Efficient Way for Chemicals Identification Using Hexagonal Fiber with the Terahertz (THz) Band

To detect chemicals, we proposed a photonic crystal fiber (PCF) with hexagonal cladding and a hexahedron core (THz). Circular air holes (CAHs) in the vestibule provide the basis of the suggested sensor. To develop and evaluate our suggested hexahedron PCF sensor, we employed the finite element (FEM) technique and perfectly matched layers (PML), which utilized the optical parameters numerically. Here, 92.65%, 95.25%, and 90.70% are relatively sensitive, and confining losses are low. the value 5.40×10− 08, 6.70×10− 08 dB/m, and 5.75×10− 08 dB/m for three chemicals such as Ethanol (n = 1.354), Benzene (n = 1.366) and Water (n = 1.330) and effective material loss (EML) of 0.00694 cm− 1. The suggested Hx-PCF sensor has been successfully tested at 1 THz. We are certain that the suggested sensor's optimal geometric structure can be manufactured and that it can contribute to real-world applications in biomedicine and industry. In terahertz areas, our suggested PCF fiber is also suited for a wide range of medical signals and applications (THz).

Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing

There are various modern systems for the measurement and consequent acquisition of valuable patient’s records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...]

A Hybrid Watermarking Technique for Copyright Protection of Medical Signals in Teleradiology

Today, an individual's health is being monitored for diagnosis and treatment of diseases upon analyzing various medical data such as images and signals. Modifications of this medical data when it is transferred over an open communication channel or network leads to deviations in diagnosis and creates a serious health issue for any individual. Digital watermarking techniques are one of the solutions for providing protection to multimedia contents. This chapter gives requirements and various techniques for the security of medical data using watermarking. This chapter also demonstrates a novel hybrid watermarking technique based on fast discrete curvelet transform (FDCuT), redundant discrete wavelet transform (RDWT), and discrete cosine transform (DCT). This watermarking technique can be used for securing medical various types of medical images and ECG signals over an open communication channel.

Platform for Analysis and Labeling of Medical Time Series

Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.

Anomaly Detection from Medical Signals and Images Using Advanced Convolutional Neural Network

In the field of Artificial Intelligence (AI), deep learning is a method falls in the wider family of machine learning algorithms that works on the principle of learning. Convolutional Neural Networks (CNNs) can be used for pattern recognition from different images based on deep learning. Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. Anomaly detection from medical EEG signals based on spectrogram and medical corneal images are tested and evaluated in this paper. Technically, deep learning CNN models are used in the train and test processes, each input image will pass through a series of convolution layers with filters (Kernels), pooling, and fully connected layers (FC) for the classification purposes. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.

Detection and Classification of Alcoholics Using Electroencephalogram Signal and Support Vector Machine

Alcoholism may be recognized with the use of (EEG) analyzing signals. None-the-less, the analysis of the multi-channel signals of EEG is a complicated issue that usually needs performing complex computation operations and takes quite a long time to execute. The presented research will propose 13 optimal channel to feature extraction. In this research, an innovative horizontal visibility graph entropy (HVGE) method has been proposed for evaluating signals of EEG from controlled drinkers and alcoholic subjects and comparing against an approach of sample entropy (SaE). Values of HVGE and SaE have been obtained from 1200 records of bio-medical signals. While in classification step using SVM as classifier.