Wavelet Packet Features Research Articles

STN localization using local field potentials based on wavelet packet features and stacking ensemble learning

Background:DBS entails the insertion of an electrode into the patient brain, enabling Subthalamic nucleus (STN) stimulation. Accurate delineation of STN borders is a critical but time-consuming task, traditionally reliant on the neurosurgeon experience in deciphering the intricacies of microelectrode recording (MER). While clinical outcomes of MER have been satisfactory, they involve certain risks to patient safety. Recently, there has been a growing interest in exploring the potential of local field potentials (LFP) due to their correlation with the STN motor territory. Method:A novel STN detection system, integrating LFP and wavelet packet transform (WPT) with stacking ensemble learning, is developed. Initial steps involve the inclusion of soft thresholding to increase robustness to LFP variability. Subsequently, non-linear WPT features are extracted. Finally, a unique ensemble model, comprising a dual-layer structure, is developed for STN localization. We harnessed the capabilities of support vector machine, Decision tree and k-Nearest Neighbor in conjunction with long short-term memory (LSTM) network. LSTM is pivotal for assigning adequate weights to every base model. Results:Results reveal that the proposed model achieved a remarkable accuracy and F1-score of 89.49% and 91.63%. Comparison with existing methods:Ensemble model demonstrated superior performance when compared to standalone base models and existing meta techniques. Conclusion:This framework is envisioned to enhance the efficiency of DBS surgery and reduce the reliance on clinician experience for precise STN detection. This achievement is strategically significant to serve as an invaluable tool for refining the electrode trajectory, potentially replacing the current methodology based on MER.

Robust Perceptual Wavelet Packet Features for the Recognition of Spontaneous Kannada Sentences

Robust Perceptual Wavelet Packet Features for the Recognition of Spontaneous Kannada Sentences

Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 80% of collected speech data is used for training the acoustic models and 20% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, the proposed features shows an improvement in Word Recognition Accuracy (WRA) of 1.79% over baseline features.

Epileptic State Classification by Fusing Hand-Crafted and Deep Learning EEG Features

Seizure onset detection and epileptic preictal prediction based on electroencephalogram (EEG) signals have been a challenge problem in the research community. In this brief, a novel epileptic states classification algorithm based on the multichannel EEGs representation using multiple hand-crafted features, the feature fusion and transfer learning (TL) with multiple pre-trained deep neural networks (DNNs), the discriminative feature extraction and epileptic state classification with a hierarchical neural network (HNN), is developed. The mean amplitude spectrum (MAS), mean power spectral density (MPSD) and wavelet packet features (WPFs) are firstly derived and fused into an image feature for multichannel EEGs representation. Then, 5 classical pre-trained DNNs are directly adopted as feature extractors on the fused image feature. A 7-layer fully-connected (FC) HNN is finally constructed for discriminative feature learning and epileptic state classification. The effectiveness is demonstrated through experiments on the CHB-MIT and the iNeuro epilepsy EEG databases.

Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis.

In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.

A Novel Incipient Fault Diagnosis Method for Analog Circuits Based on GMKL-SVM and Wavelet Fusion Features

To enhance the reliability of analog circuits in complex electrical systems, a novel incipient fault diagnosis method is presented in this article. The wavelet packet feature quantities, which consist of the energy, fluctuation coefficient, skewness, and margin factor, are obtained via multiscale time-frequency analysis with wavelet packet transform (WPT). Then, generalized discriminant analysis (GDA) is employed to realize the fusion of wavelet packet feature quantities because it can handle the data nonlinearity and eliminate redundant information. Furthermore, the generalized multiple kernel learning support vector machine (GMKL-SVM), which has the advantages of a strong generalization ability and high accuracy, is developed to identify the incipient fault classes of analog circuits. Moreover, a new particle swarm intelligent optimization algorithm, the sine cosine algorithm (SCA), is adopted to optimize key parameters of GMKL-SVM because of its high convergence speed and strong global optimization ability. The method is fully evaluated with the Sallen-Key bandpass filter circuit, the four-op-amp biquad high-pass filter circuit, and the leapfrog filter circuit. The experimental results demonstrate that the proposed incipient fault diagnosis method for analog circuits can produce higher diagnosis accuracy than other typical incipient fault diagnosis methods.

Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson’s disease using microelectrode recordings and wavelet packet features

BackgroundDeep brain stimulation (DBS) to the subthalamic nucleus (STN) is an effective neurosurgery that overcomes the motor system alternations of patients with advanced Parkinson’s disease. The most challenging aspect of DBS surgery is the accurate identification of STN and its borders. In general, it is performed manually by a neurophysiologist using the microelectrode recordings (MERs). This process is subjective, and tedious and further, interpretation of MERs is difficult because of its inherent nonstationary variations. New methodsIn this work, the wavelet-packet based features are proposed to automatically localize the STN and its subcortical structures using microelectrode recorded signals during DBS surgery. The study analyses 2904 MERs of 26 PD patients who underwent DBS implantation. The low and high order statistical parameters are extracted from the wavelet packet coefficients of MERs and used in the classifications, namely, non-STN vs. STN, pre-STN vs. STN and STN vs. post-STN. ResultsMost of the features are significantly different in STN and its subcortical regions, namely, pre-STN and post-STN. The proposed features achieve an average accuracy of 85 % in non-STN vs. STN, 87.2 % in pre-STN vs. STN and 77.7 % in STN vs. post-STN. The accuracy is improved by around 10 % in non-STN vs. STN and STN vs. post-STN when the transition error is 1 mm. Comparison with existing methodsThe proposed features are found to be better than the wavelet features. ConclusionsThe proposed approach could be a potential useful adjunct for the real-time rapid intraoperative identification of STN and its anatomical borders.

Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography

Many people lose their motor function because of spinal cord injury or stroke. This work studies the patient’s continuous movement intention of joint angles based on surface electromyography (sEMG), which will be used for rehabilitation. In this study, we introduced a new sEMG feature extraction method based on wavelet packet decomposition, built a prediction model based on the extreme learning machine (ELM) and analyzed the correlation between sEMG signals and joint angles based on the detrended cross-correlation analysis. Twelve individuals participated in rehabilitation tasks, to test the performance of the proposed method. Five channels of sEMG signals were recorded, and denoised by the empirical mode decomposition. The prediction accuracy of the wavelet packet feature-based ELM prediction model was found to be 96.23% ± 2.36%. The experimental results clearly indicate that the wavelet packet feature and ELM is a better combination to build a prediction model.

Automated Bearing Fault Diagnosis using Packet Features of Vibration Signal and Gaussian Support Vector Machine

Effective detection of the bearing fault and, specifically performance dilapidation assessment of a bearing is the topic of intensive analysis that may scale back prices and therefore the nonscheduled down time. This article presents an adaptive approach that is based on Bhattacharya space ranking method and dimensional reduction method as general discriminate analysis (GDA) with Gaussian support vector machine (GSVM) to accurately detect the defects of rolling bearing. For this investigation, first, vibration signal generated by rolling bearing was disintegrated to five levels employing wavelet packet (WP) method. Sixty three logarithmic wavelet packet features (LWPFs) were taken out from five level disintegrated vibration signals. After this, sixty three features were ranked by Bhattacharya space and top ten LWPFs were chosen. The top ten features were reduced to a new feature using GDA for effective detection and then applied to GSVM for detection of bearing fault. The experimental results show that new automated diagnosing approach attained classifier performance parameters as sensitivity (SE) or true positive rate, specificity (SP) or true negative rate, accuracy (AC) and positive prediction value (PPV) of 100, 98.50, 100 and 99.67 % for inner raceway (IR) and, AC: 99.49, SE: 100, SP: 98.78 and PPV: 99.87 for ball bearing (BB) at 0.18 mm diameter faults.

Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors.

The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.

Data Driven Leakage Detection and Classification of a Boiler Tube

Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.

Upper Arm Motion High-Density sEMG Recognition Optimization Based on Spatial and Time-Frequency Domain Features.

Background Spatial characteristics of sEMG signals are obtained by high-density matrix sEMG electrodes for further complex upper arm movement classification. Multiple electrode channels of the high-density sEMG acquisition device aggravate the burden of the microprocessor and deteriorate control system's real-time performance at the same time. A shoulder motion recognition optimization method based on the maximizing mutual information from multiclass CSP selected spatial feature channels and wavelet packet features extraction is proposed in this study. Results The relationship between the number of channels and recognition rate is obtained by the recognition optimization method. The original 64 electrodes channels are reduced to only 4-5 active signal channels with the accuracy over 92%. Conclusion The shoulder motion recognition optimization method is combined with the spatial-domain and time-frequency-domain features. In addition, the spatial feature channel selection is independent of feature extraction and classification algorithm. Therefore, it is more convenient to use less channels to achieve the desired classification accuracy.

Cavitation Detection in Centrifugal Pump Based on Interior Flow‐Borne Noise Using WPD‐PCA‐RBF

Cavitation detection is particularly essential for operating efficiency and stability of pumps. In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neural network is introduced to detect the cavitation status for centrifugal pumps. The cavitation performance and interior flow‐borne noise are measured under three different flow conditions. Then, time‐frequency domain analysis is performed on the interior flow‐borne noise signal using WPD, and the energy coefficient of each node is calculated to determine the optimal decomposition frequency band. Six‐feature parameters are extracted based on frequency‐division statistics, including three time‐domain features and three wavelet packet features. After that, the PCA is applied for dimensionality reduction. Finally, three cavitation statuses of noncavitation, inception cavitation, and serious cavitation are identified adopting RBF neural network. The results show that the comprehensive identification rate of the proposed method for three cavitation statuses reaches 98.2% with low identification error. The method based on interior flow‐borne noise analysis can be well applied for on‐line monitoring and diagnosis of pump industry.

Wavelet Packet and Granular Computing With Application to Communication Emitter Recognition

Individual recognition of communication emitter can obtain the working mechanism, system parameters, and the usage model of the emitters based on the separation and interception of the specific emitter signals. In order to realize the individual recognition of communication emitter, a method of communication emitter recognition based on wavelet packet feature extraction and attribute reduction of granular computing is proposed. The method uses hierarchical and granularity to represent the subtle feature information of different emitter signals contained in the original data after the decomposition of wavelet packet, so that the feature attributes of different emitters are efficiently mapped to different levels of granular structure which can be used to search for an optimal reduced attributes operation for signal recognition. And then, the reduced attributes are used to distinguish the emitter signals correctly by label propagation method. The experimental results show that the proposed method based on wavelet packet feature extraction and granular computing attribute reduction has good classification performance in individual recognition of communication emitters.

Towards Enhancement of Patients' Engagement: Online Modification of Rehabilitation Training Modes Using Facial Expression and Muscle Fatigue.

Rehabilitation training combined with human psychological and physiological information can enhance patients' neural engagement. For this purpose, a racial expression and muscle fatigue based rehabilitation training method is proposed in this paper. Signals from major zygomaticus and corrugator supercilii muscles are used for racial expression recognition, and signals from rectus femoris and biceps femoris muscles are used for fatigue level analysis. Facial expressions (positive, neutral, and negative) are recognized by a classifier which is constructed by wavelet packet features and neural network, and median frequency (MF) is applied to analyze fatigue level. A passive training mode and five-level active training modes are included. Different training modes have different damping levels. When the patient is with positive expression and without fatigue, the damping will be raised automatically in order to increase exercise difficulties and enhance the patient's engagement; when with negative expression and mild fatigue, damping will be decreased properly to reduce exercise difficulties and ease user's burden to obtain more efficient training. Moreover, when patient is severe fatigue, passive training is selected to avoid overfatigue and muscle injury. Feasibility of the proposed method is validated by the experiment conducted on the platform of a damping adjustable treadwheel.

Voice Activity Detection Algorithm Using Spectral-Correlation and Wavelet-Packet Transformation

It is developed the voice activity detection algorithm using noise classification technique. It is proposed the spectral-correlation and wavelet-packet (WP) features of frames for voice activity estimation. There are tested three WP trees for effective representing of audio segments: mel-scaled wavelet packet tree, bark-scaled wavelet packet tree and ERB-scaled (equivalent rectangular bandwidth) wavelet packet tree. Application only two principal components of WP features allows to classify accurately the environment noise. The using wavelet-packet tree design which follows the concept of equivalent rectangular bandwidth for acoustic feature extraction allows to increase the voice/silence segments classification accuracy by at least 4% in compare to other classification based voice activity detection algorithms for different noise.

Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation.

This study presents wavelet packet feature assessment of neural control information in paretic upper limb muscles of stroke survivors for myoelectric pattern recognition, taking advantage of high-resolution time–frequency representations of surface electromyogram (EMG) signals. On this basis, a novel channel selection method was developed by combining the Fisher’s class separability index and the sequential feedforward selection analyses, in order to determine a small number of appropriate EMG channels from original high-density EMG electrode array. The advantages of the wavelet packet features and the channel selection analyses were further illustrated by comparing with previous conventional approaches, in terms of classification performance when identifying 20 functional arm/hand movements implemented by 12 stroke survivors. This study offers a practical approach including paretic EMG feature extraction and channel selection that enables active myoelectric control of multiple degrees of freedom with paretic muscles. All these efforts will facilitate upper limb dexterity restoration and improved stroke rehabilitation.

No-reference image quality assessment using statistical wavelet-packet features

In this paper an efficient no-reference (NR) image quality assessment (IQA) method is presented based on the statistical features of subband coefficients in the wavelet-packet domain. The proposed method is based on the hypothesis that potential distortions may alter the statistical characteristics of natural un-distorted images. Hence, by characterizing the statistical properties of a given distorted image one can identify the distortion and its strength in the distorted image. For this purpose, several statistical features of a given gray-scale image as well as the magnitude of its gradient and its Laplacian are extracted in the wavelet-packet domain. The extracted features are then mapped to quality scores within a two-stage quality assessment framework. The proposed method is general-purpose, and is able to assess the image quality across various distortion categories. Experimental results indicate that the proposed method achieves high accuracy in image quality prediction as compared to several prominent and state-of-the-art full-reference and no-reference IQA methods.

Parkinson’s disease hand tremor detection system for mobile application

Parkinson’s disease currently affects millions of people worldwide and is steadily increasing. Many symptoms are associated with this disease, including rest tremor, bradykinesia, stiffness or rigidity of the extremities and postural instability. No cure is currently available for Parkinson’s disease patients; instead most medications are for treatment of symptoms. This treatment depends on the quantification of these symptoms such as hand tremor. This work proposes a new system for mobile phone applications. The system is based on measuring the acceleration from the Parkinson’s disease patient’s hand using a mobile cell phone accelerometer. Recordings from 21 Parkinson’s disease patients and 21 healthy subjects were used. These recordings were analysed using a two level wavelet packet analysis and features were extracted forming a feature vector of 12 elements. The features extracted from the 42 subjects were classified using a neural networks classifier. The results obtained showed an accuracy of 95% and a Kappa coefficient of 90%. These results indicate that a cell phone accelerometer can accurately detect and record rest tremor in Parkinson’s disease patients.