The Utility of Simulink Subsystems in Handling and Processing of Biomedical Signals and Images

To model, simulate, and analyze multi-domain dynamical systems, Simulink, which is a Matlab-based graphical programming environment, can be used effectively. Due to the drag-drop facility, accessible graphic user interface components, and zero coding environments, Simulink becomes the most used tool both in industry and academia. The design cycle time of any real-time systems can be reduced using Simulink than other software tools. This article focuses mainly on the utility behind the subsystems such as enabled subsystem, triggered subsystem, triggered and enabled subsystem, and control flow subsystem in biomedical signal and image processing. Image segmentation using enabled subsystem, voiced/unvoiced classification using triggered subsystem, and the computation of root-mean-square (RMS) amplitude using If Action subsystem are implemented using breast cancer image and human voice signal. The Matlab 9.4 tool is used for experimental simulation with the biomedical signals and images.

Advanced Digital Signal Processing Techniques on the Classification of the Heart Sound Signals

Speech processing subject primarily depends on the digital signal processing (DSP) methods, such as convolution, discrete Fourier transform (DFT), fast Fourier transforms (FFT), finite impulse response (FIR) and infinite impulse response (IIR) filters, FFT recursive and non-recursive digital filters, FFT processing, random signal theory, adaptive filters, upsampling and downsampling, etc. Recursive and non-recursive digital filters are primarily deployed to absorb the signal of interest signals and to block the unwanted signals (noise). Broadly, low-pass, high-pass, band-pass, and band-stop filters are implemented for filtering functions. In frequent, the DSP theories can be used for further biomedical engineering domains like biomedical imaging (MRI, ultrasound, CT, X-ray, PET) and genetic signal analysis-cum-processing too. In this article, the experiments such as voiced/unvoiced detection, formants estimation using FFT and spectrograms, pitch estimation and tracking and yes/no sound classification are used. Also, the analysis of normal/abnormal heart sound signals using simple energy computation and the zero-crossing rate and their results are obtained. For the entire study, the Matlab R2018a tool is used to obtain the simulation results. At last, the criticism, feedbacks, comments, reactions from the student are detailed for the exceptional development of the course.

Placental expression of leptin: fetal sex-independent relation with human placental growth

OBJECTIVES

Leptin (LEP) is a vital placental hormone that is known to affect different aspects of placental function and fetal development. The present study aimed to determine the association of placental LEP transcript abundance with maternal, placental, and newborn parameters.

SUBJECTS/METHODS

In this retrospective case-control study, placental samples (n = 105) were collected from small (SGA) and appropriate (AGA) for gestational age full-term singleton pregnancies (n = 44 SGA and n = 61 AGA). Placental transcript abundance of LEP was assessed by real-time quantitative PCR after normalization to a reference gene panel. LEP methylation was measured using a quantitative MethyLight assay in a subset of samples (n = 54).

RESULTS

Placental LEP transcript abundance was negatively and significantly associated with placental weight (β = -3.883, P = 0.015). This association continued to be significant in the SGA group (β = -10.332, P = 0.001), both in female (β = -15.423, P = 0.021) and male births (β = -10.029, P = 0.007). LEP transcript abundance was not associated with LEP methylation levels (Spearman's ρ = 0.148, P = 0.287).

CONCLUSION

We conclude that placental upregulation of LEP is an integral and fetal sex-independent component of placental growth restriction, which can be potentially targeted through maternal dietary modifications to improve fetoplacental growth.