A mathematical algorithm for ECG signal denoising using window analysis

The Effect of Moving Window on Acoustic Analysis

OBJECTIVE

To investigate the effects of the moving window method on acoustic measures and discrimination ability between normal and disordered voices.

METHODS

Fifty-three normal voices and 50 disordered voices were recruited. Three selection methods, the moving window method, the mid-vowel method, and the whole vowel method, were applied to each raw audio signal to determine the most stable segment of each signal. Acoustic parameters such as percent jitter, percent shimmer, signal-to-noise ratio (SNR), cepstral peak prominence (CPP), and correlation dimension (D2) were calculated. The Wilcoxon test was used to compare the stability of these segments across different methods. An artificial neural network was used for estimating how well disordered voices were discriminated from normal ones.

RESULTS

Segments selected using the moving window method were more stable than those selected using the other two methods, meaning lower perturbation and nonlinear dynamic measurements as well as higher SNR and CPP values. The discrimination accuracy rate for the moving window method was 91.90 ± 8.73%, whereas the mid-vowel method and the whole vowel method were 72.34 ± 12.94% and 70.34 ± 5.24%, respectively.

CONCLUSION

The moving window method is capable of providing a more stable audio segment and can discriminate disordered voices from normal ones more effectively.

Integration of gold nanoparticles in PDMS microfluidics for lab-on-a-chip plasmonic biosensing of growth hormones

Gold nanoparticles were synthesized in a poly(dimethylsiloxane) (PDMS) microfluidic chip by using an in-situ method, on the basis of reductive properties of the cross-linking agent of PDMS. The proposed integrated device was further used as a sensitive and low-cost LSPR-based biosensor for the detection of polypeptides. Synthesis of nanoparticles in the microfluidic environment resulted in improvement of size distribution with only 8% variation, compared with the macro-environment that yields about 67% variation in size. The chemical kinetics of the in-situ reaction in the microfluidic environment was studied in detail and compared with the reaction carried out at the macro-scale. The effect of temperature and gold precursor concentration on the kinetics of the reaction was investigated and the apparent activation energy was estimated to be Ea*=30 kJ/mol. The sensitivity test revealed that the proposed sensor has a high sensitivity of 74 nm/RIU to the surrounding medium. The sensing of bovine growth hormone also known as bovine somatotropin (bST) shows that the proposed biosensor can reach a detection limit of as low as 3.7 ng/ml (185 pM). The results demonstrate the successful integration of microfluidics and nanoparticles which provides a potential alternative for protein detection in clinical diagnostics.