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Typical EMG Classification accuracy rate is given in Table 3. Accurate estimation of MUAP templates in the presence of background EMG activity and instrumentation noise is an important requirement of quantitative clinical EMG analysis, especially if EMG signal decomposition is utilized.
The most common and typical method used in the present study is to assess classification accuracy with the help of error matrix (Congalton 1991).
In the receiver operator characteristic analysis, the metric with the greatest classification accuracy in the discrimination between PCA and typical Alzheimer's disease was saccade amplitude error, which had a sensitivity of 93.8% and a specificity of 83.3%.
Classification accuracy was 89%.
The classification accuracy was evaluated by the same method.
Classification accuracy for each individual domain and the effects of removing each domain on the classification accuracy.
Two-sided t-tests for independent samples on the peak classification accuracy, latency to peak classification accuracy, and mean classification accuracy at 100 250 ms post-onset confirmed this interpretation of the data.
The classification accuracy is more than 95.9%.
The accuracy was evaluated for each 500-ms window, and the maximum value of the accuracies was estimated as the classification accuracy.
The class decoder was trained at the peak classification accuracy of the offline task.
A cross-validation leave-one-stimulus-out procedure was adopted to measure classification accuracy.
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