Removal of Power Line Interference from ECG Signal
Design of RISCV Processor using Verilog
Designing and Analysis of Electrocardiogram Simulator Tool Kit
A Novel Communication System Based on Sign Language Recognition and Voice Conversion for Differently Abled Person
Cerebral Infraction Prediction System using ECG and PPG Bio-Signal
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Fetal ECG Extraction from Maternal ECG using MATLAB
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
A Novel Communication System Based on Sign Language Recognition and Voice Conversion for Differently Abled Person
Electrocardiogram (ECG) signals are vital for diagnosing cardiac abnormalities, but they are corrupted by powerline interference noise, which can obscure critical features and compromise diagnostic accuracy. This paper investigates various filtration techniques aimed at effectively removing powerline interference noise from ECG signals. Different approaches, including average filters and moving average filters, are explored and compared to determine their efficacy in noise reduction while preserving important signal characteristics. Experimental evaluations are conducted using synthetic ECG signals contaminated with simulated powerline interference noise and real-world ECG recordings corrupted by actual powerline interference. The performance of each filtration technique is assessed based on metrics such as Root Mean Square Error (RMSE) and Percentage Root Mean Square Difference (PRD). The trade-offs between noise reduction effectiveness and preservation of ECG signal fidelity are analyzed to identify the most suitable approach for clinical applications. This paper provides valuable insights into the selection and implementation of filtration techniques for mitigating powerline interference noise in ECG signals. The findings contribute to the development of robust signal processing methods for improving the reliability and accuracy of ECG-based diagnostic systems.
The main goal of this paper is to develop a 32-bit pipelined processor with several clock domains based on the RISCV (open source RV32I Version 2.0) ISA. To minimize the complexity of the instruction set and speed up the execution time per instruction, a RISC (Reduced Instruction Set Computer) processor that uses less hardware than a CISC (Complex Instruction Set Computer) is used. Furthermore, this paper constructed this processor with five levels of pipelining with the aid of necessary block diagrams, and all of the processes are well described. In this paper, a RISCV processor is designed and simulated using Verilog. The design of the RISCV processor provides an alternative for software and hardware design to the computer designers as it provides free and open instruction set architecture (ISA). Besides, the designed RISCV processor will be using 5-stage pipeline techniques to improve the overall performance of the processor. This system is started by implementing several main modules, such as alu, aludec, maindec, imem, dmem, regfile, pc_mux, result_mux, pipeline register (IF/ID, ID/IEx, IEx/IMem, and IMem/IW), forwardMuxA, and forwardMuxB. Besides, a hazard unit is implemented into the design to mitigate hazard conditions. The functionality of these modules was simulated and verified by using Xilinx Vivado software.
In the realm of medical education, research, and device testing, Electrocardiogram (ECG) simulators are indispensable tools. They provide authentic representations of cardiac electrical activity, aiding healthcare professionals in practical training and facilitating the assessment of ECG device efficacy. This paper presents an efficient ECG simulator capable of replicating synthetic ECG waves, which are combinations of individual waves namely, the P wave, QRS complex, and T wave. The simulator can generate both normal and abnormal ECG waves and offers the flexibility to produce ECG waves at different frequencies.
Sign language is a form of communication that helps deaf and mute people interact with those who can hear. In the country, around 2.78% of people are unable to speak, i.e., are mute or deaf. Their primary means of communication is through hand motions and gestures. The paper proposes a new technique called the "artificial speaking mouth" for individuals with speech impairments. Humans connect with each other through thoughts and ideas, but some people lack the ability to speak. For them, sign language is the only means of communication. Nowadays, technology has reduced this communication gap with systems that translate sign language into speech. Sign Language Recognition (SLR) and gesture-based control are two major applications used for hand gesture recognition technologies. On the other hand, the controller converts sign language into text and speech, using text-to-speech conversion and analog-to-digital conversion.
Since stroke causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration. It is essential to detect in real time the precursor symptoms of stroke, which occur differently for each individual, and to provide professional treatment by a medical institution within the proper treatment window. However, studies have focused on developing acute treatment or clinical treatment guidelines after the onset of stroke rather than detecting the prognostic symptoms of stroke. In particular, studies have mostly used image analysis such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) to detect and predict prognostic symptoms in stroke patients. Not only are these methodologies difficult to apply early in real time, but they also have limitations in terms of long testing times and high costs. This paper proposes a system that can predict and semantically interpret stroke prognostic symptoms based on machine learning using multimodal biosignals from Electrocardiogram (ECG) and Photoplethysmogram (PPG). As a result, the real-time prediction of stroke prognosis in elderly patients showed simultaneously high prediction accuracy and performance. Additionally, the CNN-LSTM model using raw data of ECG and PPG demonstrated a satisfactory prediction accuracy of 99.15%.
