scholarly journals A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6446
Author(s):  
Tahjid Ashfaque Mostafa ◽  
Sara Soltaninejad ◽  
Tara L. McIsaac ◽  
Irene Cheng
Keyword(s):  
Short Term Memory ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Network Architectures ◽  
Time Frequency ◽  
Frequency Representation ◽  
Fog Prediction ◽  
Advanced Stages ◽  
Short Time ◽  
Representation Techniques

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.

scholarly journals A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction

2021 ◽  
Author(s):  
Tahjid Ashfaque Mostafa ◽  
Sara Soltaninejad ◽  
Tara L. McIsaac ◽  
Irene Cheng
Keyword(s):  
Short Term Memory ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Network Architectures ◽  
Time Frequency ◽  
Frequency Representation ◽  
Fog Prediction ◽  
Advanced Stages ◽  
Short Time ◽  
Representation Techniques

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, it’s detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

Evaluation of potential auras in generalized epilepsy from EEG signals using deep convolutional neural networks and time-frequency representation

2020 ◽  
Vol 65 (4) ◽  
pp. 379-391 ◽  
Author(s):  
Hasan Polat ◽  
Mehmet Ufuk Aluçlu ◽  
Mehmet Siraç Özerdem
Keyword(s):  
Eeg Signals ◽  
Deep Convolutional Neural Networks ◽  
One Dimensional ◽  
Time Frequency ◽  
Specificity And Sensitivity ◽  
Frequency Representation ◽  
Proposed Model ◽  
Generalized Epilepsy ◽  
Short Time

AbstractThe general uncertainty of epilepsy and its unpredictable seizures often affect badly the quality of life of people exposed to this disease. There are patients who can be considered fortunate in terms of prediction of any seizures. These are patients with epileptic auras. In this study, it was aimed to evaluate pre-seizure warning symptoms of the electroencephalography (EEG) signals by a convolutional neural network (CNN) inspired by the epileptic auras defined in the medical field. In this context, one-dimensional EEG signals were transformed into a spectrogram display form in the frequency-time domain by applying a short-time Fourier transform (STFT). Systemic changes in pre-epileptic seizure have been described by applying the CNN approach to the EEG signals represented in the image form, and the subjective EEG-Aura process has been tried to be determined for each patient. Considering all patients included in the evaluation, it was determined that the 1-min interval covering the time from the second minute to the third minute before the seizure had the highest mean and the lowest variance to determine the systematic changes before the seizure. Thus, the highest performing process is described as EEG-Aura. The average success for the EEG-Aura process was 90.38 ± 6.28%, 89.78 ± 8.34% and 90.47 ± 5.95% for accuracy, specificity and sensitivity, respectively. Through the proposed model, epilepsy patients who do not respond to medical treatment methods are expected to maintain their lives in a more comfortable and integrated way.

Assessment of time–frequency representation techniques for thoracic sounds analysis

2014 ◽  
Vol 114 (3) ◽  
pp. 276-290 ◽  
Author(s):  
B.A. Reyes ◽  
S. Charleston-Villalobos ◽  
R. González-Camarena ◽  
T. Aljama-Corrales
Keyword(s):  
Time Frequency ◽  
Frequency Representation ◽  
Representation Techniques

A review of time–frequency representations, with application to sound/music analysis–resynthesis

1997 ◽  
Vol 2 (3) ◽  
pp. 193-205 ◽  
Author(s):  
PAUL MASRI ◽  
ANDREW BATEMAN ◽  
NISHAN CANAGARAJAH
Keyword(s):  
Music Analysis ◽  
Intermediate Representation ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Time Domain Signal ◽  
Analysis Process ◽  
Frequency Representation ◽  
The Time Domain ◽  
Higher Order Spectra ◽  
Short Time

Analysis–resynthesis (A–R) systems gain their flexibility for creative transformation of sound by representing sound as a set of musically useful features. The analysis process extracts these features from the time domain signal by means of a time–frequency representation (TFR). The TFR provides an intermediate representation of sound that must make the features accessible and measurable to the rest of the analysis. Until very recently, the short-time Fourier transform (STFT) has been the obvious choice for time–frequency representation, despite its limitations in terms of resolution. Recent and ongoing developments are providing several alternative schemes that allow for a more considered choice of TFR. This paper reviews these contemporary approaches in comparison with the more classical ones and with reference to their applicability, merits and shortcomings for application to sound analysis. (Where they have been successfully applied, details are provided.) The techniques reviewed include linear, bilinear and higher-order spectra, nonparametric and parametric methods and some sound-model-specific TFRs.

scholarly journals Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3705 ◽  
Author(s):  
Delaram Jarchi ◽  
Dario Salvi ◽  
Lionel Tarassenko ◽  
David Clifton
Keyword(s):  
Respiratory Rate ◽  
Healthy Subjects ◽  
Wearable Sensors ◽  
Ground Truth ◽  
Longest Common Subsequence ◽  
Upper Body ◽  
Time Frequency ◽  
Frequency Representation ◽  
Common Subsequence ◽  
Highly Correlated

Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.

scholarly journals Time-frequency Analysis of Multicomponent LFM signal based on Hough and Chirplet Transform

2018 ◽  
Vol 173 ◽  
pp. 03054
Author(s):  
Xueqin Zhang ◽  
Ruolun Liu
Keyword(s):  
Fourier Transform ◽  
Line Detection ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
Chirplet Transform ◽  
Frequency Map ◽  
Linear Frequency Modulated Signal ◽  
Short Time

The Chirplet Transform (CT) is effective in the characterization of IF for mono-component linear-frequency-modulated signal. However, During the initialization process, using the peak of the time-frequency map of the short-time Fourier transform to fit the line is greatly affected by noise. For the multi-component signals, it is more difficult to distinguish and fit different IF lines. Since the Hough is good at a common algorithm for the line detection, the ridge edge fitting is replaced by the Hough transform in this paper. The experiment results show significant improvement in the obtained time-frequency representation.

scholarly journals Applications of an Improved Time-Frequency Filtering Algorithm to Signal Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Junbo Long ◽  
Haibin Wang ◽  
Daifeng Zha ◽  
Hongshe Fan ◽  
Zefeng Lao ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Stable Distribution ◽  
Signal Reconstruction ◽  
Order Moment ◽  
Frequency Filtering ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Noise Environment ◽  
Frequency Representation ◽  
Short Time

The short time Fourier transform time-frequency representation (STFT-TFR) method degenerates, and the corresponding short time Fourier transform time-frequency filtering (STFT-TFF) method fails underαstable distribution noise environment. A fractional low order short time Fourier transform (FLOSTFT) which takes advantage of fractionalporder moment is proposed forαstable distribution noise environment, and the corresponding FLOSTFT time-frequency representation (FLOSTFT-TFR) algorithm is presented in this paper. We study vector formulation of the FLOSTFT and inverse FLOSTFT (IFLOSTFT) methods and propose a FLOSTFT time-frequency filtering (FLOSTFT-TFF) method which takes advantage of time-frequency localized spectra of the signal in time-frequency domain. The simulation results show that, employing the FLOSTFT-TFR method and the FLOSTFT-TFF method with an adaptive weight function, time-frequency distribution of the signals can be better gotten and time-frequency localized region of the signal can be effectively extracted fromαstable distribution noise, and also the original signal can be restored employing the IFLOSTFT method. Their performances are better than the STFT-TFR and STFT-TFF methods, and MSEs are smaller in differentαand GSNR cases. Finally, we apply the FLOSTFT-TFR and FLOSTFT-TFF methods to extract fault features of the bearing outer race fault signal and restore the original fault signal fromαstable distribution noise; the experimental results illustrate their performances.

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