MATERIALS AND METHODS: Seven cephalometric variables (facial angle, ANB, maxillary depth, U1/FH, FMA, IMPA, FMIA) were measured by a dentist in 60 Malay subjects (30 males and 30 females) with class I occlusion and balanced face. Two standard images were taken for each subject with conventional cephalometric radiography and MicroScribe-3DXL. All the images were traced and analysed. SPSS version 2.0 was used for statistical analysis with P-value was set at P<0.05.
RESULTS: The results revealed a significant statistic difference in four measurements (U1/FH, FMA, IMPA, FMIA) with P-value range (0.00 to 0.03). The difference in the measurements was considered clinically acceptable. The overall reliability of MicroScribe-3DXL was 92.7% and its validity was 91.8%.
CONCLUSION: The MicroScribe-3DXL is reliable and valid to most of the cephalometric variables with the advantages of saving time and cost. This is a promising device to assist in diverse areas in dental practice and research.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.