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Medical Instrumentation Application And Design

Behavior of the PR interval with increasing heart rate in patients with COVID-19.

Respiration is a crucial vital sign to indicate acidotic state of human body. In this paper, we have developed a Holo-Hilbert spectral analysis (HHSA)-based approach to detect subject’s respiration frequency from wrist photoplethysmogram (PPG) signals. The HHSA is a two-layer EMD architecture which first decomposes the original signal into intrinsic mode functions and then tries to find additive and multiplicative interactions among the participating components. The HHSA enables frequency-modulated (FM) and amplitude-modulated (AM) features of a signal can be comprehensively presented on a Holo-Hilbert spectrum (HHS). With the help of HHSA, subject’s respiration frequency can be identified on HHS by finding the AM frequency with the most prominent amplitude around the FM frequency of heart rate. The efficacy of the proposed method has been demonstrated in two designed experiments. In the first experiment, 75 subjects with ages ranged from 20–80 years old, and the difference between the detected results of proposed method and the readouts of transthoracic impedance plethysmography is 0.04 ± 0.96 breathes-per-minute (brm). In the second experiment, six subjects were requested to breathe at 6, 12, 18, 24 brm, following the pacing rhythms of a metronome. The difference between detected respiration rates and expected breathing rates is 0.01 ± 0.70 brm. The HHSA-based approach has manifested its capability to extract respiration-induced multiplicative component in PPG signal. It avoids the ambiguity of frequency representation in signal multiplication when using traditional additive decomposition methods.

A Method for Respiration Rate Detection in Wrist PPG Signal Using Holo-Hilbert Spectrum

Free radicals are generated as byproducts of normal metabolic processes as well as due to exposure to several environmental pollutants. They are highly reactive species, causing cellular damage and are associated with a plethora of oxidative stress-related diseases and disorders. Antioxidants can control autoxidation by interfering with free radical propagation or inhibiting free radical formation, reducing oxidative stress, improving immune function, and increasing health longevity. Antioxidant functionalized metal nanoparticles, transition metal oxides, and nanocomposites have been identified as potent nanoantioxidants. They can be formulated in monometallic, bimetallic, and multi-metallic combinations via chemical and green synthesis techniques. The intrinsic antioxidant properties of nanomaterials are dependent on their tunable configuration, physico-chemical properties, crystallinity, surface charge, particle size, surface-to-volume ratio, and surface coating. Nanoantioxidants have several advantages over conventional antioxidants, involving increased bioavailability, controlled release, and targeted delivery to the site of action. This review emphasizes the most pioneering types of nanoantioxidants such as nanoceria, silica nanoparticles, polydopamine nanoparticles, and nanocomposite-, polysaccharide-, and protein-based nanoantioxidants. This review overviews the antioxidant potential of biologically synthesized nanomaterials, which have emerged as significant alternatives due to their biocompatibility and high stability. The promising nanoencapsulation nanosystems such as solid lipid nanoparticles, nanostructured lipid carriers, and liposome nanoparticles are highlighted. The advantages, limitations, and future insights of nanoantioxidant applications are discussed.

Nanoantioxidants: Pioneer Types, Advantages, Limitations, and Future Insights.

In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart’s rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.

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ECG Data Analysis with Denoising Approach and Customized CNNs

In many situations, the Electrocardiogram (ECG) is recorded during ambulatory or strenuous conditions such that the signal is corrupted by different types of noise, sometimes originating from another physiological process of the body. Hence, noise removal is an important aspect of signal processing. Here five different filters i.e. median, Low Pass Butter worth, FIR, Weighted Moving Average and Stationary Wavelet Transform (SWT) with their filtering effect on noisy ECG are presented. Comparative analyses among these filtering techniques are described and statically results are evaluated.

Multidimensional approaches for noise cancellation of ECG signal

Surface Plasmon Resonance (SPR) technique has been of recent interest in many fields such as biological and chemical sensing, defense-related applications, etc. The introduction of graphene sheet (metal-like optical properties) over a thin-film layers plays a significant role in generating SPR. Different nanoplasmonic structures like Coupled Waveguide SPR (CWSPR) and Long Range SPR (LRSPR) are considered with modified Kretschmann-Raether configurations for different wavelengths in visible range for plasmonic sensors application. Characteristic Transfer Matrix (CTM) method in MATLAB environment is employed to obtain the theoretical results of LRSPR-based nanoplasmonic structures. Based on different compositions, minimum reflectivity as obtained is reported here. Due to the incorporation of additional graphene thin layer, keeping the angle of incidence constant, the percentage of reflectivity is being increased significantly, thereby reducing the transmission within the layers. With the variation of thickness, refractive index of different layers as well as wavelength of the LASER source, one may optimize the structures to obtain best possible performance.

Influence of Design Parameters on Multilayered Nanoplasmonic Structures in Modified Kretschmann-Raether Configurations

Chalcogenide Glasses are of recent interest in the field of chemical and bio-sensing applications. These are considered either in stored memory devices or in optical domain. The involvement of such material in the bio-sensing application for the development of SPR (Surface Plasmon Resonance) Sensors is very essential and thus, has been considered in this manuscript. In a Kretschmann SPR configuration, a thin-film layer of such Chalcogenide material has been incorporated which is further accompanied with a Graphene layer. The optical properties of both the materials viz. chalcogenide and graphene have been portrayed and presented in this paper by taking the assistance of MATLAB environment and Characteristic Transfer Matrix (CTM) Method.

Bio-sensing application of chalcogenide thin film in a graphene-based surface plasmon resonance (SPR) sensor

Photoplethysmography (PPG) and Phonocardiography (PCG) are two important non-invasive techniques for monitoring physiological parameters of cardiovascular diagnostics. The PCG signal discloses information about cardiac function through vibrations caused by the working heart. PPG measures relative blood volume changes in the blood vessels close to the skin. This paper emphasizes on simultaneous acquisition of PCG and PPG signals from the same subject with the aid of NIELVIS II+ DAQ and the signals are imported to MATLAB for further processing. Heart rate is extracted from both the signals which are found to be distinctive. This analytical approach of processing these signals can abet for analysis of Heart rate variability (HRV) which is widely used for quantifying neural cardiac control and low variability is particularly predictive of death in patients after myocardial infarction.

Real time acquisition and analysis of PCG and PPG signals

extracted from both the signals which are found to be distinctive. This anal ytical a pproach of processin g these si gnals can abet for anal ysis of Heart rate variabilit y (HRV) which is widel y used for quantif ying neural cardiac control and low variabilit y is particularl y p redictive of death in patients after myocardial infarction.

Jitendra Singh Tamang

Papers

Multidimensional approaches for noise cancellation of ECG signal

Influence of Design Parameters on Multilayered Nanoplasmonic Structures in Modified Kretschmann-Raether Configurations

Bio-sensing application of chalcogenide thin film in a graphene-based surface plasmon resonance (SPR) sensor

Real time acquisition and analysis of PCG and PPG signals

Real Time Acquisition and Analysis of peG and PPG Signals