The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.
Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5–10 years old) and 25 children with autism (20 boys and 5 girls aged 5–10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1–4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.
In the present paper, wavelet transform and empirical mode decomposition (EMD) are combined to extracted the features of electroencephalogram (EEG) signal with music intervention, and to achieve a better classification accuracy rate and reliability in emotional assessment in order to provide a support for music therapy. The data were from Database for Emotion Analysis using Physiological Signals (DEAP). Based on wavelet transform α, β and θ rhythms were extracted at frontal (F3, F4), temporal (T7, T8) and central regions (C3, C4). Based on the EMD, the intrinsic mode function (IMF) was analyzed and extracted. Furthermore, average energy and amplitude difference of IMF were analyzed and obtained. The support vector machine was used to assess the state of emotion in order to support music therapy. According to this algorithm, the classification accuracy rate could reach 100% between no emotions, positive emotions and negative emotions, which made a 10% improvement between positive and negative emotion recognition. Effective evaluation result between positive and negative emotions was achieved. The states of emotion would influence the effect of music therapy, undoubtedly, the classification accuracy rate increasing of emo-tional assessment will further help improve the effect of music therapy and provide a better support to the therapy.
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.
The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the ‘clean’ EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram (EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper proposes a method which combines wavelet transform and artificial neural network (ANN) to assess the depth of anesthesia. Discrete wavelet transform was used to decompose the EEG signal, and the approximation coefficients and detail coefficients were used to calculate the 9 characteristic parameters. Kruskal-Wallis statistical test was made to these characteristic parameters, and the test showed that the parameters were statistically significant for the differences of the four levels of anesthesia: awake, light anesthesia, moderate anesthesia and deep anesthesia (P < 0.001). The 9 characteristic parameters were used as the input of ANN, the bispectral index (BIS) was used as the reference output, and the method was evaluated by the data of 8 patients during general anesthesia. The accuracy of the method in the classification of the four anesthesia levels of the test set in the 7:3 set-out method was 85.98%, and the correlation coefficient with the BIS was 0.977 0. The results show that this method can better distinguish four different anesthesia levels and has broad application prospects for monitoring the depth of anesthesia.
Due to the characteristics and environmental factors, electrocardiogram (ECG) signals are usually interfered by noises in the course of signal acquisition, so it is crucial for ECG intelligent analysis to eliminate noises in ECG signals. On the basis of wavelet transform, threshold parameters were improved and a more appropriate threshold expression was proposed. The discrete wavelet coefficients were processed using the improved threshold parameters, the accurate wavelet coefficients without noises were gained through inverse discrete wavelet transform, and then more original signal coefficients could be preserved. MIT-BIH arrythmia database was used to validate the method. Simulation results showed that the improved method could achieve better denoising effect than the traditional ones.
We proposed a multi-resolution-wavelet-transform based method to extract brainstem auditory evoked potential (BAEP) from the background noise and then to identify its characteristics correctly. Firstly we discussed the mother wavelet and wavelet transform algorithm and proved that bi-orthogonal wavelet bior5.5 and stationary discrete wavelet transform (SWT) were more suitable for BAEP signals. The correlation analysis of D6 scale wavelet coefficients between single trails and the ensemble average of all trails showed that the trails with good correlation (> 0.4) had higher signal-to-noise ratio, so that we could get a clear BAEP from a few trails by an average and wavelet filter method. Finally, we used this method to select desirable trails, extracted BAEP from every 10 trails and calculated theⅠ-Ⅴinter-waves' latency. The results showed that this strategy of trail selection was efficient. This method can not only achieve better de-noising effect, but also greatly reduce the stimulation time needed as well.
In this work, a new method of heart sound signal preprocessing is presented. First, the heart sound signals are decomposed by using multilayer wavelet transform. And then double parameters as thresholds are used in processing each layer after decomposition for denoising. Next, reconstruction of heart sound signals could be done after processing last layer. Four methods, i.e. wavelet transform, Hilbert-Huang transform (HHT), mathematical morphology, and normalized average Shannon energy, were used to extract the envelop of the heart sound signals respectively after reconstruction of heart sounds. All methods were improved in this study. We finally in our study chose 30 cases of raw heart sound signals, which were selected randomly from a database comed from The Clinical Medicine Institute of Montreal, and processed them by using the improved methods. The results were satisfactory. It showed that the extracted envelope with the original signal has a high degree of matching, whether it is a low frequency portion or high frequency portion. Most of all information of heart sound has been maintained in the envelope.
The main shortcomings of using electrocortical stimulation (ECS) in identifying the motor functional area around the focus in neurosurgery are certainly time-consuming, possibly cerebral cortex injuring and perhaps triggering epilepsy. To solve these problems, we in our research presented an intraoperative motor cortex functional mapping based on electrocorticography (ECoG). At first, using power spectrum estimation, we analyzed the characteristic of ECoG which was related to move task, and selected Mu rhythm as the move-related feature. Then we extracted the feature from original ECoG by multi-resolution wavelet analysis. By calculating the sum value of feature in every channel and observing the distribution of these sum values, we obtained the correlation between the cortex area under the electrode and motor cortex functional area. The results showed that the distribution of the relationship between the cortex under the electrode and motor cortex functional area was almost consistent with those identified by ECS which was called as ‘the gold-standard’. It indicated that this method was basically feasible, and it just needed five minutes totally. In conclusion, ECoG-based and passive identification of motor cortical function may serve as a useful adjunct to ECS in the intraoperative mapping.