Motor threshold in transcranial magnetic stimulation: comparison of three estimation methods.

AIMS: Motor threshold (MT) is an important parameter for the practice of transcranial magnetic stimulation. Our goal was to compare three methods to estimate MT in a clinical setting. METHODS: Comparison of three MT estimation algorithms: 1) the Rossini-Rothwell method consists in lowering stimulus intensity until only five positive responses out of 10 trials are recorded, defining MT; 2) the Mills-Nithi method considers the MT as the mean of an upper threshold (10 positive out of 10 trials) and a lower threshold (0 out of 10 trials); 3) the supervised parametric method estimates the MT by fitting (mathematically and graphically) a sigmoid function on raw data obtained by stimulation at variable intensities. Six MT estimations (two per method) were recorded in a single session in 10 healthy subjects. RESULTS: The within-subject variation of MT (expressed as % of the mean MT+/-standard deviation) during a single session was of 8.5+/-7.2% for the Rossini-Rothwell method, 8.7+/-5.7% for the Mills-Nithi method and 9.5+/-4.0% for the supervised parametric method. No significant differences in variability of MT estimation were found between the methods, but the Rossini-Rothwell method was significantly shorter (half the number of stimuli compared to the two other methods). CONCLUSION: In our setting, Rossini-Rothwell method was superior to the two other methods. The variability of MT estimation measured in our study is important, yet acceptable for clinical applications. However, this variability can be a source of considerable errors in excitability studies and should be a focus of future research.

Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound.

The objective of the study was to develop a non-invasive method for the estimation of pulmonary arterial pressure (PAP) using a neural network (NN) and features extracted from the second heart sound (S2). To obtain the information required to train and test the NN, an animal model of pulmonary hypertension (PHT) was developed, and nine pigs were investigated. During the experiments, the electrocardiogram, phonocardiogram and PAP were recorded. Subsequently, between 15 and 50 S2 heart sounds were isolated for each PAP stage and for each animal studied. A Coiflet wavelet decomposition and a pseudo smoothed Wigner-Ville distribution were used to extract features from the S2 sounds and train a one-hidden-layer NN using two-thirds of the data. The NN performance was tested on the remaining one-third of the data. NN estimates of the systolic and mean PAPs were obtained for each S2 and then ensemble averaged over the 15-50 S2 sounds selected for each PAP stage. The standard errors between the mean and systolic PAPs estimated by the NN and those measured with a catheter were 6.0 mmHg and 8.4 mmHg, respectively, and the correlation coefficients were 0.89 and 0.86, respectively. The classification accuracy, using 23 mmHg mean PAP and 30 mmHg systolic PAP thresholds between normal PAP and PHT, was 97% and 91%, respectively.