The purpose for researchers in medical electronics field, in signal processing and information field is to provide powerful and low-cost systems to help doctors to make his specific spots. In this paper, a biomedical monitoring system is proposed based on LabVIEW FPGA. This monitoring system has the advantage to acquire and analyze several physiological signals. The Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyogram (EMG) signals are recorded from respectively the heart, the brain and the muscle. Then, each signal is amplified, filtered and displayed on the computer. The designed system can be used to determine and monitor some physiological parameters. This system is designed using a National Instrument data acquisition device. The signals processing, the display as well as the user interface are managed by the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) with the specialized module LabVIEW FPGA. The field programmable gate arrays (FPGA) configuration has not been made by Verilog Hardware Description Language (VHDL) but rather by a graphic programming with the LabVIEW FPGA. The programming by LabVIEW saves a lot of time and can be easily verified in the case of a bug. This system allows the determination of some physiological parameters and can be used as a powerful vital signs monitor.

Biomedical monitoring system using LabVIEW FPGA

Vital sign measurement devices are medical devices commonly used to measure the health condition of a patient. A common method to determine heart condition is by observing cardiac electrical signal to find out any abnormalities on heart. In this paper, we report the design of a non-invasive medical instrumentation that can display the recorded electrocardiogram signal and measure heart rate. The cardiac electrical activities were recorded with the usage of three-electrode Einthoven Standard Lead. The FPGA Spartan-3 was used for signal processing as it can be programmed according to desired design. As the result, the PQRST electrocardiogram signal was successfully acquired and displayed on monitor as well as the heartbeat rate. The heart beat rate has the average error less than 2% when compared to manual counting.

Development of FPGA-based three-lead electrocardiography

Technology is developed to benefit society. One of the applications of technology in the healthcare sector is telehealth monitoring system. The system proposes a new way of communication between the doctor and the patient, even in a very remote location. In this paper, we elaborate the progress and challenges regarding the development of Tele-ECG in Indonesia, which includes data acquisition, feature extraction, data compression, classification algorithm, mobile and web development system and small device implementation on an FPGA board. The classification is conducted by using LVQ, GLVQ, FNLVQ, FNLVQ-PSO, FNGLVQ, and AM-GLVQ. The compression is conducted by using SPIHT algorithm. Tele-ECG can assist in monitoring heartbeat anomalies and reduce the risk of heart attack. It could also be a solution for infrastructure discrepancy in healthcare.

/pdf/developing-smart-tele-ecg-system-for-early-detection-and-31ohw2pu1t.pdf

Developing smart Tele-ECG system for early detection and monitoring heart diseases based on ECG signal: progress and challenges

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

Cardiovascular complications are considered as one of the most common and fatal complications to the rescue personnel and require urgent medical intervention to save an emergency patient. Often due to the delay in detecting heart complication in urgent situation, necessary treatment paths cannot be followed which results in high mortality rate. Although the current researches show promising aspects in detecting cardiovascular diseases in early stage, they are focused on detecting only some of specific and common cardiovascular diseases from clinically recorded or long term historical patients’ data. The novel approach followed in this research is: instead of using traditional health data collected in clinical environment, we developed the model with 9 years of rescue mission’s real-time recorded data to recognise any cardiovascular situation in general. To find out the best model, different machine learning(ML) algorithms like Support Vector Machine(SVM), Random Forest(RF), K-nearest neighbour(KNN), Extreme Gradient Boosting(XGB), Logistic Regression(LR), Naive Bayes(NB) and Artificial Neural Network(ANN) were used. From the performance comparison, we concluded that extreme gradient boosting and neural network showed the best performance in terms of all evaluation parameters. Fast inference is the basic requirement for any rescue mission. So an inference time analysis of the ML models and Apache-TVM machine learning compiler was shown to understand their compatibility in real world applications.

A Novel Analysis of Performance and Inference Time of Machine Learning Models to Detect Cardiovascular Emergency Situations of Rescue Patients

This paper presents a FPGA design and implementation of Electrocardiogram (ECG) biomedical embedded system (ECG-SoC). It performs ECG pre-processing and heart rate variability (HRV) feature extraction which suitable for remote homecare monitoring and rural health care application. The ECG-SoC is designed using hardware/software co-design technique based on offline dataset from MIT-BIH database. Altera Cyclone II DE2-115 FPGA platform was used for system prototyping and functionality verification. The computation results are displayed on Nios II-Linux terminal and produce output files for post analysis executed on the host personal computer (PC).

FPGA design and implementation of Electrocardiogram biomedical embedded system

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FPGA design and implementation of Electrocardiogram biomedical embedded system