36 results match your criteria extracts time-frequency


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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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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A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

PLoS One 2021 26;16(4):e0250618. Epub 2021 Apr 26.

Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan.

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for diagnosing OSA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of OSA diagnosis in public health sectors. Read More

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MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI.

Authors:
Omneya Attallah

Diagnostics (Basel) 2021 Feb 20;11(2). Epub 2021 Feb 20.

Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt.

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Read More

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February 2021

Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM).

Sensors (Basel) 2020 Nov 30;20(23). Epub 2020 Nov 30.

Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Korea.

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Read More

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November 2020

Empirical Mode Decomposition Based Multi-Modal Activity Recognition.

Sensors (Basel) 2020 Oct 24;20(21). Epub 2020 Oct 24.

School of Information Engineering, Guangdong University of Technology, Guangzhou 511006, China.

This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. Read More

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October 2020

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

J Healthc Eng 2020 18;2020:7526825. Epub 2020 May 18.

Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450001, China.

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. Read More

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Time-frequency Relevancy Analysis between Local Field Potentials and Lever Pressing Motion of Rats.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:7169-7172

Cortical Local field potentials (LFPs) in primary motor cortex (M1) include significant movement information. As a movement decoding source, LFPs have the advantage of being long-lasting and stable. However, LFPs contain activity of neural ensemble from neighborhood or even distant cortical areas. Read More

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An improved separation method of multi-components signal for sensing based on time-frequency representation.

Rev Sci Instrum 2019 Jun;90(6):064901

Department of Computer Science and Media Technology, Malmö University, Malmö 20506, Sweden and Internet of Things and People Research Center, Malmö University, Malmö 20506, Sweden.

In many situations, it is essential to analyze a nonstationary signal for sensing whose components not only overlapped in time-frequency domain (TFD) but also have different durations. In order to address this issue, an improved separation method based on the time-frequency distribution is proposed in this paper. This method computes the time-frequency representation (TFR) of the signal and extracts the instantaneous frequency (IF) of components by a two-dimensional peak search in a limited area in which normalized energy is greater than the set threshold value. Read More

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Cross-correlation task-related component analysis (xTRCA) for enhancing evoked and induced responses of event-related potentials.

Neuroimage 2019 08 27;197:177-190. Epub 2019 Apr 27.

Swartz Center for Computational Neuroscience, Institute of Neural Computation, University of California San Diego, 9500 Gilman Drive # 0559, La Jolla, CA, 92093-0559, USA.

We propose an analysis method that extracts trial-reproducible (i.e., recurring) event-related spatiotemporal EEG patterns by optimizing a spatial filter as well as trial timings of task-related components in the time domain simultaneously in a unified manner. Read More

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A SFTD Algorithm for Optimizing the Performance of the Readout Strategy of Residence Time Difference Fluxgate.

Sensors (Basel) 2018 Nov 16;18(11). Epub 2018 Nov 16.

Key Laboratory of Geophysical Exploration Equipment, Ministry of Education (Jilin University), Changchun 130026, China.

Residence time difference (RTD) fluxgate sensor is a potential device to measure the DC or low-frequency magnetic field in the time domain. Nevertheless, jitter noise and magnetic noise severely affect the detection result. A novel post-processing algorithm for jitter noise reduction of RTD fluxgate output strategy based on the single-frequency time difference (SFTD) method is proposed in this study to boost the performance of the RTD system. Read More

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November 2018

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

IEEE Trans Biomed Circuits Syst 2018 02;12(1):171-181

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. Read More

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February 2018

State-space multitaper time-frequency analysis.

Proc Natl Acad Sci U S A 2018 01 18;115(1):E5-E14. Epub 2017 Dec 18.

Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139;

Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Read More

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January 2018

Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during hypsarrhythmia.

Med Biol Eng Comput 2017 Sep 9;55(9):1659-1668. Epub 2017 Feb 9.

Department of Computer and Electrical Engineering, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL, 33431, USA.

Infantile spasms (ISS) is a devastating epileptic syndrome that affects children under the age of 1 year. The diagnosis of ISS is based on the semiology of the seizure and the electroencephalogram (EEG) background characterized by hypsarrhythmia (HYPS). However, even skilled electrophysiologists may interpret the EEG of children with ISS differently, and commercial software or existing epilepsy detection algorithms are not helpful. Read More

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September 2017

Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.

Hear Res 2017 02 30;344:183-194. Epub 2016 Nov 30.

ISVR, University of Southampton, University Rd, Southampton SO17 1BJ, United Kingdom.

Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). Read More

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February 2017

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots.

Front Syst Neurosci 2015 14;9:184. Epub 2016 Jan 14.

Team Neurosys, InriaVillers-lès-Nancy, France; Loria, Centre National de la Recherche Scientifique, UMR no 7503Villers-lès-Nancy, France; Université de Lorraine, Loria, UMR no 7503Villers-lès-Nancy, France.

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Read More

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February 2016

Scanning for oscillations.

J Neural Eng 2015 Dec 26;12(6):066020. Epub 2015 Oct 26.

Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France. Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL* Research University, France. UCL Ear Institute, UK.

Objective: Oscillations are an important aspect of brain activity, but they often have a low signal-to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine oscillations, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time-frequency analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or frequency bands. Read More

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December 2015

One hundred ways to process time, frequency, rate and scale in the central auditory system: a pattern-recognition meta-analysis.

Front Comput Neurosci 2015 3;9:80. Epub 2015 Jul 3.

Science et Technologie de la Musique et du Son, IRCAM/Centre National de la Recherche Scientifique UMR9912/UPMC Paris, France.

The mammalian auditory system extracts features from the acoustic environment based on the responses of spatially distributed sets of neurons in the subcortical and cortical auditory structures. The characteristic responses of these neurons (linearly approximated by their spectro-temporal receptive fields, or STRFs) suggest that auditory representations are formed, as early as in the inferior colliculi, on the basis of a time, frequency, rate (temporal modulations) and scale (spectral modulations) analysis of sound. However, how these four dimensions are integrated and processed in subsequent neural networks remains unclear. Read More

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Enhanced automated sleep spindle detection algorithm based on synchrosqueezing.

Med Biol Eng Comput 2015 Jul 17;53(7):635-44. Epub 2015 Mar 17.

Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, 97239, USA,

Detection of sleep spindles is of major importance in the field of sleep research. However, manual scoring of spindles on prolonged recordings is very laborious and time-consuming. In this paper, we introduce a new algorithm based on synchrosqueezing transform for detection of sleep spindles. Read More

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Doppler radar fall activity detection using the wavelet transform.

IEEE Trans Biomed Eng 2015 Mar 4;62(3):865-75. Epub 2014 Nov 4.

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. Read More

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Identification of microsatellites in DNA using adaptive S-transform.

IEEE J Biomed Health Inform 2015 May 13;19(3):1097-105. Epub 2014 Jun 13.

Microsatellites are tandem repeats of size 1-6 base pairs, associated with various diseases, DNA fingerprinting, and also useful in evolutionary studies. A signal processing algorithm for microsatellite detection, based on adaptive S-transform is proposed. The standard deviation of the Gaussian window kernel of the S-transform has been optimized for integer periods of interest by maximizing the concentration measure. Read More

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Multichannel instantaneous frequency analysis of ultrasound propagating in cancellous bone.

J Acoust Soc Am 2014 Mar;135(3):1197-206

Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan.

An ultrasonic pulse propagating in cancellous bone can be separated into two waves depending on the condition of the specimen. These two waves, which are called the fast wave and the slow wave, provide important information for the diagnosis of osteoporosis. The present study proposes to utilize a signal processing method that extracts the instantaneous frequency (IF) of waveforms from multiple spectral channels. Read More

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Electronic evaluation for video commercials by impression index.

Cogn Neurodyn 2013 Dec 19;7(6):531-5. Epub 2013 Apr 19.

Department of Physiology and Pharmacology, University of Rome "Sapienza", Rome, Italy.

How to evaluate the effect of commercials is significantly important in neuromarketing. In this paper, we proposed an electronic way to evaluate the influence of video commercials on consumers by impression index. The impression index combines both the memorization and attention index during consumers observing video commercials by tracking the EEG activity. Read More

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December 2013

Network dynamics predict improvement in working memory performance following donepezil administration in healthy young adults.

Neuroimage 2014 03 21;88:228-41. Epub 2013 Nov 21.

ElMindA Ltd., Herzliya, Israel; Electrical and Computer Engineering, Ben Gurion University of the Negev, Beer Sheba, Israel.

Attentional selection in the context of goal-directed behavior involves top-down modulation to enhance the contrast between relevant and irrelevant stimuli via enhancement and suppression of sensory cortical activity. Acetylcholine (ACh) is believed to be involved mechanistically in such attention processes. The objective of the current study was to examine the effects of donepezil, a cholinesterase inhibitor that increases synaptic levels of ACh, on the relationship between performance and network dynamics during a visual working memory (WM) task involving relevant and irrelevant stimuli. Read More

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Automated detection of instantaneous gait events using time frequency analysis and manifold embedding.

IEEE Trans Neural Syst Rehabil Eng 2013 Nov 11;21(6):908-16. Epub 2013 Jan 11.

Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. Read More

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November 2013

Benefits of multi-domain feature of mismatch negativity extracted by non-negative tensor factorization from EEG collected by low-density array.

Int J Neural Syst 2012 Dec;22(6):1250025

Department of Mathematical Information Technology, University of Jyväskylä, Finland.

Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-domain feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new feature of MMN significantly discriminated the two groups of children. This is because the feature was derived from multi-domain information with significant reduction of the heterogeneous effect of datasets. Read More

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December 2012

Real-time, time-frequency mapping of event-related cortical activation.

J Neural Eng 2012 Aug 19;9(4):046018. Epub 2012 Jul 19.

Department of Neurological Surgery, UC San Francisco, CA, USA.

Functional mapping of eloquent cortex is a common and necessary component of neurosurgical operative planning. Current electrical stimulation-based techniques are inefficient, can evoke seizures and are prone to false-negative results. Here, we present a novel cortical mapping system that extracts event-related neural activity from passive electrocorticographic recordings to quickly and accurately localize sensory and motor cortices using the precise temporal properties of spectral alteration. Read More

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Quadratic component analysis.

Neuroimage 2012 Feb 4;59(4):3838-44. Epub 2011 Nov 4.

Laboratoire de Psychologie de la Perception, UMR 8581, CNRS and Université Paris Descartes, France.

I present a method for analyzing multichannel recordings in response to repeated stimulus presentation. Quadratic Component Analysis (QCA) extracts responses that are stimulus-induced (triggered by the stimulus but not precisely locked in time), as opposed to stimulus-evoked (time-locked to the stimulus). Induced responses are often found in neural response data from magnetoencephalography (MEG), electroencephalography (EEG), or multichannel electrophysiological and optical recordings. Read More

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February 2012

An adaptive filter-based method for robust, automatic detection and frequency estimation of whistles.

J Acoust Soc Am 2011 Aug;130(2):893-903

Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK.

This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Read More

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Spectral Spatio-Temporal template extraction from EEG signals.

Annu Int Conf IEEE Eng Med Biol Soc 2010 ;2010:4678-82

Embedded Systems and Signal Processing Lab, Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.

Analysis of Event Related Potentials (ERPs) produced by brain activities can provide insight into the timing of underlying brain function. ERPs can be classified by their time/frequency characteristics and spatial location on the scalp. Traditionally, ERPs are manually located by temporally and spatially averaged EEG signals. Read More

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