Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

Wachowiak, Mark P.; Wachowiak-Smolikova, Renata; Johnson, Michel J.; Hay, Dean C.; Power, Kevin E.; Williams-Bell, F. Michael

doi:10.1098/rsta.2017.0250

Cited by 32 publications

(24 citation statements)

References 28 publications

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“…The CWT is computed using the analytic Morse wavelet with the symmetry parameter equal to 3 and the time-bandwidth product equal to 60. The generalized Morse wavelets were preferred because of their time and frequency localization performance and their capability in isolating and extracting features in the time–frequency domain [ 47 ]. Moreover, CWT was preferred with respect to DWT because of its more fine-grained resolution.…”

Section: Methodsmentioning

confidence: 99%

A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Perpetuini

Cardone

Filippini

et al. 2021

Sensors

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Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes’ movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes’ movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes’ movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.

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Section: Methodsmentioning

confidence: 99%

A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Perpetuini

Cardone

Filippini

et al. 2021

Sensors

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“…A 1-dimensional (downsampled) subsequence is transformed into the 2-dimensional time-frequency domain using continuous wavelet transform (CWT), i.e., the two axes of the response image correspond to time and frequency resolutions. We employ the complex Morlet wavelet, the default choice for wavelet analysis [11] for its simplicity…”

Section: F Wavelet-based Feature Extractionmentioning

confidence: 99%

Wavelet-based CNN for Predicting PAP Adherence Using Overnight Polysomnography Recordings: a Pilot Study

Lei

Maxim

Valladares

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Obstructive sleep apnea (OSA) is a common sleep disorder. Positive airway pressure (PAP) therapy is the first-line treatment, while its effectiveness is significantly limited by incomplete adherence in many patients. This work aims to find a predictive association between data from in-laboratory sleep studies during treatment (PAP titration polysomnogram, or PSG) and PAP adherence. Based on a PAP titration PSG database, we present a pipeline to develop a wavelet-based deep learning model and address two challenges. First, to tackle the problem of extremely long overnight PSG signals, it randomly draws segments and extracts features locally. The global representation for the entire signal is achieved by local feature P-norm pooling. Second, to tackle the problem of limited dataset size, the pre-trained EfficienNet-B7 is used as an unsupervised feature extractor to transfer ImageNet knowledge to PSG signals in the wavelet domain. The trained pipeline achieves 78% balanced accuracy and 83% AUC on the test set using airflow and frontal EEG signals, which, we believe, is a compelling result as a pilot study. Clinical relevance-Polysomnogram signals may improve clinical treatment of OSA by identifying patients with low likelihood of PAP adherence, enabling intensive efforts to improve adherence or consider alternative therapies.

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“…Three papers concerning medical applications of the CWT are then presented. These are breath sound analysis and pain characterization using higher order spectral techniques, including the instantaneous wavelet bispectrum, biamplitude, biphase and bicoherence by Hadjileontiadis [6]; the analysis of ECG and EMG biosignals using wavelet ridge analysis and wavelet-based dispersion measures by Wachowiak et al [7]; and a study of intracranial pressure during lumbar infusion tests by Garcia et al [8] using wavelet entropy, Jensen divergence and spectral flux. CWT applications are not restricted to time series: this is wonderfully illustrated in the analysis of the intricate spatial properties of the radial ring structure of Saturn in this issue by Tiscareno & Hedman [9].…”

Section: Who Is Using the Continuous Wavelet Transform?mentioning

confidence: 99%

Introduction to redundancy rules: the continuous wavelet transform comes of age

Addison¹

2018

Phil. Trans. R. Soc. A.

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Redundancy: it is a word heavy with connotations of lacking usefulness. I often hear that the rationale for not using the continuous wavelet transform (CWT)-even when it appears most appropriate for the problem at hand-is that it is 'redundant'. Sometimes the conversation ends there, as if self-explanatory. However, in the context of the CWT, 'redundant' is not a pejorative term, it simply refers to a less compact form used to represent the information within the signal. The benefit of this new form-the CWT-is that it allows for intricate structural characteristics of the signal information to be made manifest within the transform space, where it can be more amenable to study: resolution over redundancy. Once the signal information is in CWT form, a range of powerful analysis methods can then be employed for its extraction, interpretation and/or manipulation. This theme issue is intended to provide the reader with an overview of the current state of the art of CWT analysis methods from across a wide range of numerate disciplines, including fluid dynamics, structural mechanics, geophysics, medicine, astronomy and finance.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

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Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

Cited by 32 publications

References 28 publications

A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Wavelet-based CNN for Predicting PAP Adherence Using Overnight Polysomnography Recordings: a Pilot Study

Introduction to redundancy rules: the continuous wavelet transform comes of age

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