Journal articles on the topic 'Signals classification'
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Fikri, Muhammad Rausan, Indah Soesanti, and Hanung Adi Nugroho. "ECG Signal Classification Review." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (June 18, 2021): 15. http://dx.doi.org/10.22146/ijitee.60295.
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The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.
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Chen, Shichuan, Kunfeng Qiu, Shilian Zheng, Qi Xuan, and Xiaoniu Yang. "Radio–Image Transformer: Bridging Radio Modulation Classification and ImageNet Classification." Electronics 9, no. 10 (October 9, 2020): 1646. http://dx.doi.org/10.3390/electronics9101646.
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Radio modulation classification is widely used in the field of wireless communication. In this paper, in order to realize radio modulation classification with the help of the existing ImageNet classification models, we propose a radio–image transformer which extracts the instantaneous amplitude, instantaneous phase and instantaneous frequency from the received radio complex baseband signals, then converts the signals into images by the proposed signal rearrangement method or convolution mapping method. We finally use the existing ImageNet classification network models to classify the modulation type of the signal. The experimental results show that the proposed signal rearrangement method and convolution mapping method are superior to the methods using constellation diagrams and time–frequency images, which shows their performance advantages. In addition, by comparing the results of the seven ImageNet classification network models, it can be seen that, except for the relatively poor performance of the architecture MNASNet1_0, the modulation classification performance obtained by the other six network architectures is similar, indicating that the proposed methods do not have high requirements for the architecture of the selected ImageNet classification network models. Moreover, the experimental results show that our method has good classification performance for signal datasets with different sampling rates, Orthogonal Frequency Division Multiplexing (OFDM) signals and real measured signals.
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Ting, Evon Lim Wan, Almon Chai, and Lim Phei Chin. "A Review on EMG Signal Classification and Applications." International Journal of Signal Processing Systems 9, no. 1 (March 2022): 1–6. http://dx.doi.org/10.18178/ijsps.10.1.1-6.
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Electromyography (EMG) signals are muscles signals that enable the identification of human movements without the need of complex human kinematics calculations. Researchers prefer EMG signals as input signals to control prosthetic arms and exoskeleton robots. However, the proper algorithm to classify human movements from raw EMG signals has been an interesting and challenging topic to researchers. Various studies have been carried out to produce EMG-based human movement classification that gives high accuracy and high reliability. In this paper, the methods used in EMG signal acquisition and processing are reviewed. The different types of feature extraction techniques preferred by researchers are also discussed, including some combination and comparison of feature extraction techniques. This paper also reviews the different types of classifiers favored by researchers to recognize human movements based on EMG signals. The current applications of EMG signals are also reviewed.
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Pah, Nemuel D., and Dinesh Kant Kumar. "Thresholding Wavelet Networks for Signal Classification." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 03 (September 2003): 243–61. http://dx.doi.org/10.1142/s0219691303000220.
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This paper reports a new signal classification tool, a modified wavelet network called Thresholding Wavelet Networks (TWN). The network is designed for the purposes of classifying signals. The philosophy of the technique is that often the difference between signals may not lie in the spectral or temporal region where the signal strength is high. Unlike other wavelet networks, this network does not concentrate necessarily on the high-energy region of the input signals. The network iteratively identifies the suitable wavelet coefficients (scale and translation) that best differentiate the different signals provided during training, irrespective of the ability of these coefficients to represent the signals. The network is not limited to the changes in temporal location of the signal identifiers. This paper also reports the testing of the network using simulated signals.
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RAPP, P. E., T. A. A. WATANABE, P. FAURE, and C. J. CELLUCCI. "NONLINEAR SIGNAL CLASSIFICATION." International Journal of Bifurcation and Chaos 12, no. 06 (June 2002): 1273–93. http://dx.doi.org/10.1142/s021812740200508x.
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In this contribution, we show that the incorporation of nonlinear dynamical measures into a multivariate discrimination provides a signal classification system that is robust to additive noise. The signal library was composed of nine groups of signals. Four groups were generated computationally from deterministic systems (van der Pol, Lorenz, Rössler and Hénon). Four groups were generated computationally from different stochastic systems. The ninth group contained inter-decay interval sequences from radioactive cobalt. Two classification criteria (minimum Mahalanobis distance and maximum Bayesian likelihood) were tested. In the absence of additive noise, no errors occurred in a within-library classification. Normally distributed random numbers were added to produce signal to noise ratios of 10, 5 and 0 dB. When the minimum Mahalanobis distance was used as the classification criterion, the corresponding error rates were 2.2%, 4.4% and 20% (Expected Error Rate = 89%). When Bayesian maximum likelihood was the criterion, the error rates were 1.1%, 4.4% and 21% respectively. Using nonlinear measures an effective discrimination can be achieved in cases where spectral measures are known to fail. Most classification errors occurred at low signal to noise ratios when a stochastic signal was misclassified into a different group of stochastic signals. When the within-library classification exercise is limited to the four groups of deterministic signals, no classification errors occurred with clean data, at SNR = 10 dB, or at SNR = 5 dB. A single classification error (Observed Error Rate = 2.5%, Expected Error Rate = 75%) occurred with both classification criteria at SNR = 0 dB.
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Matz, Vaclav, Marcel Kreidl, and Radislav Smid. "Classification of ultrasonic signals." International Journal of Materials and Product Technology 27, no. 3/4 (2006): 145. http://dx.doi.org/10.1504/ijmpt.2006.011267.
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Abdullah, A. R., N. A. Abidullah, N. H. Shamsudin, N. H. H. Ahmad, and M. H. Jopri. "Power Quality Signals Classification System Using Time-Frequency Distribution." Applied Mechanics and Materials 494-495 (February 2014): 1889–94. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1889.
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Power quality signals are an important issue to electricity consumers. The signals will affect manufacturing process, malfunction of equipment and economic losses. Thus, an automated monitoring system is required to identify and classify the signals for diagnosis purposes. This paper presents the development of power quality signals classification system using time-frequency analysis technique which is spectrogram. From the time-frequency representation (TFR), parameters of the signal are estimated to identify the characteristics of the signals. The signal parameters are instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion, total harmonic distortion and total non harmonic distortion. In this paper, major power quality signals are focused based on IEEE Std. 1159-2009 such as swell, sag, interruption, harmonic, interharmonic, and transient. An automated signal classification system using spectrogram is developed to identify, classify as well as provide the information of the signal.
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Duan, Li Juan, Xue Bin Wang, Zhen Yang, Hai Yan Zhou, Chun Peng Wu, Qi Zhang, and Jun Miao. "EEG Signal Classification by Global Field Power." Applied Mechanics and Materials 128-129 (October 2011): 1434–37. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1434.
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Our project focuses on the emotional face evoked EEG signal recognition. Since EEG signals contain enough information to separate different emotional facial expressions. Thus we propose a new approach which is based on global field power on EEG signal classification. In order to perform this result, firstly, we gather a dataset with EEG signals. This is done by measuring EEG signals from people aged 20-30 that are stimulated by emotional facial expressions (Happy, Neutral, Sad). Secondly, the collected EEG signals are preprocessed through using noise reduction method. And then select features by principal component analysis (PCA) to filter out redundant information. Finally, using fisher classifier and a 10-fold cross validation method for training and testing, a good classification rate is achieved when combination local max global field power EEG signals. The rate is 90.49%.
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Abdullah, Abdul Rahim, N. A. Abidullah, N. H. Shamsudin, N. H. H. Ahmad, and M. H. Jopri. "Performance Verification of Power Quality Signals Classification System." Applied Mechanics and Materials 752-753 (April 2015): 1158–63. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1158.
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Power quality has become a greater concern nowadays. The increasing number of power electronics equipment contributes to the poor quality of electrical power supply. The power quality signals will affect manufacturing process, malfunction of equipment and economic losses. This paper presents the verification analysis of power quality signals classification system. The developed system is based on linear time-frequency distribution (TFD) which is spectrogram that represents the signals jointly in time-frequency representation (TFR). The TFD is very appropriate to analyze power quality signals that have magnitude and frequency variations. Parameters of the signal such as root mean square (RMS) and fundamental RMS, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) of voltage signal are estimated from the TFR to identify the characteristics of the signal. Then, the signal characteristics are used as input for signal classifier to classify power quality signals. In addition, standard power line measurements are also calculated from voltage and current such as RMS and fundamental RMS voltage and current, real power, apparent power, reactive power, frequency and power factor. The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std. 1159-2009. The power quality analysis has been tested using a set of data and the results show that, the spectrogram gives high accuracy measurement of signal characteristics. However, the system offers lower accuracy compare to simulation due to the limitation of the system.
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Abed, Sawsan Hadi, and Nidaa A. Abbas. "Gender Classification of Mixing and De-mixing Speech." Webology 19, no. 1 (January 20, 2022): 5353–68. http://dx.doi.org/10.14704/web/v19i1/web19359.
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Gender classification is growing in popularity due to the variety of fields in which it can be used. It can be employed in various fields, including criminal investigations and security and authentication services. Gender Classifying speech for different speakers is still a demanding and challenging task for recognizing overlapped speech and building a robust prediction model. The paper provides a gender classification system that uses Independent Component Analysis (ICA) and several machine learning algorithms to identify mixing and de-mixing speech signals. ICA is employed to separate the mixed signal into their source signals. The system consists of two stages: the first stage is the mixing and separating process for signals. The second stage involves combining feature extraction and constructing a classification model to determine whether a signal is male or female based on its acoustic attributes. The system will evaluate the efficacy and significance of machine learning algorithms for selecting the optimal method to identify the speaker's gender with the most excellent efficiency and accuracy. Experimentation shows that the best accuracy value for an SVM model with mixing speeches is 87.1 %, and the best accuracy value for a Neural Net and SVM model with de-mixing speeches is 97.8 %.
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Ramírez-Martínez, Daniel, Mariel Alfaro-Ponce, Oleksiy Pogrebnyak, Mario Aldape-Pérez, and Amadeo-José Argüelles-Cruz. "Hand Movement Classification Using Burg Reflection Coefficients." Sensors 19, no. 3 (January 24, 2019): 475. http://dx.doi.org/10.3390/s19030475.
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Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
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Seppänen, Henri, Siang Tat Chua, Joel Elizondo Martinez, and Pedro Villa. "Ultrasonic Wire Bond Outlier Classification." International Symposium on Microelectronics 2021, no. 1 (October 1, 2021): 000256–59. http://dx.doi.org/10.4071/1085-8024-2021.1.000256.
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Abstract K&S developed and tested the Advanced Process Diagnostics (APD) algorithm to classify bonding outliers in ultrasonic wire bond production. APD is a software feature, part of Kulicke & Soffa wedge bonders to measure and analyze process signals and detect and classify bond outliers. APD helps bonder operators, production supervisors and process engineers to detect process deviations and fix the underlying root causes. APD measures bond signals, such as deformation, ultrasonic current and ultrasonic frequency. Bonds are automatically divided into subgroups based on bond order and process parameters and the signals within a subgroup are then normalized. For outlier classification, the features are extracted from the normalized signals and combined into failure class values. The failure classes, such as contamination, misaligned wire and unstable substrate, are calculated independently. Within the APD feature, a user can define limits for the failure class values and define bonder actions based on the severity of the detected outlier. We measured the detection rates for large wire Al bond failure classes and demonstrate how APD calculates failure class values from the signals. Additionally, we show how new signal features and failure classes can be defined to detect production specific or rare failure classes. Finally, we present outlier classification performance metrics against large production data sets.
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Houssein, Essam H., Moataz Kilany, and Aboul Ella Hassanien. "ECG signals classification: a review." International Journal of Intelligent Engineering Informatics 5, no. 4 (2017): 376. http://dx.doi.org/10.1504/ijiei.2017.087944.
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Hassanien, Aboul Ella, Moataz Kilany, and Essam H. Houssein. "ECG signals classification: a review." International Journal of Intelligent Engineering Informatics 5, no. 4 (2017): 376. http://dx.doi.org/10.1504/ijiei.2017.10008807.
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Padhy, Sibasankar, and S. Sai Suryateja. "EEG Classification Using TQWT and Classifiers." Volume 5 - 2020, Issue 8 - August 5, no. 8 (August 27, 2020): 603–11. http://dx.doi.org/10.38124/ijisrt20aug408.
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The purpose of this study is to detect the epileptic seizures, which can be indicated by the abnormal disturbances in intracranial neurons using the electroencephalogram (EEG) signals. The EEG signals are grouped into three categories viz., Normal EEG signals (Z and O subsets), Seizure-free EEG signals (N and F subsets), and Seizure EEG signals (S subset). Whereas, for classification in this study, EEG signals are divided into three groups namely NF-S, O-FS, and ZO-NF-S. The signal length is fixed to be 4096 samples. The EEG signals will be decomposed by using Tunable-Q Wavelet Transform (TQWT), which produces intrinsic mode functions (IMFs) in decreasing order of frequency. These IMFs are analysed to gather the features of these signals, which help to classify them into various categories, and these features are fed as inputs to three classifiers viz., Random Forest (RF), Decision Table (DT), and Logistic Regression (LR). Logistic Regression classifier has showed higher accuracy, specificity and sensitivity for NF-S and O-F-S groups in comparison to RF and DT classifiers, whereas, Random Forest classifier expressed higher accuracy, specificity and sensitivity for ZO-NF-S groups in comparison to other classifiers. By utilising LR classifier, the suitable parameters of TQWT in NF-S (seizure-free vs. Seizure) are Q=6, r=3, and J=9 and showed maximum accuracy of 98%; and in O-F-S (Normal vs. Seizure-free vs. Seizure), Q=1, r=3, and J=9 attained maximum accuracy of 94.7%. Whereas, in ZONF-S (Normal vs. Seizure-free vs. Seizure), Q=4, r=3, and J=9 expressed maximum accuracy of 99.8% utilising Random Forest classifier.
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Shao, Shiliang, Ting Wang, Yongliang Wang, Yun Su, Chunhe Song, and Chen Yao. "Research of HRV as a Measure of Mental Workload in Human and Dual-Arm Robot Interaction." Electronics 9, no. 12 (December 17, 2020): 2174. http://dx.doi.org/10.3390/electronics9122174.
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Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator’s mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject’s HRV signal to train the model, the subject’s mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators’ well-being and safety in the human-robot interaction process.
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Rawi, Atiaf A., Murtada K. Elbashir, and Awadallah M. Ahmed. "ECG Heartbeat Classification Using CONVXGB Model." Electronics 11, no. 15 (July 22, 2022): 2280. http://dx.doi.org/10.3390/electronics11152280.
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ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification using a convolutional neural network (CNN) with eXtreme Gradient Boosting (XBoost), ConvXGB. This model was established by stacking two convolutional layers for automatic feature extraction from ECG signals, followed by XGBoost as the last layer, which is used for classification. This technique simplified ECG classification in comparison to other methods by minimizing the number of required parameters and eliminating the need for weight readjustment throughout the backpropagation phase. Furthermore, experiments on two famous ECG datasets–the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets–demonstrated that this technique handled the ECG signal classification issue better than either CNN or XGBoost alone. In addition, a comparison showed that this model outperformed state-of-the-art models, with scores of 0.9938, 0.9839, 0.9836, 0.9837, and 0.9911 for accuracy, precision, recall, F1-score, and specificity, respectively.
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Zheng, Tianxiang, and Pavel Loskot. "Signal Folding for Efficient Classification of Near-Cyclostationary Biological Signals." Mathematics 10, no. 2 (January 8, 2022): 192. http://dx.doi.org/10.3390/math10020192.
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The classification of biological signals is important in detecting abnormal conditions in observed biological subjects. The classifiers are trained on feature vectors, which often constitute the parameters of the observed time series data models. Since the feature extraction is usually the most time-consuming step in training a classifier, in this paper, signal folding and the associated folding operator are introduced to reduce the variability in near-cyclostationary biological signals so that these signals can be represented by models that have a lower order. This leads to a substantial reduction in computational complexity, so the classifier can be learned an order of magnitude faster and still maintain its decision accuracy. The performance of different classifiers involving signal folding as a pre-processing step is studied for sleep apnea detection in one-lead ECG signals assuming ARIMA modeling of the time series data. It is shown that the R-peak-based folding of ECG segments has superior performance to other more general, similarity based signal folding methods. The folding order can be optimized for the best classification accuracy. However, signal folding requires precise scaling and alignment of the created signal fragments.
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Liu, Chang, Ruslan Antypenko, Iryna Sushko, Oksana Zakharchenko, and Ji Wang. "Marine Distributed Radar Signal Identification and Classification Based on Deep Learning." Traitement du Signal 38, no. 5 (October 31, 2021): 1541–48. http://dx.doi.org/10.18280/ts.380531.
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Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.
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Barrera Alvarez, Jayro Lazaro, and Fidel Ernesto Hernandez Montero. "Classification of MPSK Signals through Eighth-Order Statistical Signal Processing." IEEE Latin America Transactions 15, no. 9 (2017): 1601–7. http://dx.doi.org/10.1109/tla.2017.8015041.
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Hadiyoso, Sugondo, Inung Wijayanto, and Annisa Humairani. "Signal Dynamics Analysis for Epileptic Seizure Classification on EEG Signals." Traitement du Signal 38, no. 1 (February 28, 2021): 73–78. http://dx.doi.org/10.18280/ts.380107.
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Epilepsy is the most common form of neurological disease. Patients with epilepsy may experience seizures of a certain duration with or without provocation. Epilepsy analysis can be done with an electroencephalogram (EEG) examination. Observation of qualitative EEG signals generates high cost and often confuses due to the nature of the non-linear EEG signal and noise. In this study, we proposed an EEG signal processing system for EEG seizure detection. The signal dynamics approach to normal and seizure signals' characterization became the main focus of this study. Spectral Entropy (SpecEn) and fractal analysis are used to estimate the EEG signal dynamics and used as feature sets. The proposed method is validated using a public EEG dataset, which included preictal, ictal, and interictal stages using the Naïve Bayes classifier. The test results showed that the proposed method is able to generate an ictal detection accuracy of up to 100%. It is hoped that the proposed method can be considered in the detection of seizure signals on the long-term EEG recording. Thus it can simplify the diagnosis of epilepsy.
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Diab, Mohamad O., Amira El-Merhie, Nour El-Halabi, and Layal Khoder. "Classification of uterine EMG signals using supervised classification method." Journal of Biomedical Science and Engineering 03, no. 09 (2010): 837–42. http://dx.doi.org/10.4236/jbise.2010.39113.
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Ridouh, Abdelhakim, Daoud Boutana, and Salah Bourennane. "EEG Signals Classification Based on Time Frequency Analysis." Journal of Circuits, Systems and Computers 26, no. 12 (August 2017): 1750198. http://dx.doi.org/10.1142/s0218126617501985.
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This paper presents a method to characterize, identify and classify some pathological Electroencephalogram (EEG) signals. We use some Time Frequency Distributions (TFDs) to analyze its nonstationarity. The analysis is conducted by the spectrogram (SP), the Choi–Williams Distribution (CWD) and the Smoothed Pseudo Wigner Ville Distribution (SPWVD). The studies are carried on some real EEG signals collected from a known database. The estimation of the best value of parameters for each distribution is achieved using the Rényi entropy (RE). The time-frequency results have permitted to characterize some pathological EEG signals. In addition, the Rényi Marginal Entropy (RME) is used for the purpose of detecting the peak seizures and discriminates between normal and pathological EEG signals. The frequency bands are evaluated using the Marginal Frequency (MF). The EEG signal classification of two sets A and E containing normal and pathologic EEG signals, respectively, is performed using our proposed method based on energy extraction of signals from time-frequency plane. Also, the Moving Average (MA) is used as a tool to obtain better classification results. The results conducted on real-life EEG signals illustrate the effectiveness of the proposed method.
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Sharan, Roneel V., Hao Xiong, and Shlomo Berkovsky. "Benchmarking Audio Signal Representation Techniques for Classification with Convolutional Neural Networks." Sensors 21, no. 10 (May 14, 2021): 3434. http://dx.doi.org/10.3390/s21103434.
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Audio signal classification finds various applications in detecting and monitoring health conditions in healthcare. Convolutional neural networks (CNN) have produced state-of-the-art results in image classification and are being increasingly used in other tasks, including signal classification. However, audio signal classification using CNN presents various challenges. In image classification tasks, raw images of equal dimensions can be used as a direct input to CNN. Raw time-domain signals, on the other hand, can be of varying dimensions. In addition, the temporal signal often has to be transformed to frequency-domain to reveal unique spectral characteristics, therefore requiring signal transformation. In this work, we overview and benchmark various audio signal representation techniques for classification using CNN, including approaches that deal with signals of different lengths and combine multiple representations to improve the classification accuracy. Hence, this work surfaces important empirical evidence that may guide future works deploying CNN for audio signal classification purposes.
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Xu, Jian Fei, Fu Ping Wang, and Zan Ji Wang. "A New Classification Algorithm of MPSK Signals Based on Phase Distribution." Applied Mechanics and Materials 446-447 (November 2013): 1028–33. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.1028.
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Based on the phase distribution which is defined in this paper, a new classification algorithm of M-ary phase shift keying (PSK) signals is proposed. To classify the modulation type of the M-ary PSK signal, the phase distribution of the unclassified signal is calculated firstly, and then the characters of the signal modulation are extracted by computing the FFT of the phase distribution. Moreover, the method is improved in this paper that it is extended to MPSK baseband signals with frequency offset, and the calculation complexity is reduced. Simulation result shows that the accuracy rate of the classification of BPSK, QPSK and 8PSK signals can reach 98.5% when the symbol length is 500, SNR is 3dB, and 16PSK signals can also be well classified when the SNR improves to 9dB.
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Zhang, Zhichao, Anton Umek, and Anton Kos. "COMPUTERIZED RADIAL ARTERY PULSE SIGNAL CLASSIFICATION FOR LUNG CANCER DETECTION." Facta Universitatis, Series: Mechanical Engineering 15, no. 3 (December 9, 2017): 535. http://dx.doi.org/10.22190/fume170504021z.
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Pulse diagnosis, the main diagnosis method in traditional Chinese medicine, is a non-invasive and convenient way to check the health status. Doctors usually use three fingers to feel three positions; Cun, Guan, and Chi of the wrist pulse, to diagnose the body’s healthy status. However, it takes many years to master the pulse diagnosis. This paper aims at finding the best position for acquiring wrist-pulse-signal for lung cancer diagnosis. In our paper, the wrist-pulse-signals of Cun, Guan, and Chi are acquired by three optic fiber pressure sensors of the same type. Twelve features are extracted from the signals of these three positions, respectively. Eight classifiers are applied to detect the effectiveness of the signal acquired from each position by classifying the pulse signals of healthy individuals and lung cancer patients. The results achieved by the proposed features show that the signal acquired at Cun is more effective for lung cancer diagnosis than the signals acquired at Guan and Chi.
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Li, Junhua. "Thoughts on neurophysiological signal analysis and classification." Brain Science Advances 6, no. 3 (September 2020): 210–23. http://dx.doi.org/10.26599/bsa.2020.9050020.
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Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.
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TSUJI, TOSHIO, NAN BU, JUN ARITA, and MAKOTO OHGA. "A SPEECH SYNTHESIZER USING FACIAL EMG SIGNALS." International Journal of Computational Intelligence and Applications 07, no. 01 (March 2008): 1–15. http://dx.doi.org/10.1142/s1469026808002119.
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This paper proposes a novel phoneme classification method using facial electromyography (EMG) signals. This method makes use of differential EMG signals between muscles for phoneme classification, which enables a speech synthesizer to be constructed using fewer electrodes. The EMG signal is derived as a differential between monopolar electrodes attached to two different muscles, unlike conventional methods in which the EMG signal is derived as a differential between bipolar electrodes attached to the same muscle. Frequency-based feature patterns are then extracted using a filter bank, and the phonemes are classified using a probabilistic neural network, called a reduced-dimensional log-linearized Gaussian mixture network (RD-LLGMN). Since RD-LLGMN merges feature extraction and pattern classification processes into a single network structure, a lower-dimensional feature set that is consistent with classification purposes can be extracted; consequently, classification performance can be improved. Experimental results indicate that the proposed method with a fewer number of electrodes can achieve a considerably high classification accuracy.
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Jadhav, Chetan M., and V. K. Bairagi. "Detection & Classification of Cardiac Arrhythmia." International Journal of Informatics and Communication Technology (IJ-ICT) 6, no. 1 (June 22, 2017): 31. http://dx.doi.org/10.11591/ijict.v6i1.pp31-36.
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<p>The term Arrhythmia refers to any change from the normal sequence in the electrical impulses. It is also treated as abnormal heart rhythms or irregular heartbeats. The rate of growth of Cardiac Arrhythmia disease is very high & its effects can be observed in any age group in society. Arrhythmia detection can be done in many ways but effective & simple method for detection & diagnosis of Cardiac Arrhythmia is by doing analysis of Electrocardiogram signals from ECG sensors. ECG signal can give us the detail information of heart activities, so we can use ECG signals to detect the rhythm & behaviour of heart beats resulting into detection & diagnosis of Cardiac Arrhythmia. In this paper new & improved methodology for early Detection & Classification of Cardiac Arrhythmia has been proposed. In this paper ECG signals are captured using ECG sensors & this ECG signals are used & processed to get the required data regarding heart beats of the human being & then proposed methodology applies for Detection & Classification of Cardiac Arrhythmia. Detection of Cardiac Arrhythmia using ECG signals allows us for easy & reliable way with low cost solution to diagnose Arrhythmia in its prior early stage.</p>
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Mala, Younus H., and Mahmud A. Mohammad. "Brain Waves Signal Modeling for Object Classification Using Random Forest Method." Science Journal of University of Zakho 10, no. 1 (March 8, 2022): 16–23. http://dx.doi.org/10.25271/sjuoz.2022.10.1.876.
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In this research, the connection between human vision information and simultaneous brain signal is studied; to this end, an experiment has been made. Clearly, brain wave signals are captured in the situation that the participants are looking at the specific object. More precisely, the brain signals of 9 shapes are recorded for each participant. Also, 9 participants voluntarily have been involved in the experiment. Then, the collected signals are organised into training and testing groups. After that Random Forest classifier is used to classify the signals.
The accuracy results demonstrate a connection between human vision information and simultaneous brain signal. Overall accuracy for all shapes as separated as per cases is 20.48%, and for shapes, numbers 6 and 8 are 55.34% 36.57%, respectively. It can be concluded that human brain signals can be categorised based on human vision inputs.
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Wei, Yangjie, Shiliang Fang, and Xiaoyan Wang. "Automatic Modulation Classification of Digital Communication Signals Using SVM Based on Hybrid Features, Cyclostationary, and Information Entropy." Entropy 21, no. 8 (July 30, 2019): 745. http://dx.doi.org/10.3390/e21080745.
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Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.
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Saini, Manish Kumar, Rajiv Kapoor, and Bharat Bhushan Sharma. "PQ Event Classification Using Fuzzy Classifier." Advanced Materials Research 403-408 (November 2011): 3854–58. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3854.
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The work presented here uses multiwavelet because of its inherent property to resolve the signal better than all single wavelets. Multiwavelets are based on more than one scaling function. The proposed methodology utilizes an enhanced resolving capability of multiwavelet to recognize power quality events. PQ events classification scheme is performed using multiwavelet transform for feature extraction and fuzzy classifier for classification. In proposed algorithm,statistical features (.i.e. mean, standard deviation, variation etc.) and energy of the signal at different decomposition levels have been considered as feature vectors. The performance of fuzzy classifier has been evaluated by using total 1000 PQ disturbance signals which are generated using the based model. The classification performance of different PQ events using proposed algorithm has been tested. The rate of average correct classification is about 99.95% for the different PQ disturbance signals and noisy disturbances.
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Yoo, Hyun-Joon, Hyeong-jun Park, and Boreom Lee. "Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning." Sensors 19, no. 10 (May 23, 2019): 2370. http://dx.doi.org/10.3390/s19102370.
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Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, research on optimal algorithms and electrode placements for the processing of sEMG signals is still inconclusive. In addition, very few studies have focused on minimizing the number of electrodes. In this study, we investigated the most effective method for myoelectric signal classification with a small number of electrodes. A total of 23 subjects participated in the study, and the sEMG data of 14 different hand movements of the subjects were acquired from targeted muscles and untargeted muscles. Furthermore, the study compared the classification accuracy of the sEMG data using discriminative feature-oriented dictionary learning (DFDL) and other conventional classifiers. DFDL demonstrated the highest classification accuracy among the classifiers, and its higher quality performance became more apparent as the number of channels decreased. The targeted method was superior to the untargeted method, particularly when classifying sEMG signals with DFDL. Therefore, it was concluded that the combination of the targeted method and the DFDL algorithm could classify myoelectric signals more effectively with a minimal number of channels.
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Khade, Darshan A., and Ilakiyaselvan N. "SCENE AND OBJECT CLASSIFICATION USING BRAIN WAVES SIGNAL." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 137. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19495.
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This study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology.
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Ridouh, Abdelhakim, Daoud Boutana, and Salah Bourennane. "EEG Signals Classification Using Support Vector Machine." Advanced Science, Engineering and Medicine 12, no. 2 (February 1, 2020): 215–24. http://dx.doi.org/10.1166/asem.2020.2490.
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We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods.
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An, Qi, Zi-shu He, Hui-yong Li, and Yong-hua Li. "Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2398464.
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Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.
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Athavale, Yashodhan, Sridhar Krishnan, and Aziz Guergachi. "Pattern Classification of Signals Using Fisher Kernels." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/467175.
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The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software. The biomedical domain study involves knee signals collected using the vibration arthrometry technique. This study uses the severity of cartilage degeneration for classifying normal and abnormal knee joints. In both studies, we apply Fisher Kernels incorporated with a Gaussian mixture model (GMM) for dimension transformation into feature space, which is created as a three-dimensional plot for visualization and for further classification using support vector machines. From our experiments we observe that Fisher Kernel usage fits really well for both kinds of signals, with low classification error rates.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 2, no. 3 (August 30, 2016): 35. http://dx.doi.org/10.26417/ejis.v2i3-35-44.
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Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 2, no. 3 (August 30, 2016): 35. http://dx.doi.org/10.26417/ejis.v2i3.35-44.
Full textAbstract:
Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 2, no. 3 (August 30, 2016): 35. http://dx.doi.org/10.26417/ejis.v2i3.p35-44.
Full textAbstract:
Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Altın, Cemil, and Orhan Er. "Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG." European Journal of Interdisciplinary Studies 5, no. 1 (August 30, 2016): 35. http://dx.doi.org/10.26417/ejis.v5i1.p35-44.
Full textAbstract:
Objective:In this study we will get EMG signals from arm for different elbow gestures, than filtering the signal and later classification the signal. The reason for doing is that, EMG signals are used for many rehabilitation and assistive prostheses of paralyzed or injured people. Methods:Filtering a biological signal is the key point for these type studies. Filtering the EMG signals needed and starts with the elimination of the 50 Hz mains supply noise. After filtering the signal, feature extraction will be applied for both wrist flexion and wrist extension cases. There are many feature extraction methods for time and frequency domain. After feature extraction, classification of hand movements will be studied using extracted features. Classification is made using K Nearest Neighbor algorithm. The dataset used in this study is acquired by the EMG signal acquisition tool and belong to us. Results:90 % accuracy performance is obtained by K Nearest Neighbor algorithm purposed signal classification. Conclusion:This system is capable of conducting the classification process with a good performance to biomedical studies. So,this structure can be helpful as machine-learning based decision support system for medical purpose.
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Yao, Gang, Yunce Wang, Mohamed Benbouzid, and Mourad Ait-Ahmed. "A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM." Applied Sciences 11, no. 11 (May 28, 2021): 4996. http://dx.doi.org/10.3390/app11114996.
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In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.
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Wen, Tingxi, Yu Du, Ting Pan, Chuanbo Huang, and Zhongnan Zhang. "A Deep Learning-Based Classification Method for Different Frequency EEG Data." Computational and Mathematical Methods in Medicine 2021 (October 21, 2021): 1–13. http://dx.doi.org/10.1155/2021/1972662.
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In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the EEG data. Thus, the adaptability of EEG classification methods has become significant. This study proposed a deep network model for autonomous learning and classification of EEG signals, which could self-adaptively classify EEG signals with different sampling frequencies and lengths. The artificial design feature extraction methods could not obtain stable classification results when analyzing EEG data with different sampling frequencies. However, the proposed depth network model showed considerably better universality and classification accuracy, particularly for EEG signals with short length, which was validated by two datasets.
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Zhu, Zijiang, Hang Chen, Song Xie, Yi Hu, and Jing Chang. "Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (July 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/6548811.
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The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors’ clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients’ physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician’s diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of c and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.
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Salimy, Alireza, Imene Mitiche, Philip Boreham, Alan Nesbitt, and Gordon Morison. "Dynamic Noise Reduction with Deep Residual Shrinkage Networks for Online Fault Classification." Sensors 22, no. 2 (January 10, 2022): 515. http://dx.doi.org/10.3390/s22020515.
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Fault signals in high-voltage (HV) power plant assets are captured using the electromagnetic interference (EMI) technique. The extracted EMI signals are taken under different conditions, introducing varying noise levels to the signals. The aim of this work is to address the varying noise levels found in captured EMI fault signals, using a deep-residual-shrinkage-network (DRSN) that implements shrinkage methods with learned thresholds to carry out de-noising for classification, along with a time-frequency signal decomposition method for feature engineering of raw time-series signals. The approach will be to train and validate several alternative DRSN architectures with previously expertly labeled EMI fault signals, with architectures then being tested on previously unseen data, the signals used will firstly be de-noised and a controlled amount of noise will be added to the signals at various levels. DRSN architectures are assessed based on their testing accuracy in the varying controlled noise levels. Results show DRSN architectures using the newly proposed residual-shrinkage-building-unit-2 (RSBU-2) to outperform the residual-shrinkage-building-unit-1 (RSBU-1) architectures in low signal-to-noise ratios. The findings show that implementing thresholding methods in noise environments provides attractive results and their methods prove to work well with real-world EMI fault signals, proving them to be sufficient for real-world EMI fault classification and condition monitoring.
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Damkilang, Vattanaphong, and Mahasak Ketcham. "Classification of Electromyography Signal with Machine Learning." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 08 (June 28, 2022): 49–60. http://dx.doi.org/10.3991/ijoe.v18i08.30581.
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The problem of classifying electromyography signals in each gesture occurs due to the use of a constant level of signal amplitude. This research presents an efficiency enhancement of electromyography signal classification in each gesture with machine learning. The performance efficiency of 5 models: SVM, RF, MLP, KNN, and Deep Leaning was compared. The signals were recorded by a low-cost signal sensor. Fist clenching and hand opening gestures were alternately performed every 5 second for 5 times each. Therefore, the total was 4,767 records divided into 3,274 records of hand opening gesture, 1,492 records of fist clenching gesture and 4,833 records of wrist rotating gesture. The results showed that the MLP model was found to have the highest accuracy at 81.45% for fist clenching and hand opening gestures. The Deep Learning model was found to have the highest accuracy at 89.03% for wrist rotating and hand opening gestures.
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Wan, Jian, Guoqing Ruan, Qiang Guo, and Xue Gong. "A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA." Symmetry 10, no. 11 (November 20, 2018): 659. http://dx.doi.org/10.3390/sym10110659.
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Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.
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Snider, R. K., and A. B. Bonds. "Classification of non-stationary neural signals." Journal of Neuroscience Methods 84, no. 1-2 (October 1998): 155–66. http://dx.doi.org/10.1016/s0165-0270(98)00110-1.
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Dufaux, Alain, Laurent Besacier, Michael Ansorge, and Fausto Pellandini. "Automatic classification of wideband acoustic signals." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1359–60. http://dx.doi.org/10.1121/1.426440.
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Tzanetakis, G., and P. Cook. "Musical genre classification of audio signals." IEEE Transactions on Speech and Audio Processing 10, no. 5 (July 2002): 293–302. http://dx.doi.org/10.1109/tsa.2002.800560.
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