated by Ocular Artifacts (OA), removal of which is critical for the feature extraction and classification. With the increasing interest in wearable technologies, single channel EEG systems are becoming more prevalent that often require real-time signal processing for immediate feedback. In this context, a new hybrid algorithm to detect OA and subsequently remove OA from single channel streaming EEG data is proposed here. The algorithm first detects the OA zones using Algebraic approach, and then removes artifact from the detected OA zones using Discrete Wavelet Transform (DWT) decomposition method. De-noising technique is applied only to the OA zone that minimizes interference to neural information outside of OA zone. The microcontroller hardware implemented hybrid OA removal algorithm demonstrated real-time execution with sufficient accuracy in both OA detection and removal. The performance evaluation was carried out qualitatively and quantitatively for 0.5 sec epoch in overlapping manner using time-frequency analysis, mean square coherence, Correlation Coefficient (CC) and Mutual Information statistics. Matlab implementation resulted in average CC of 0.3242 and average MI of 1.0042, while microcontroller implementation resulted in average CC of 0.4033 for all blinks. Successful implementation of OA removal from single channel real-time EEG data using the proposed algorithm shows promise for real-time feedabck applications of wearable EEG devices

Low strain pile testing based on synchrosqueezing wavelet transformation analysis

Low strain detection, an indirect and nondestructive testing method, is one of the main pile integrity testing methods. We propose low strain testing analysis based on a synchrosqueezing wavelet transformation (SST). Through a typical model pile test, the SST is applied to identify pile bottom signal reflection time and to separate signal from noise. It is also compared with the conventional wavelet de-noising and the empirical mode decomposition (EMD) de-noising method. Results show that the SST technique can be used to identify the reflected signal of the pile bottom, achieve signal and noise separation, and improve signal-to-noise ratio. The method has significant advantage in low strain detection signal processing compared to other methods

Low strain pile testing based on synchrosqueezing wavelet transformation analysis

Low strain detection, an indirect and nondestructive testing method, is one of the main pile integrity testing methods. We propose low strain testing analysis based on a synchrosqueezing wavelet transformation (SST). Through a typical model pile test, the SST is applied to identify pile bottom signal reflection time and to separate signal from noise. It is also compared with the conventional wavelet de-noising and the empirical mode decomposition (EMD) de-noising method. Results show that the SST technique can be used to identify the reflected signal of the pile bottom, achieve signal and noise separation, and improve signal-to-noise ratio. The method has significant advantage in low strain detection signal processing compared to other methods

Low strain pile testing based on synchrosqueezing wavelet transformation analysis

Low strain detection, an indirect and nondestructive testing method, is one of the main pile integrity testing methods. We propose low strain testing analysis based on a synchrosqueezing wavelet transformation (SST). Through a typical model pile test, the SST is applied to identify pile bottom signal reflection time and to separate signal from noise. It is also compared with the conventional wavelet de-noising and the empirical mode decomposition (EMD) de-noising method. Results show that the SST technique can be used to identify the reflected signal of the pile bottom, achieve signal and noise separation, and improve signal-to-noise ratio. The method has significant advantage in low strain detection signal processing compared to other methods