The effect of segment selection on acoustic analysis.

OBJECTIVE/HYPOTHESIS: Acoustic analysis is a commonly used method for quantitatively measuring vocal fold function. Voice signals are analyzed by selecting a waveform segment and using various algorithms to arrive at parameters such as jitter, shimmer, and signal-to-noise ratio (SNR). Accurate and reliable methods for selecting a representative vowel segment have not been established. STUDY DESIGN: Prospective repeated-measure experiment. METHODS: We applied a moving window method by isolating consecutive, overlapping segments of the raw voice signal from onset through offset. Ten normal voice signals were analyzed using acoustic measures calculated from the moving window. The location and value of minimum perturbation/maximum SNR was compared across individuals. The moving window method was compared with data from the whole vowel excluding onset and offset, the mid-vowel, and the visually selected steadiest portion of the voice signal. RESULTS: Results showed that the steadiest portion of the waveforms, as defined by minimum perturbation and maximum SNR values, was not consistent across individuals. Perturbation and nonlinear dynamic values differed significantly based on what segment of the waveform was used. Other commonly used segment selection methods resulted in significantly higher perturbation values and significantly lower SNR values than those determined by the moving window method (P<0.001). CONCLUSIONS: The selection of a sample for acoustic analysis can introduce significant inconsistencies into the analysis procedure. The moving window technique may provide more accurate and reliable acoustic measures by objectively identifying the steadiest segment of the voice sample.

Evaluation of focal cortical dysplasia and mixed neuronal and glial tumors in pediatric epilepsy patients using 18F-FDG and 11C-methionine pet.

UNLABELLED: Focal cortical dysplasia (FCD) and mixed neuronal and glial tumors share many clinical characteristics; therefore, the presurgical differential diagnosis of these diseases using MRI is difficult in some cases. The aim of this study was to determine whether (11)C-methionine PET, compared with (18)F-FDG PET, was useful for the evaluation of these diseases. METHODS: The clinical and imaging data of 30 pediatric lesional epilepsy patients pathologically diagnosed with FCD, dysembryoplastic neuroepithelial tumor (DNT), or ganglioglioma were reviewed. Eleven patients had FCD, 8 patients had a DNT, and 11 patients had a ganglioglioma. (18)F-FDG and (11)C-methinine PET scans were obtained from 25 patients and 15 patients, respectively. Visual grading analysis and quantitative assessment of (18)F-FDG and (11)C-methionine PET, represented as a lesion-to-gray matter ratio (LGR), were performed. RESULTS: In the visual grading analysis, both (18)F-FDG PET and (11)C-methionine PET detected a significant difference among the 3 disease groups (P = 0.033 and P = 0.016, respectively), but discrimination of FCD from mixed neuronal and glial tumors was possible only with (11)C-methionine PET. The mean LGR of (18)F-FDG PET was 0.502 +/- 0.119 for FCD, 0.631 +/- 0.107 for DNTs, and 0.620 +/- 0.196 for gangliogliomas; there was no significant difference in LGR among the groups (P = 0.111). However, the mean LGR of (11)C-methionine PET was 1.078 +/- 0.182 for FCD, 1.564 +/- 0.368 for DNT, and 2.114 +/- 0.723 for gangliogliomas; there was a significant difference in LGR among the groups (P = 0.014). Post hoc analysis revealed that the LGR of FCD was significantly different from that of DNTs and gangliogliomas. The mean LGR value of DNTs fell between those of FCD and gangliogliomas. CONCLUSION: Although (18)F-FDG plays a major role in the preoperative work-up of epilepsy surgery patients, it appears from this study that (18)F-FDG does not contribute to the differential diagnosis and that another tracer such as (11)C-methinine is required. (11)C-methinine PET results correlated well with the pathologic spectrum in pediatric lesional epilepsy patients.