A FPGA system for QRS complex detection based on Integer Wavelet Transform

Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system

ELECTROCARDIOGRAM (ECG) SIGNAL PROCESSING ON FPGA FOR EMERGING HEALTHCARE APPLICATIONS

In this project an ECG signal processing module will be implemented in VHDL on FPGA platform. The digital filtering will be carried out with low pass FIR architecture. Filters shall filter the 50 Hz coupled noise and other high frequency noises. The filtered signal is subjected to Short Time Fourier transform by which lot of inferences can be made by medical experts. A recorded ECG signal will be used as test input to test the modules implemented on FPGA. The Modelsim Xilinx Edition and Xilinx Integrated Software Environment will be used simulation and synthesis respectively. The Xilinx Chipscope tool will be used to test the results, while the logic running on FPGA. The Xilinx Spartan 3 Family FPGA development board will be used this project

Antepartum Fetal Monitoring through a Wearable System and a Mobile Application

Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Keywords: tele-monitoring; wearable devices; fetal heart rate; telemedicin

Real-Time Electrocardiogram (ECG) Signal Analysis and Heart Rate Determination in FPGA Platform

Heart disease is one of the leading cause for death of people globally. According to a recent study by the Indian Council of Medical Research (ICMR), about 25 percent of deaths in the age group of 25- 69 years occur because of heart diseases.Electrocardiogram (ECG) is one of the primary tool for the treatment of heart disease. ECG is an important biological signal that reects the electrical activities of the heart. A typical ECG signal consists of mainly ve components namely P,Q, R, S and T wave. Amplitude and morphology of each component contains numerous medical information. The automated detection and delineation of each component in ECG signal is a challenging task in Bio-medical signal processing community. In this research work, a four stage method based on Shannon energy envelope has been proposed in order to detect QRS complex in ECG signal. Peak detection of the proposed algorithm is amplitude threshold free. To evaluate the performance eciency of the proposed method standard MIT-BIH arrhythmia ECG database has been used and get an average accuracy of 99.84 %, Sensitivity 99.95% and Positive Predictivity value 99.88 %. To detect and delineate P and T waves, an algorithm based on Extended Kalman Filter (EKF) with PSO has been proposed. For performance examination, standard QT ECG database has been used. The proposed algorithm yields an average Sensitivity of 99.61 % and Positive Predictivity of 99.00 % for the ECG signal of QT database. A long term automatic heart rate monitoring system is very much essential for standard supervision of a critical stage patient. This work also includes a feld programmable gate array (FPGA) implementation of a system that calculate the heart rate from Electrocardiogram (ECG) signal

Energy Consumption Saving in Embedded Microprocessors Using Hardware Accelerators

This paper deals with the reduction of power consumption in embedded microprocessors. Computing power and energy efficiency are becoming the main challenges for embedded system applications. This is, in particular, the caseof wearable systems. When the power supply is provided by batteries, an important requirement for these systems is the long service life. This work investigates a method for the reduction of microprocessor energy consumption, based on the use of hardware accelerators. Their use allows to reduce the execution time and to decrease the clock frequency, so reducing the power consumption. In order to provide experimental results, authors analyze a case of study in the field of wearable devices for the processing of ECG signals. The experimental results show that the use of hardware accelerator significantly reduces the power consumption

Measuring dynamic signals with direct sensor-to-microcontroller interfaces applied to a magnetoresistive sensor

This paper evaluates the performance of direct interface circuits (DIC), where the sensor is directly connected to a microcontroller, when a resistive sensor subjected to dynamic changes is measured. The theoretical analysis provides guidelines for the selection of the components taking into account both the desired resolution and the bandwidth of the input signal. Such an analysis reveals that there is a trade-off between the sampling frequency and the resolution of the measurement, and this depends on the selected value of the capacitor that forms the RC circuit together with the sensor resistance. This performance is then experimentally proved with a DIC measuring a magnetoresistive sensor exposed to a magnetic field of different frequencies, amplitudes, and waveforms. A sinusoidal magnetic field up to 1 kHz can be monitored with a resolution of eight bits and a sampling frequency of around 10 kSa/s. If a higher resolution is desired, the sampling frequency has to be lower, thus limiting the bandwidth of the dynamic signal under measurement. The DIC is also applied to measure an electrocardiogram-type signal and its QRS complex is well identified, which enables the estimation, for instance, of the heart rate.Postprint (published version

Magnetocardiography with a modular spin-exchange relaxation free atomic magnetometer array

We present a portable four-channel atomic magnetometer array operating in the spin exchange relaxation-free regime. The magnetometer array has several design features intended to maximize its suitability for biomagnetic measurement, specifically foetal magnetocardiography, such as a compact modular design, and fibre coupled lasers. The modular design allows the independent positioning and orientation of each magnetometer, in principle allowing for non-planar array geometries. Using this array in a magnetically shielded room, we acquire adult magnetocadiograms. These measurements were taken with a 6-11 fT Hz^(-1/2) single-channel baseline sensitivity that is consistent with the independently measured noise level of the magnetically shielded room.Comment: 15 pages, 5 figure