The global Beta test for hidden periodicities in signals and its extensions to multivariate systems.

BACKGROUND AND OBJECTIVE: There are many phenomena that lead to changes in the power spectrum of a given signal, and their detection has been a challenge that has received considerable attention over the years. Objective Response Detection (ORD) techniques are a set of tools that perform automated tests for such a task, allowing thus to automatically track changes in the spectrum. The performance of these detectors is affected by the signal-to-noise ratio (SNR) of the recorded signal as well as the length of the available data. The Global F Test (GFT) is a promising detector that can be used to test whether there is a statistically significant difference between the spectrum before and during an event. In fact, this detector has proved useful in the detection of event-related desynchronization/synchronization (ERD/ERS), where only amplitude, but not the phase, changes are locked to the stimulus. In order to improve the statistical power of the GFT (for the same length of recording), multiple channels recorded simultaneously can be included. This concept is called Multivariate Response Detection. The aim of the current work is to extend the GFT to the multivariate (multichannel) case. METHODS: Firstly, the single channel normalization of the GFT is presented as a new ORD detector - the global Beta test (GBT). After that, three multivariate extensions of this new test are derived. The critical values used in the detection of spectral changes are obtained by using theoretical distributions, and where this is intractable, by means of Monte Carlo simulations. The probability of detection (PD) of each technique was estimated using simulation and was used in order to compare the detectors performance. A practical example with the electroencephalogram (EEG) from 10 volunteers under intermittent photic stimulation was also provided. RESULTS: The statistics under both the null and alternative hypothesis could be obtained for all detectors. Simulated results for PD demonstrate the strong potential of the proposed method and the performances in EEG data are always improved with increasing number of signals. CONCLUSION: If more than one signal is available, then the multivariate extensions may provide significant benefit compared to the original GFT.

Recovery coefficients determination for partial volume effect correction in oncological PET/CT images considering the effect of activity outside the field of view.

OBJECTIVES: The partial volume effect (PVE) has a great impact in quantitative PET/CT imaging. Correction methods have been recently proposed by many authors to make the image quantification more accurate. This work presents a methodology for determining the recovery coefficients (RCs) for PVE correction in PET/CT images. It was taken into account the radioactivity outside the field of view (FOV), which is expected in a patient image acquisition. METHODS: The NEMA image quality phantom and the NEMA scatter phantom were used. The phantoms were filled with (18)F-FDG for different sphere-to-background ratios. The RCs have been determined from image acquisitions in a Siemens Biograph 16 Hi-Rez PET/CT scanner with and without the scatter phantom. RESULTS: The RC values that ranged from 0.38 to 1.00 without the scatter phantom exhibited a wider variation when this latter was taken into account (from 0.27 to 1.02). This more realistic estimation must be considered if one takes into account that an incorrect SUV measure in tumors leads to errors in the evaluation of the response to therapy based on PET/CT images. CONCLUSIONS: The activity outside the FOV should be considered in RCs determination to improve the RC-based PVE correction method.

Gain and coherence estimates between respiration and heart-rate: differences between inspiration and expiration.

The interaction of respiration and heart-rate variability (HRV), leading to respiratory sinus arrhythmia (RSA) and, in the inverse direction, cardioventilatory coupling has been subject of much study and controversy. A parametric linear feedback model can be used to study these interactions. In order to investigate differences between inspiratory and expiratory periods, we propose that models are estimated separately for each period, by finding least mean square estimates only over the desired signal segments. This approach was tested in simulated data and heart-rate and respiratory air flow signals recorded from 25 young healthy adults (13 men and 12 women), at rest, breathing spontaneously through a face mask for 5 min. The results show significant differences (p<0.05) between the estimates of coherence obtained from the whole recording, and the inspiration and expiration periods. Simple and causal coherence from respiration to HRV was higher during inspiration than expiration. The estimates of gain also differed significantly in the high frequency (HF) band (0.15-0.5Hz) between those obtained from the whole recording, and the inspiratory and expiratory periods. These results indicate that a single linear model fitted to the whole recording neglects potentially important differences between inspiration and expiration, and the current paper shows how such differences can be estimated, without the need to control breathing.