Feature fusion method for pulmonary tuberculosis patient detection based on cough sound.

Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis techniques have proven to be effective in detecting respiratory diseases. In 2021, there were up to 10 million TB-infected patients worldwide, with an annual growth rate of 4.5%. Most of the patients were from economically underdeveloped regions and countries. The PPD test, a common screening method in the community, has a sensitivity of as low as 77%. Although IGRA and Xpert MTB/RIF offer high specificity and sensitivity, their cost makes them less accessible. In this study, we proposed a feature fusion model-based cough sound classification method for primary TB screening in communities. Data were collected from hospitals using smart phones, including 230 cough sounds from 70 patients with TB and 226 cough sounds from 74 healthy subjects. We employed Bi-LSTM and Bi-GRU recurrent neural networks to analyze five traditional feature sets including the Mel frequency cepstrum coefficient (MFCC), zero-crossing rate (ZCR), short-time energy, root mean square, and chroma_cens. The incorporation of features extracted from the speech spectrogram by 2D convolution training into the Bi-LSTM model enhanced the classification results. With traditional futures, the best TB patient detection result was achieved with the Bi-LSTM model, with 93.99% accuracy, 93.93% specificity, and 92.39% sensitivity. When combined with a speech spectrogram, the classification results showed 96.33% accuracy, 94.99% specificity, and 98.13% sensitivity. Our findings underscore that traditional features and deep features have good complementarity when fused using Bi LSTM modelling, which outperforms existing PPD detection methods in terms of both efficiency and accuracy.

Robust optimization of a MEMS accelerometer considering temperature variations.

A robust optimization approach for a MEMS accelerometer to minimize the effects of temperature variations is presented. The mathematical model of the accelerometer is built. The effects of temperature variations on the output performance of the accelerometer are determined, and thermal deformation of the accelerometer is analyzed. The deviations of the output capacitance and resonance frequency due to temperature fluctuations are calculated and discussed. The sensitivity analysis method is employed to determine the design variables for robust optimization and find out the key structural parameters that have most significant influence on the output capacitance and resonance frequency of the accelerometer. The mathematical model and procedure for the robust optimization of the accelerometer are proposed. The robust optimization problem is solved and discussed. The robust optimization results show that an optimized accelerometer with high sensitivity, high temperature robustness and decoupling structure is finally obtained.