Heart Sound Cancellation Based on Multiscale Products and Linear Prediction

2007 ◽  
Vol 54 (2) ◽  
pp. 234-243 ◽  
Author(s):  
Daniel Flores-Tapia ◽  
Zahra M. K. Moussavi ◽  
Gabriel Thomas
Keyword(s):  
Linear Prediction ◽  
Heart Sound ◽  
Multiscale Products ◽  
Sound Cancellation

Heart sound cancellation from lung sound record using cyclostationarity

2013 ◽  
Vol 35 (12) ◽  
pp. 1831-1836 ◽  
Author(s):  
Ting Li ◽  
Hong Tang ◽  
Tianshuang Qiu ◽  
Yongwan Park
Keyword(s):  
Heart Sound ◽  
Lung Sound ◽  
Sound Cancellation

scholarly journals Performance Evaluation of Heart Sound Cancellation in FPGA Hardware Implementation for Electronic Stethoscope

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chun-Tang Chao ◽  
Nopadon Maneetien ◽  
Chi-Jo Wang ◽  
Juing-Shian Chiou
Keyword(s):  
Heart Sound ◽  
Sound Processing ◽  
Electronic Stethoscope ◽  
Gate Arrays ◽  
Implementation Evaluation ◽  
Breath Sounds ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Hardware Circuit ◽  
Sound Cancellation

This paper presents the design and evaluation of the hardware circuit for electronic stethoscopes with heart sound cancellation capabilities using field programmable gate arrays (FPGAs). The adaptive line enhancer (ALE) was adopted as the filtering methodology to reduce heart sound attributes from the breath sounds obtained via the electronic stethoscope pickup. FPGAs were utilized to implement the ALE functions in hardware to achieve near real-time breath sound processing. We believe that such an implementation is unprecedented and crucial toward a truly useful, standalone medical device in outpatient clinic settings. The implementation evaluation with one Altera cyclone II–EP2C70F89 shows that the proposed ALE used 45% resources of the chip. Experiments with the proposed prototype were made using DE2-70 emulation board with recorded body signals obtained from online medical archives. Clear suppressions were observed in our experiments from both the frequency domain and time domain perspectives.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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