Secondary Transform Decoupling of Shifted Nonstationary Signal Modulation Components: Application to Photoplethysmography

Abstract

We present a method for the detection of pertinent signal features masked by other features with similar spectral content but which perturb (but not necessarily periodically) other constituent parts of the signal. This modulation may be a modulation in frequency and/or amplitude of a locus of chosen selected points on a transform surface. A secondary transform is then performed on this derived signal. We apply the method, which we have termed secondary wavelet feature decoupling (SWFD), to the specific problem of decomposition and analysis of signals used in pulse oximetry where perturbations of the ridges in wavelet space associated with the pulse band may be used to detect respiration features. The method has wide application to problematic signals in many other disciplines.

Real-time chest-wall-motion tracking by a single optical fibre grating: a prospective method for ventilator triggering

“…The additional requirement for accepting this global threshold was that the frequency corresponding to the peak power spectral density within physiologically plausible cardiac frequency range (30-220 beats per minute) was in all subjects higher than the pre-selected global threshold. [Addison and Watson 2004]. c) frequency content (power spectrum density (PSD)) of respiratory and heart activity components of the signal.…”

Wavelet‐Based Analysis of Heart‐Rate‐Dependent ECG Features

“…In particular, we have shown how the sudden onset of a rapid frequency increase associated with the fusion region of a pacing signal transitioning between intrinsic and ventricular conduction can be tracked using wavelet‐ridge‐following methods. This could particularly prove useful for signals containing excessive noise where signal‐peak‐picking algorithms may break down as the ridge‐following methods are generally more resistant to noisy perturbations 13 . An algorithm using a suitable trigger threshold for the rate of increase could be incorporated within the current devices; in this way these regions of concern may be detected in real time using a suitably programmed medical device.…”

“…An algorithm using a suitable trigger threshold for the rate of increase could be incorporated within the current devices; in this way these regions of concern may be detected in real time using a suitably programmed medical device. It is envisaged that wavelet band detection, selection, and interrogation methods that we have developed elsewhere for other biosignals 13,14 could be used to achieve this. Early, accurate, and noise‐resistant identification of the onset of ventricular conduction would have a wide range of uses within medical devices.…”

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

“…Wavelet ridges are useful for determining instantaneous frequencies and amplitudes of signal components [5]. Ridge analysis assumes that oscillatory properties can be represented by wavelet ridge curves in time-frequency space, and are based on characteristics discerned from the wavelet transform [22][23][24]. An estimator for a modulated oscillatory signal is then obtained through the MsCWT values evaluated along the ridge.…”