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  1. Fulltext A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students' Interest in the Mathematics Classroom
    Babiker A, Faye I
    Comput Intell Neurosci, 2021;2021:6617462.
    PMID: 33564299 DOI: 10.1155/2021/6617462
    Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.
    Matched MeSH terms: Wavelet Analysis*
  2. Fulltext Improved architecture of speaker recognition based on wavelet transform and Mel Frequency Cepstral Coefficient (MFCC)
    Nor Ashikin Rahman, Noor Azilah Muda, Norashikin Ahmad
    Pertanika Journal of Science & Technology, 2017;25(106):1-10.
    MyJurnal
    Combining Mel Frequency Cepstral Coefficient with wavelet transform for feature extraction is not new. This paper proposes a new architecture to help in increasing the accuracy of speaker recognition compared with conventional architecture. In conventional speaker model, the voice will undergo noise elimination first before feature extraction. The proposed architecture however, will extract the features and eliminate noise simultaneously. The MFCC is used to extract the voice features while wavelet de-noising technique is used to eliminate the noise contained in the speech signals. Thus, the new architecture achieves two outcomes in one single process: ex-tracting voice feature and elimination of noise.
    Matched MeSH terms: Wavelet Analysis
  3. Fulltext Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting
    Alenezy AH, Ismail MT, Jaber JJ, Wadi SA, Alkhawaldeh RS
    PLoS One, 2022;17(12):e0278835.
    PMID: 36490280 DOI: 10.1371/journal.pone.0278835
    This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Saudi Arabia's stock exchange (Tadawul) data. The MODWT comprises five mathematical functions and fuzzy inference rules. The inputs are the oil price (Loil) and repo rate (Repo) according to multiple regression correlation, and the Engle and Granger Causality test Engle RF, (1987). The logarithm of the stock market price (LSCS) in Tadawul reflects the output variable. The correlation matrix reveals that there is no collinearity between the input variables, and the causality test demonstrates that the input variables significantly influence the outcome variable. According to the multiple regression, there is a substantial negative influence between Loil and LSCS but a significant positive effect between Repo and output. For the 80% dataset under ME (0.000005), MAE (0.003214), and MAPE (0.064497), the MODWT-LA8 (ARIMA(1,1,0) with drift) for the LSCS variable performs better than other WT functions. In the novel hybrid model MODWT-FIR.DM, each function's approximation coefficient (LSCS) is applied with input variables (Loil and Repo). We evaluate the performance of the proposed model (MODWT-LA8-FIR.DM) using different statistical measures (ME, RMSE, MAE, MPE) and compare it to two established models: the original FIR.DM and other MODWT-FIR.DM functions for forecasting 20% of datasets. The outcomes show that the MODWT-LA8-FIR.DM performs better than the traditional models based on lower ME (3.167586), RMSE (3.167638), MAE (3.167586), and MPE (80.860849). The proposed hybrid model may be a potential stock market forecasting model.
    Matched MeSH terms: Wavelet Analysis*
  4. Fulltext Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis
    Faysal A, Ngui WK, Lim MH, Leong MS
    Sensors (Basel), 2021 Dec 04;21(23).
    PMID: 34884120 DOI: 10.3390/s21238114
    Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier's performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis.
    Matched MeSH terms: Wavelet Analysis*
  5. Optimal selection of mother wavelet for accurate infant cry classification
    Saraswathy J, Hariharan M, Nadarajaw T, Khairunizam W, Yaacob S
    Australas Phys Eng Sci Med, 2014 Jun;37(2):439-56.
    PMID: 24691930 DOI: 10.1007/s13246-014-0264-y
    Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis.
    Matched MeSH terms: Wavelet Analysis*
  6. Luminance sticker based facial expression recognition using discrete wavelet transform for physically disabled persons
    Nagarajan R, Hariharan M, Satiyan M
    J Med Syst, 2012 Aug;36(4):2225-34.
    PMID: 21465183 DOI: 10.1007/s10916-011-9690-5
    Developing tools to assist physically disabled and immobilized people through facial expression is a challenging area of research and has attracted many researchers recently. In this paper, luminance stickers based facial expression recognition is proposed. Recognition of facial expression is carried out by employing Discrete Wavelet Transform (DWT) as a feature extraction method. Different wavelet families with their different orders (db1 to db20, Coif1 to Coif 5 and Sym2 to Sym8) are utilized to investigate their performance in recognizing facial expression and to evaluate their computational time. Standard deviation is computed for the coefficients of first level of wavelet decomposition for every order of wavelet family. This standard deviation is used to form a set of feature vectors for classification. In this study, conventional validation and cross validation are performed to evaluate the efficiency of the suggested feature vectors. Three different classifiers namely Artificial Neural Network (ANN), k-Nearest Neighborhood (kNN) and Linear Discriminant Analysis (LDA) are used to classify a set of eight facial expressions. The experimental results demonstrate that the proposed method gives very promising classification accuracies.
    Matched MeSH terms: Wavelet Analysis*
  7. Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review
    Sudarshan VK, Mookiah MR, Acharya UR, Chandran V, Molinari F, Fujita H, et al.
    Comput Biol Med, 2016 Feb 1;69:97-111.
    PMID: 26761591 DOI: 10.1016/j.compbiomed.2015.12.006
    Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
    Matched MeSH terms: Wavelet Analysis*
  8. Fulltext Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework
    Yahya N, Musa H, Ong ZY, Elamvazuthi I
    Sensors (Basel), 2019 Nov 08;19(22).
    PMID: 31717412 DOI: 10.3390/s19224878
    In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.
    Matched MeSH terms: Wavelet Analysis*
  9. Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images
    Yeap ZX, Sim KS, Tso CP
    Microsc Res Tech, 2019 Apr;82(4):402-414.
    PMID: 30575192 DOI: 10.1002/jemt.23181
    Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two-dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub-band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub-band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.
    Matched MeSH terms: Wavelet Analysis
  10. A Hybrid Wavelet-Arima Model for Standardized Precipitation Index Drought Forecasting
    Alfa Mohammed Salisu, Ani Shabri
    MATEMATIKA, 2020;36(2):141-156.
    MyJurnal
    This paper proposes A Hybrid Wavelet-Auto-Regressive Integrated Moving Average (W-ARIMA) model to explore the ability of the hybrid model over an ARIMA model. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMA model using the Standardized Precipitation Index (SPI) drought data for forecasting drought modeling development. SPI data from January 1954 to December 2008 used was divided into two - (80%/20% for training/testing respectively). The results were compared with the conventional ARIMA model with Mean Square Error (MSE) and Mean Average Error (MAE) as an error measure. The results of the proposed method achieved the best forecasting performance.
    Matched MeSH terms: Wavelet Analysis
  11. On-line detection method for outliers of dynamic instability measurement data in geological exploration control process
    Fang Liu, Weixing Su, Jianjun Zhao, Xiaodan Liang
    Sains Malaysiana, 2017;46:2205-2213.
    Considering the characteristics of the vibration data detected by the unstable regulation process in the grinding and grading control system and the shortcomings of the traditional wavelet anomaly detection method, an online anomaly detection method combining autoregressive and wavelet analysis is proposed. By introducing the improved robust AR model, this method can overcome the problem that the time and frequency of traditional anomaly detection using wavelet analysis method cannot be well balanced and ensure the rationality of normal detection of process data. Considering the characteristics of parameter change and dynamic characteristics in the process of grinding and grading, the proposed method has the ability of on-line detection and parameter updating in real time, which ensures the control parameters of time-varying process control system. In order to avoid the problem that the traditional anomaly detection method needs to set the detection threshold, introduce the HMM to analyse the wavelet coefficients and update the HMM parameters online, which can ensure that the HMM can well reflect the distribution of the abnormal value of the process data. Through the experiment and application, it is proven that the anomaly data detection method proposed in this paper is more suitable for the detection data in the process of unstable regulation.
    Matched MeSH terms: Wavelet Analysis
  12. Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems
    Al-Busaidi AM, Khriji L, Touati F, Rasid MF, Mnaouer AB
    J Med Syst, 2017 Sep 12;41(10):166.
    PMID: 28900815 DOI: 10.1007/s10916-017-0817-1
    One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.
    Matched MeSH terms: Wavelet Analysis
  13. Multiclass tumor identification using combined texture and statistical features
    Latif G, Bashar A, Awang Iskandar DNF, Mohammad N, Brahim GB, Alghazo JM
    Med Biol Eng Comput, 2023 Jan;61(1):45-59.
    PMID: 36323980 DOI: 10.1007/s11517-022-02687-w
    Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.
    Matched MeSH terms: Wavelet Analysis
  14. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques
    Amin HU, Malik AS, Ahmad RF, Badruddin N, Kamel N, Hussain M, et al.
    Australas Phys Eng Sci Med, 2015 Mar;38(1):139-49.
    PMID: 25649845 DOI: 10.1007/s13246-015-0333-x
    This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.
    Matched MeSH terms: Wavelet Analysis*
  15. Fulltext The impact of the wavelet propagation distribution on SEIRS modeling with delay
    Apenteng OO, Ismail NA
    PLoS One, 2014;9(6):e98288.
    PMID: 24911023 DOI: 10.1371/journal.pone.0098288
    Previous models of disease spread involving delay have used basic SIR (susceptible--infectious--recovery) formulae and approaches. This paper demonstrates how time-varying SEIRS (S--exposed--I - R - S) models can be extended with delay to produce wave propagations that simulate periodic wave fronts of disease spread in the context of population movements. The model also takes into account the natural mortality associated with the disease spread. Understanding the delay of an infectious disease is critical when attempting to predict where and how fast the disease will propagate. We use cellular automata to model the delay and its effect on the spread of infectious diseases where population movement occurs. We illustrate an approach using wavelet transform analysis to understand the impact of the delay on the spread of infectious diseases. The results indicate that including delay provides novel ways to understand the effects of migration and population movement on disease spread.
    Matched MeSH terms: Wavelet Analysis*
  16. Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals
    Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G
    J Med Syst, 2016 Jan;40(1):20.
    PMID: 26531753 DOI: 10.1007/s10916-015-0392-2
    Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1-1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28 % is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45 % and an area under receiver operating characteristic curve (AUC) of 95.06 % is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26 % in accuracy and 1.45 % in AUC is observed.
    Matched MeSH terms: Wavelet Analysis*
  17. Fulltext Stock market comovements among Asian emerging economies: A wavelet-based approach
    Younis I, Longsheng C, Basheer MF, Joyo AS
    PLoS One, 2020;15(10):e0240472.
    PMID: 33044995 DOI: 10.1371/journal.pone.0240472
    Stock market, is one of the most important financial market which has a close relationship with a country's economy, due to which it is often called the barometer of the economy. Over the past 25 years, the stock markets have been affected by different global economic shocks. Various researchers have analyzed different aspects of these effects one by one, however, this study is an assessment of stock market interrelationship of emeriging Asian economies which include most of the East Asian, and Southeast Asian emerging economies with special focus on China for past decades during which different crisis occurred. We used Morgan Stanley capital international (MSCI) daily indices data for each stock market and compared Chinese stock market with the stock markets of India, Pakistan, Malaysia, Singapore, and Indonesia. We analyzed the data through the individual wavelet power spectrum, cross-wavelet transform and wavelet coherence, to determine the correlation and volatility among the selected stock markets. These model have the power to analyze co-movements among these countries with respect to both frequency and time spaces. Our findings show that there are co-movement patterns of higher frequencies during the crises periods of 1997, 2008 and 2015. The dependency strength among the considered economies is noted to increase in the crisis periods, which implies increased short- and long-term benefits for the investors. From a financial point of view, it has been determined that the co-movement strength among the emerging economies of Asia may have an effect on the VaR (Value at Risk) levels of a multi-country portfolio. Furthermore, the stock market of China shows a high correlation with the other six Asian stock emerging markets in both high and low-frequency spectrums. The association of the south and east Asian stock market with Chinese stock markets show the interconnection of these economies with the economy of China since past two decades. These findings are useful for investors, portfolio managers and the policymaker around the globe.
    Matched MeSH terms: Wavelet Analysis*
  18. Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia
    Yong NK, Awang N
    Environ Monit Assess, 2019 Jan 11;191(2):64.
    PMID: 30635772 DOI: 10.1007/s10661-019-7209-6
    This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models' performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.
    Matched MeSH terms: Wavelet Analysis*
  19. Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies
    Koh JEW, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, et al.
    Comput Biol Med, 2017 05 01;84:89-97.
    PMID: 28351716 DOI: 10.1016/j.compbiomed.2017.03.008
    Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.
    Matched MeSH terms: Wavelet Analysis*
  20. Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques
    Amin HU, Ullah R, Reza MF, Malik AS
    J Neuroeng Rehabil, 2023 Jun 02;20(1):70.
    PMID: 37269019 DOI: 10.1186/s12984-023-01179-8
    BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.

    METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.

    RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.

    CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

    Matched MeSH terms: Wavelet Analysis*
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