In this paper, we present and investigate a special kind of stationary wavelet algorithm using “inverse” hard threshold to eliminate the electrocardiogram (ECG) interference included in diaphragmatic electromyographic (EMGdi). Differing from traditional wavelet hard threshold, “inverse” hard threshold is used to shrink strong coefficients of ECG interference and reserve weak coefficients of EMGdi signal. Meanwhile, a novel QRS location algorithm is proposed for the position detection of R wave by using low frequency coefficients in this paper. With the proposed method, raw EMGdi is decomposed by wavelet at fifth scale. Then, each ECG interference threshold is calculated by mean square, which is estimated by wavelet coefficients in the ECG cycle at each level. Finally, ECG interference wavelet coefficients are removed by “inverse” hard threshold, and then the de-noised signal is reconstructed by wavelet coefficients. The simulation and clinical EMGdi de-noising results show that the “inverse” hard threshold investigated in this paper removes the ECG interference in EMGdi availably and reserves its signal characteristics effectively, as compared to wavelet threshold.
Electrocardiography* ; Methods ; Wavelet Analysis*

In order to find the most suitable algorithm of T-wave end point detection for clinical detection, we tested three methods, which are not just dependent on the threshold value of T-wave end point detection, i. e. wavelet method, cumulative point area method and trapezium area method, in PhysioNet QT database (20 records with 3 569 beats each). We analyzed and compared their detection performance. First, we used the wavelet method to locate the QRS complex and T-wave. Then we divided the T-wave into four morphologies, and we used the three algorithms mentioned above to detect T-wave end point. Finally, we proposed an adaptive selection T-wave end point detection algorithm based on T-wave morphology and tested it with experiments. The results showed that this adaptive selection method had better detection performance than that of the single T-wave end point detection algorithm. The sensitivity, positive predictive value and the average time errors were 98.93%, 99.11% and (--2.33 ± 19.70) ms, respectively. Consequently, it can be concluded that the adaptive selection algorithm based on T-wave morphology improves the efficiency of T-wave end point detection.
Algorithms ; Electrocardiography ; Humans ; Wavelet Analysis

A method based on wavelet transform has been developed for detecting QRS-waves.The MIT-BIH database is used to evaluate this method, and the accuracy is over 99.5%. Then the method is implanted into the embedded system to implement the real-time detection of ECG. This method has good performances in time-delay and computation.
Algorithms ; Electrocardiography ; methods ; Wavelet Analysis

An applied technique of contrast enhancement for X-ray image is proposed which is based on combined enhancement of scaling and wavelet coefficients in discrete wavelet transform space. Conventional contrast enhancement methods such as contrast limited adaptive histogram equalization (CLAHE), multi-scale image contrast amplification (MUSICA) and gamma correction were applied on scaling coefficients to enhance the contrast of an original. In order to enhance the detail as well as reduce the blurring caused by up scaling of contrast modified scale coefficients from lower resolution, the sigmoid manipulation function was used to manipulate wavelet coefficients. The contrast detail mammography (CDMAM) phantom was imaged and processed to measure the image line profile of results and contrast to noise ratio (CNR) comparatively. The proposed technique produced better results than direct application of various contrast enhancement methods on image itself. The proposed method can enhance contrast, and also suppress the amplification of noise components in a single process. It could be useful for various applications in medical, industrial and graphical images where contrast and detail are of importance.
Colon, Sigmoid ; Mammography ; Noise ; Wavelet Analysis

This paper focuses on lossless medical image compression methods for medical images that operate on three-dimensional(3-D) irreversible integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm to medical images, using a 3-D wavelet decomposition and a 3-D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling(square root 2) and truncations keep the integer precision and the transform unitary. We have tested our encoder on volumetric medical images using different integer filters and different coding unit sizes. The coding unit sizes of 16 slices save considerable dynamic memory(RAM) and coding delay from full sequence coding units used in previous works. Results show that, even with these small coding units, our algorithm with certain filters performs as well and better in lossless coding than previous coding systems using 3-D integer wavelet transforms on volumetric medical images.
Clinical Coding ; Data Compression* ; Lifting* ; Wavelet Analysis*

Aiming at local field potential, the present paper introduces a method of estimating lag of neuron activities between brain areas based on windowed Harmonic wavelet transform (WHWT). Firstly, the WHWT of signals of two brain areas are calculated. Secondly, the instantaneous amplitude of the signals is calculated and finally, these amplitudes are cross-correlated and the lag at which the cross-correlation peak occurs is determined as the lag of neurons activities. Comparing with amplitude cross-correlation based on Gabor wavelet transform (GWT) or Hilbert transform (HT), this method is more precise and efficient in estimating the directionality and lag.
Brain ; physiology ; Humans ; Neurons ; physiology ; Wavelet Analysis

All the collected original electroencephalograph (EEG) signals were the subjects to low-frequency and spike noise. According to this fact, we in this study performed denoising based on the combination of wavelet transform and independent component analysis (ICA). Then we used three characteristic parameters, complexity, approximate entropy and wavelet entropy values, to calculate the preprocessed EEG data. We then made a distinguishing judge on the EEG state by the state change rate of the characteristic parameters. Through the anesthesia and non-anesthesia EEG data processing results showed that each of the three state change rates could reach about 50.5%, 21.6%, 19.5%, respectively, in which the performance of wavelet entropy was the highest. All of them could be used as a foundation in the quantified research of depth of anesthesia based on EEG analysis.
Anesthesia ; Electroencephalography ; Entropy ; Humans ; Wavelet Analysis

Detection of epileptic waves in EEG is particularly helpful in the interpretation of the underlying process in seizures. This study is aimed at providing a new method for automatic detection of epileptic waves through the wavelet analysis of EEGs. It mainly deals with the detection of spikes or spike-waves based on wavelet transform. Since spikes and spike-waves contain high frequency energy, they will be represented in a particular scale localized in a small time window. According to these feature waveforms of epileptic waves, a continuous processing system for epileptic waveforms detection is constructed. We apply discrete wavelet transform on EEGs. Because of the time-frequency domain localization of wavelet transforms, we can get the local maximal positions across several successive dyadic scales of wavelet transform. And these positions indicate the points of sharp transitions in EEGs. Then we calculate the distance between every two successive maximal positions in each scale. This distance stands for the period of subwave. Furthermore, the distribution of subwave periods of each scale can be worked out. Then, comparing the distribution of normal EEG's and epileptic EEG's. The difference between these two waveforms provides us the criteria for automatic detection and classification. In order to reduce the detection workload, we also compare the detection efficiency of each scale. The scale that provides highest accuracy is selected for our automatic detection system. The results presented in this study show that scale 3 provides the best detection accuracy. So, scale 3 is deemed to be the proper scale for automatic detection. This system has the following advantages: (1) Reduced the workload significantly by selecting proper scale(s) for automatic selection; (2) Enhanced the detection accuracy by selecting proper criteria and threshold; (3) Capable of continuous detection; (4) It is also fit for the detection of other biomedical signals. This system showed good performance, and the initial clinical results obtained are also encouraging.
Electroencephalography ; Epilepsy ; diagnosis ; Humans ; Wavelet Analysis

According to the limitations of wavelet threshold in de-noising method, we approached a combining algorithm of the stationary wavelet transform with adaptive filter. The stationary wavelet transformation can suppress Gibbs phenomena in traditional DWT effectively, and adaptive filter is introduced at the high scale wavelet coefficient of the stationary wavelet transformation. It would remove baseline wander and keep the shape of low frequency and low amplitude P wave, T wave and ST segment wave of ECG signal well. That is important for analyzing ECG signal of other feature information.
Algorithms ; Electrocardiography ; methods ; Humans ; Wavelet Analysis

BACKGROUND: We have described an objective and reproducible classification method for grading malignancy in the Feulgen stained bladder carcinoma. To create an optimized classifier for malignancy grading of histological bladder carcinoma cell images, it is necessary to extract the features that accurately describle the order/disorder of the nuclear variation and to evaluate the significance of the features. Above all, features selection considered about the correlation of features is very important, because the performance of the classification method depends on the selected features. METHODS: First, we acquired 40 representative histological bladder carcinoma cell images from each of four groups (Grade 1, Grade 2A, Grade 2B, Grade 3) and extracted morphology features, texture features and the texture features of wavelet transformed images. Second, we evaluated the significance of the extracted features using variance analysis. Third, we created classifiers for each selected feature and its combination set using discriminant analysis. Finally, we compared and analyzed the correct classification rate of each classifer. RESULTS: The optimized classifier was created from the combination of morphology features, texture features and the texture features of wavelet transformed images. CONCLUSIONS: We found that the correlation of features is more important than one feature's great significance in grading the malignancy of bladder carcinoma, and we have confirmed that the correct classification rate is determined by feature extractin, feature evaluation and feature selection.
Classification ; Multivariate Analysis ; Urinary Bladder Neoplasms ; Urinary Bladder* ; Wavelet Analysis