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.

Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG

2017 ◽  
Vol 27 (04) ◽  
pp. 1750005 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Qing Cai ◽  
Yu-Xuan Yang ◽  
Na Dong ◽  
Shan-Shan Zhang
Keyword(s):  
Epileptic Seizure ◽  
Healthy Subjects ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Eeg Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Brain States ◽  
Optimal Kernel

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.

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 Time-Frequency Representation of Motor Evoked Potentials in Brain Tumor Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Kathrin Machetanz ◽  
Alberto L. Gallotti ◽  
Maria Teresa Leao Tatagiba ◽  
Marina Liebsch ◽  
Leonidas Trakolis ◽  
...  
Keyword(s):  
Time Series ◽  
Brain Tumor ◽  
Time Domain ◽  
Healthy Subjects ◽  
Motor System ◽  
Motor Evoked Potentials ◽  
Tumor Patients ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Status

Background: The integrity of the motor system can be examined by applying navigated transcranial magnetic stimulation (nTMS) to the cortex. The corresponding motor-evoked potentials (MEPs) in the target muscles are mirroring the status of the human motor system, far beyond corticospinal integrity. Commonly used time domain features of MEPs (e.g., peak-to-peak amplitudes and onset latencies) exert a high inter-subject and intra-subject variability. Frequency domain analysis might help to resolve or quantify disease-related MEP changes, e.g., in brain tumor patients. The aim of the present study was to describe the time-frequency representation of MEPs in brain tumor patients, its relation to clinical and imaging findings, and the differences to healthy subject.Methods: This prospective study compared 12 healthy subjects with 12 consecutive brain tumor patients (with and without a paresis) applying nTMS mapping. Resulting MEPs were evaluated in the time series domain (i.e., amplitudes and latencies). After transformation into the frequency domain using a Morlet wavelet approach, event-related spectral perturbation (ERSP), and inter-trial coherence (ITC) were calculated and compared to diffusion tensor imaging (DTI) results.Results: There were no significant differences in the time series characteristics between groups. MEPs were projecting to a frequency band between 30 and 300 Hz with a local maximum around 100 Hz for both healthy subjects and patients. However, there was ERSP reduction for higher frequencies (>100 Hz) in patients in contrast to healthy subjects. This deceleration was mirrored in an increase of the inter-peak MEP latencies. Patients with a paresis showed an additional disturbance in ITC in these frequencies. There was no correlation between the CST integrity (as measured by DTI) and the MEP parameters.Conclusion: Time-frequency analysis may provide additional information above and beyond classical MEP time domain features and the status of the corticospinal system in brain tumor patients. This first evaluation indicates that brain tumors might affect cortical physiology and the responsiveness of the cortex to TMS resulting in a temporal dispersion of the corticospinal transmission.

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.

scholarly journals Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramon J. Boekesteijn ◽  
José M. H. Smolders ◽  
Vincent J. J. F. Busch ◽  
Alexander C. H. Geurts ◽  
Katrijn Smulders
Keyword(s):  
Dual Task ◽  
Wearable Sensors ◽  
Body Motion ◽  
Effect Sizes ◽  
Upper Body ◽  
Healthy Controls ◽  
Gait Parameters ◽  
Dual Task Cost ◽  
Dual Task Performance ◽  
Spatiotemporal Parameters

Abstract Background Although it is well-established that osteoarthritis (OA) impairs daily-life gait, objective gait assessments are not part of routine clinical evaluation. Wearable inertial sensors provide an easily accessible and fast way to routinely evaluate gait quality in clinical settings. However, during these assessments, more complex and meaningful aspects of daily-life gait, including turning, dual-task performance, and upper body motion, are often overlooked. The aim of this study was therefore to investigate turning, dual-task performance, and upper body motion in individuals with knee or hip OA in addition to more commonly assessed spatiotemporal gait parameters using wearable sensors. Methods Gait was compared between individuals with unilateral knee (n = 25) or hip OA (n = 26) scheduled for joint replacement, and healthy controls (n = 27). For 2 min, participants walked back and forth along a 6-m trajectory making 180° turns, with and without a secondary cognitive task. Gait parameters were collected using 4 inertial measurement units on the feet and trunk. To test if dual-task gait, turning, and upper body motion had added value above spatiotemporal parameters, a factor analysis was conducted. Effect sizes were computed as standardized mean difference between OA groups and healthy controls to identify parameters from these gait domains that were sensitive to knee or hip OA. Results Four independent domains of gait were obtained: speed-spatial, speed-temporal, dual-task cost, and upper body motion. Turning parameters constituted a gait domain together with cadence. From the domains that were obtained, stride length (speed-spatial) and cadence (speed-temporal) had the strongest effect sizes for both knee and hip OA. Upper body motion (lumbar sagittal range of motion), showed a strong effect size when comparing hip OA with healthy controls. Parameters reflecting dual-task cost were not sensitive to knee or hip OA. Conclusions Besides more commonly reported spatiotemporal parameters, only upper body motion provided non-redundant and sensitive parameters representing gait adaptations in individuals with hip OA. Turning parameters were sensitive to knee and hip OA, but were not independent from speed-related gait parameters. Dual-task parameters had limited additional value for evaluating gait in knee and hip OA, although dual-task cost constituted a separate gait domain. Future steps should include testing responsiveness of these gait domains to interventions aiming to improve mobility.

scholarly journals Method for designing multi-input system identification signals using a compact time-frequency representation

2021 ◽  
Author(s):  
Mathias Stefan Roeser ◽  
Nicolas Fezans
Keyword(s):  
System Identification ◽  
Design Method ◽  
Flight Test ◽  
Compact Representation ◽  
Time Frequency ◽  
Stability And Control ◽  
Frequency Representation ◽  
Aerodynamic Parameters ◽  
Definition Of ◽  
And Control

AbstractA flight test campaign for system identification is a costly and time-consuming task. Models derived from wind tunnel experiments and CFD calculations must be validated and/or updated with flight data to match the real aircraft stability and control characteristics. Classical maneuvers for system identification are mostly one-surface-at-a-time inputs and need to be performed several times at each flight condition. Various methods for defining very rich multi-axis maneuvers, for instance based on multisine/sum of sines signals, already exist. A new design method based on the wavelet transform allowing the definition of multi-axis inputs in the time-frequency domain has been developed. The compact representation chosen allows the user to define fairly complex maneuvers with very few parameters. This method is demonstrated using simulated flight test data from a high-quality Airbus A320 dynamic model. System identification is then performed with this data, and the results show that aerodynamic parameters can still be accurately estimated from these fairly simple multi-axis maneuvers.

Sign in / Sign up

Export Citation Format

Share Document