907 results match your criteria time-frequency features


Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Int J Neural Syst 2021 Jul 16:2150032. Epub 2021 Jul 16.

Nanyang Technological University, Singapore.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification. Read More

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Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG-Based Brain Network.

Comput Intell Neurosci 2021 23;2021:5565824. Epub 2021 Jun 23.

Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China.

Compared with the efficacy of traditional physical therapy, a new therapy utilizing motor imagery can induce brain plasticity and allows partial recovery of motor ability in patients with hemiplegia after stroke. Here, we proposed an updated paradigm utilizing motor coordination imagery involving the lower limbs (normal gait imagery and hemiplegic gait imagery after stroke) and decoded such imagery via an electroencephalogram- (EEG-) based brain network. Thirty subjects were recruited to collect EEGs during motor coordination imagery involving the lower limbs. Read More

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A novel method based on matching pursuit decomposition of gait signals for Parkinson's disease, Amyotrophic lateral sclerosis and Huntington's disease detection.

Neurosci Lett 2021 Jul 10;761:136107. Epub 2021 Jul 10.

Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran. Electronic address:

Background And Objective: An accurate detection of neurodegenerative diseases (NDDs) definitely improves the life of patients and has attracted growing attention.

Methods: In this paper, a general automatic method for detection of Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) has been proposed based on the localized time-frequency information of gait signals. The new main part of the detection method is to obtain a small set of sparse coefficients for the local representation of gait signals with appropriate time and frequency resolution. Read More

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Effective multi-sensor data fusion for chatter detection in milling process.

ISA Trans 2021 Jul 5. Epub 2021 Jul 5.

Industry 4.0 Implementation Center, Center of Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Department of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., B. O. Box 11241, Cairo, Egypt.

This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. Read More

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Deep learning-based direction-of-arrival estimation for multiple speech sources using a small scale array.

J Acoust Soc Am 2021 Jun;149(6):3841

Hangzhou Applied Acoustics Research Institute, Hangzhou, Zhejiang 310012, China.

A high resolution direction-of-arrival (DOA) approach is presented based on deep neural networks (DNNs) for multiple speech sources localization using a small scale array. First, three invariant features from the time-frequency spectrum of the input signal include generalized cross correlation (GCC) coefficients, GCC coefficients in the mel-scaled subband, and the combination of GCC coefficients and logarithmic mel spectrogram. Then the DNN labels are designed to fit the Gaussian distribution, which is similar to the spatial spectrum of the multiple signal classification. Read More

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Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.

Authors:
Tuan D Pham

Sci Rep 2021 Jul 1;11(1):13703. Epub 2021 Jul 1.

Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, 31952, Saudi Arabia.

Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. Read More

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Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition.

Sensors (Basel) 2021 Jun 24;21(13). Epub 2021 Jun 24.

Ocean and Mechanical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431, USA.

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. Read More

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V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier.

Sensors (Basel) 2021 Jun 23;21(13). Epub 2021 Jun 23.

COSYS-LEOST, University Gustave Eiffel, IFSTTAR, F-59650 Villeneuve d'Ascq, France.

Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Read More

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Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.

Comput Biol Med 2021 Jun 20;135:104589. Epub 2021 Jun 20.

Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.

Introduction: Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their cognitive workload, captured while they undertake routine fetal ultrasound examinations. Our experiments and analysis are performed on real-world datasets obtained using remote eye-tracking under natural clinical environmental conditions. Read More

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Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model.

Comput Math Methods Med 2021 29;2021:6665357. Epub 2021 May 29.

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.

In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians' confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. Read More

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A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models.

Comput Intell Neurosci 2021 2;2021:9955130. Epub 2021 Jun 2.

School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.

In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi-Williams distribution (CWD) transformed. Read More

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[Research on feature classification of lower limb motion imagination based on electrical stimulation to enhance rehabilitation].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2021 Jun;38(3):425-433

Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, P.R.China.

Motor imaging therapy is of great significance to the rehabilitation of patients with stroke or motor dysfunction, but there are few studies on lower limb motor imagination. When electrical stimulation is applied to the posterior tibial nerve of the ankle, the steady-state somatosensory evoked potentials (SSSEP) can be induced at the electrical stimulation frequency. In order to better realize the classification of lower extremity motor imagination, improve the classification effect, and enrich the instruction set of lower extremity motor imagination, this paper designs two experimental paradigms: Motor imaging (MI) paradigm and Hybrid paradigm. Read More

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A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

PLoS One 2021 25;16(6):e0253094. Epub 2021 Jun 25.

Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia.

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. Read More

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Deep convolutional neural networks based ECG beats classification to diagnose cardiovascular conditions.

Biomed Eng Lett 2021 May 16;11(2):147-162. Epub 2021 Feb 16.

Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW 2010 Australia.

Medical practitioners need to understand the critical features of ECG beats to diagnose and identify cardiovascular conditions accurately. This would be greatly facilitated by identifying the significant features of frequency components in temporal ECG wave-forms using computational methods. In this study, we have proposed a novel ECG beat classifier based on a customized VGG16-based Convolution Neural Network (CNN) that uses the time-frequency representation of temporal ECG, and a method to identify the contribution of interpretable ECG frequencies when classifying based on the SHapley Additive exPlanations (SHAP) values. Read More

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AF episodes recognition using optimized time-frequency features and cost-sensitive SVM.

Phys Eng Sci Med 2021 Jun 17. Epub 2021 Jun 17.

Institut IRIMAS, Université de Haute Alsace, 68093, Mulhouse, France.

Although atrial fibrillation (AF) Arrhythmia is highly prevalent within a wide range of populations with major associated risks and due to its episodic occurrence, its recognition remains a challenge for doctors. This paper aims to present and experimentally validate a new efficient approach for the detection and classification of this cardiac anomaly using multiple Electrocardiogram (ECG) signals. This work consists of applying Stockwell transform (ST) with compact support kernel (ST-CSK) for ECG time-frequency analysis. Read More

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Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks.

Physiol Meas 2021 Jun 29;42(6). Epub 2021 Jun 29.

Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara 06560, Turkey.

In this study, we conducted a comparative analysis of deep convolutional neural network (CNN) models in predicting obstructive sleep apnea (OSA) using electrocardiograms. Unlike other studies in the literature, this study automatically extracts time-frequency features by using CNNs instead of manual feature extraction from ECG recordings.The proposed model generates scalogram and spectrogram representations by transforming preprocessed 30 s ECG segments from time domain to the frequency domain using continuous wavelet transform and short time Fourier transform, respectively. Read More

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Children ASD Evaluation Through Joint Analysis of EEG and Eye-Tracking Recordings With Graph Convolution Network.

Front Hum Neurosci 2021 25;15:651349. Epub 2021 May 25.

School of Computer Science, Wuhan University, Wuhan, China.

Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram (EEG) and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder (ASD) than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags behind the increasing requirement in clinical or educational practices as the "bio-marker" information carried by the bio-signal of a single-modality is likely insufficient or distorted. This study proposes an approach to joint analysis of EEG and eye-tracking for children ASD evaluation. Read More

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IMU-Based Deep Neural Networks: Prediction of Locomotor and Transition Intentions of an Osseointegrated Transfemoral Amputee.

IEEE Trans Neural Syst Rehabil Eng 2021 15;29:1079-1088. Epub 2021 Jun 15.

This paper focuses on the design and comparison of different deep neural networks for the real-time prediction of locomotor and transition intentions of one osseointegrated transfemoral amputee using only data from inertial measurement units. The deep neural networks are based on convolutional neural networks, recurrent neural networks, and convolutional recurrent neural networks. The architectures' input are features in both the time domain and the time-frequency domain, which are derived from either one inertial measurement unit (placed above the prosthetic knee) or two inertial measurement units (placed above and below the prosthetic knee). Read More

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Surface Electromyography and Electroencephalogram-Based Gait Phase Recognition and Correlations Between Cortical and Locomotor Muscle in the Seven Gait Phases.

Front Neurosci 2021 21;15:607905. Epub 2021 May 21.

The Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

The classification of gait phases based on surface electromyography (sEMG) and electroencephalogram (EEG) can be used to the control systems of lower limb exoskeletons for the rehabilitation of patients with lower limb disorders. In this study, the slope sign change (SSC) and mean power frequency (MPF) features of EEG and sEMG were used to recognize the seven gait phases [loading response (LR), mid-stance (MST), terminal stance (TST), pre-swing (PSW), initial swing (ISW), mid-swing (MSW), and terminal swing (TSW)]. Previous researchers have found that the cortex is involved in the regulation of treadmill walking. Read More

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Heartbeats Classification Using Hybrid Time-Frequency Analysis and Transfer Learning Based on ResNet.

IEEE J Biomed Health Inform 2021 Jun 2;PP. Epub 2021 Jun 2.

The classification of heartbeats is an important method for cardiac arrhythmia analysis. This study proposes a novel heartbeat classification method using hybrid time-frequency analysis and transfer learning based on ResNet-101. The proposed method has the following major advantages over the afore-mentioned methods: it avoids the need for manual features extraction in the traditional machine learning method, and it utilizes 2-D time-frequency diagrams which provide not only frequency and energy information but also preserve the morphological characteristic within the ECG recordings, and it owns enough deep to make better use of performance of CNN. Read More

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Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm.

Entropy (Basel) 2021 May 31;23(6). Epub 2021 May 31.

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.

A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. Read More

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High Accuracy WiFi-Based Human Activity Classification System with Time-Frequency Diagram CNN Method for Different Places.

Sensors (Basel) 2021 May 30;21(11). Epub 2021 May 30.

Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan.

Older people are very likely to fall, which is a significant threat to the health. However, falls are preventable and are not necessarily an inevitable part of aging. Many different fall detection systems have been developed to help people avoid falling. Read More

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Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis.

Sensors (Basel) 2021 May 25;21(11). Epub 2021 May 25.

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, such as singular value decomposition (SVD) and reweighted SVD (RSVD), were proposed recently to solve this problem. However, such technologies also face failure in bearing fault detection due to the poor interpretability of the obtained eigenvector. Read More

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Urodynamic and clinical features in women with overactive bladder: When to suspect concomitant voiding dysfunction?

Neurourol Urodyn 2021 Aug 26;40(6):1509-1514. Epub 2021 May 26.

Departamento de Urología, Pontificia Universidad Católica de Chile, Santiago, Chile.

Aim: The aim of this study is to describe the prevalence and type of female voiding dysfunction (FVD) in patients with overactive bladder (OAB) who were studied by urodynamics and its relationship with voiding symptoms.

Methods: This is a cross-sectional study of female adult patients with OAB syndrome who underwent UDS in a University Hospital in Chile between January 2015 and April 2020. FVD was defined either as bladder outlet obstruction (BOO) or detrusor underactivity (DU). Read More

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Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG.

Neural Plast 2021 27;2021:6644365. Epub 2021 Apr 27.

Graduate School of Engineering, Saitama Institute of Technology, 369-0293, Japan.

Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection of the epileptogenic lesion. However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. Read More

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Corrigendum to "Cardiotocography signal abnormality classification using time-frequency features and ensemble cost-sensitive SVM classifier" [Comput. Biol. Med. 130 (2021) 104218].

Comput Biol Med 2021 Jul 7;134:104466. Epub 2021 May 7.

Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China. Electronic address:

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Deep convolution stack for waveform in underwater acoustic target recognition.

Sci Rep 2021 May 5;11(1):9614. Epub 2021 May 5.

Guangzhou Key Laboratory of Multilingual Intelligent Processing, Guangdong University of Foreign Studies, Guangzhou, 510006, China.

In underwater acoustic target recognition, deep learning methods have been proved to be effective on recognizing original signal waveform. Previous methods often utilize large convolutional kernels to extract features at the beginning of neural networks. It leads to a lack of depth and structural imbalance of networks. Read More

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Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

Comput Biol Med 2021 06 25;133:104434. Epub 2021 Apr 25.

Birla Institute of Technology & Science, Pilani, K K Birla Goa Campus, Goa, 403 726, India.

The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, including time-consuming inspections, the need for highly trained neurologists, and the subjectiveness of the evaluation. Thus, an automated EEG pathology detection system would be helpful to assist neurologists to enhance the treatment procedure by making a quicker diagnosis and reducing error due to the human element. Read More

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Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing.

Entropy (Basel) 2021 Apr 24;23(5). Epub 2021 Apr 24.

School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number K and penalty factor α. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. Read More

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Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles.

Sensors (Basel) 2021 Apr 22;21(9). Epub 2021 Apr 22.

School of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China.

Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Read More

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