This paper presents a survey of the state-of-the-art advances in human vital signs detection using radar sensors, their integration and coexistence with communication systems, and their issues in spectrum sharing. The focus of this survey is to review the detection, monitoring, and tracking of vital signs, specifically the respiration rate and heartbeat rate over the recent five years. It is observed that in line with technological advancements, a multitude of radar types operating in diverse frequency spectra have been introduced with different hardware implementations, considering various detection scenarios, and applying multiple signal processing algorithms. The aim of these researches varies, from enhancing the detection accuracy, improving the processing speed, reducing the power consumption, simplifying the hardware used, lowering implementation costs, and the combinations of them. Besides that, this review also focuses on literature aimed at increasing the detection accuracy and reducing the processing time using FPGAs, prior to benchmarking them against other processing platforms. Finally, a perspective on the future of human vital signs detection using radar sensors concludes this review.

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A Survey on Vital Signs Detection Using Radar Techniques and Processing With FPGA Implementation

New Model For Wilson and Morris–Lecar Neuron Models: Validation and Digital Implementation on FPGA

Nowadays depression is a major health issue with critical psychological wellness, that influencing the world lives. It impacts emotionally as well as physical and physiological individuals Condition. It can also lead to suicide. Ongoing studies say 43% Indians experience depression. Depression can detect using HRV ECG signal, before getting into Severe depression because, HRV has been associated with depression. The QRS detection algorithm is utilized for calculating heart rate. In this project, comparative analysis of QRS detection algorithms is executed in MATLAB and performance compared for analyzing HRV of various MIT-BIH ECG information base acquired from physio.net The QRS detection algorithm technique has been decided for R peak detection and heart rate calculation, proposes a simple method and it doesn't include complex numerical model. The detection design plans in Verilog to figure heart pulse. Thus, the designed code for analyzing QRS detection algorithms and HRV ECG signal simulated in MATLAB as well as on XILINXS ISE 14.7.

Depression Detection Using Comparative Analysis of QRS Detection Algorithms and HRV Of ECG Signal Implemented on MATLAB and Verilog

After uploading the user ID data and ECG data to the cloud platform data center by the use of Intelligent Cloud health detector, this paper describes how to generate the image of user or ECG, in order to meet the display and use of the onstage website and mobile phone APP.

Design and Implementation of ECG Generation Software Based on Primary Medical Care

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

Recently, many studies have attempted to develop portable cardiac monitoring systems that can be used outside of a hospital setting. To achieve this objective, in this paper, a field-programmable gate array (FPGA) design and the implementation of an embedded electrocardiography (ECG) system using System-on-Chip (ECG-SoC) technology are proposed. The proposed system performs ECG pre-processing and heart rate variability (HRV) feature extraction that are suitable for remote homecare monitoring and rural health care applications. The development of ECG-SoC consists of four phases: the development of the hardware system, the Nios II-Linux embedded operating system (OS), the ECG-SoC software, and followed by system integration. The results of this study show that the developed ECG-SoC system is capable of compiling a raw ECG dataset, detecting QRS, computing R-R intervals, and displaying the FFT output and power spectrum analysis. The proposed system has the potential to be used in the future as a portable stand alone medical device for heart disease detection and monitoring.

Implementation of heart rate variability analysis algorithm on FPGA platform

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Implementation of heart rate variability analysis algorithm on FPGA platform