James Nicholas Watson

CONTINUOUS WAVELET TRANSFORM MODULUS MAXIMA ANALYSIS OF THE ELECTROCARDIOGRAM: BEAT CHARACTERISATION AND BEAT-TO-BEAT MEASUREMENT

The problem of automatic beat recognition in the ECG is tackled using continuous wavelet transform modulus maxima (CWTMM). Features within a variety of ECG signals can be shown to correspond to various morphologies in the CWTMM domain. This domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of this enhanced time-frequency analysis technique for ECG signals, an R-wave detector is developed and tested using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.70% and a positive predictive value of 99.68%).

A FULLY AUTOMATED ALGORITHM FOR THE DETERMINATION OF RESPIRATORY RATE FROM THE PHOTOPLETHYSMOGRAM

Objective. To determine if an automatic algorithm using wavelet analysis techniques can be used to reliably determine respiratory rate from the photoplethysmogram (PPG). Methods. Photoplethysmograms were obtained from 12 spontaneously breathing healthy adult volunteers. Three related wavelet transforms were automatically polled to obtain a measure of respiratory rate. This was compared with a secondary timing signal obtained by asking the volunteers to actuate a small push button switch, held in their right hand, in synchronisation with their respiration. In addition, individual breaths were resolved using the wavelet-method to identify the source of any discrepancies. Results. Volunteer respiratory rates varied from 6.56 to 18.89 breaths per minute. Through training of the algorithm it was possible to determine a respiratory rate for all 12 traces acquired during the study. The maximum error between the PPG derived rates and the manually determined rate was found to be 7.9%. Conclusion. Our technique allows the accurate measurement of respiratory rate from the photoplethysmogram, and leads the way for developing a simple non-invasive combined respiration and saturation monitor.

Evaluating arrhythmias in ECG signals using wavelet transforms

Recent work has attempted to utilize wavelet techniques in the analysis of biomedical signals including ECGs. Here, the authors present an energy-based method of interrogating the ECG in VF using high-resolution, log-scale continuous wavelet plots. With this method, underlying structures within the VF waveform are made visible in the wavelet time-scale half space.

A novel time–frequency-based 3D Lissajous figure method and its application to the determination of oxygen saturation from the photoplethysmogram

We present a novel time–frequency method for the measurement of oxygen saturation using the photoplethysmogram (PPG) signals from a standard pulse oximeter machine. The method utilizes the time–frequency transformation of the red and infrared PPGs to derive a 3D Lissajous figure. By selecting the optimal Lissajous, the method provides an inherently robust basis for the determination of oxygen saturation as regions of the time–frequency plane where high- and low-frequency signal artefacts are to be found are automatically avoided.

An Algorithm for the Detection of Individual Breaths from the Pulse Oximeter Waveform

Objectives. To determine if wavelet analysis techniques can be used to reliably identify individual breaths from the photoplethysmogram (PPG). Methods. Photoplethysmograms were obtained from 22 healthy adult volunteers timing their respiration rate in synchronisation with a metronome. A secondary timing signal was obtained by asking the volunteers to actuate a small push button switch, held in their right hand, in synchronisation with their respiration. Each PPG was analyzed using primary wavelet decomposition and two new, related, secondary decompositions to determine the accuracy of individual breath detection. Results. The optimal breath capture was obtained by manually polling the three techniques, allowing detection of 466 out of the 472 breaths studied; a detection rate of 98.7% with no false positive breaths detected. Conclusion. Our technique allows the accurate capture of individual breaths from the photoplethysmogram, and leads the way for developing a simple non-invasive combined respiration and saturation monitor.

R-wave detection using continuous wavelet modulus maxima

Modulus maxima derived from the continuous wavelet transform offers an enhanced time-frequency analysis technique for ECG signal analysis. Features within the ECG can be shown to correspond to various morphologies in the continuous modulus maxima domain. This domain has an easy interpretation and offers a good tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of these properties we have developed an R-wave detector and tested it using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.7% and a positive predictive value of 99.68%).

A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation

We report a new method of interrogating the surface ECG signal using techniques developed in the field of wavelet transform analysis. Previously unreported structure within the ECG during ventricular fibrillation (VF) is found using a high-resolution decomposition of the signal employing the continuous wavelet transform. We believe that wavelet transform methods could lead to the development of powerful tools for use in the resuscitation of patients with cardiac arrest.

Finding coordinated atrial activity during ventricular fibrillation using wavelet decomposition

Until recently, the ECG recorded during ventricular fibrillation was thought to represent disorganized and unstructured electrical activity of the heart. Using a new signal analysis technique based on wavelet decomposition, we have begun to reveal previously unreported structure within the ECG tracing. We report preliminary findings that provide the first evidence linking this structure to unexpected mechanical phenomena occurring in the heart.

Secondary wavelet feature decoupling (SWFD) and its use in detecting patient respiration from the photoplethysmogram

We describe a method for the identification of time-frequency features associated with patient respiration in the wavelet decomposition of the photoplethysmogram where the respiration features are masked by other signal components with similar spectral content. In the novel methodology a secondary transform is performed on a signal derived from the original wavelet decomposition in the region of the pulse band. The method has wide application to many other problematic signals.

SECONDARY TRANSFORM DECOUPLING OF SHIFTED NONSTATIONARY SIGNAL MODULATION COMPONENTS: APPLICATION TO PHOTOPLETHYSMOGRAPHY

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.