Electrocardiosignal Research Articles

Application and research of two-stage amplifier in ECG signal

With the development of modern science and technology, electrocardiograph (ECG) technology has gradually become one of the most important detection tools in modern medicine. This article introduces the characteristics of ECG including weak signal, strong noise, low frequency range and strong signal randomness, and it mainly includes P waves, QRS wave group and T wave. Also, the processing flow of ECG is introduced, and eliminate the effects of baseline drift, power-line interference, and electromyographic interference by denoising. Also introduces the characteristics of two-stage amplifier and the importance of frequency compensation. Then the electro cardio signal is simplified to classical differential signal, and a two-stage amplifier circuit is designed to amplify it. The simulation result is 80.31dB DC voltage gain, 3kHz -3dB bandwidth and 72 degrees phase margin. So, it can provide sufficient stability and allow the system to remain stable in the face of parameter changes, external disturbances, or any uncertainties. This study can better use of two-stage amplifier to obtain complete and accurate ECG.

Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing

Coronary vascular disease (CHD) is one of the most fatal diseases worldwide. Cardio vascular diseases are not easily diagnosed in early disease stages. Early diagnosis is important for effective treatment, however, medical diagnoses are based on physician's personal experiences of the disease which increase time and testing cost to reach diagnosis. Physicians assess patients' condition based on electrocardiography, sonography and blood test results. In this research we develop classification model of the functional state of the cardiovascular system based on the monitoring of the evolution of the amplitudes of the first and second harmonics of the system rhythm of 0.1 Hz. We separate the signal to three streams; the first stream works with natural electro cardio signal, the other two streams are obtained as a result of frequency analysis of the amplitude- and frequency-detected electro cardio signal. We use sliding window of a demodulated electro cardio signal by means of amplitude and frequency detectors. The developed NN model showed an increase in accuracy of diagnostic efficiency by 11%. The neural network model can be trained to give accurate early detection of disease class.

A signal quality analysis method for electrocardiosignals in the CS domain

Compressed sensing (CS) is an important cutting-edge low-power technology for wearable health monitoring systems. To solve the problem of how the quality of compressive sampled electrocardiosignal (ECG) affects the subsequent arrhythmia recognition and signal reconstruction, a novel ECG quality evaluation method in the CS domain is proposed. In order to achieve the evaluation effect in the CS domain, a modified bandpass filter method is designed to obtain the approximate frequency band energy and approximate wavelet entropy, which are used as the evaluation index for the support vector machine classifier to realize the CS ECG signal quality evaluation. Based on the simulation results from the MIT-BIH ECG database, this method can reach an accuracy of more than 92% under different compression ratios (2, 4, 8 and 16). Simulation experiments based on data from the PhysioNet/CinC Challenge 2011 database show that the accuracy of this method can reach above 83% under different compression ratios. In evaluation experiments using actual ECG signals, we show that this method can achieve good accuracy when the compression ratio is less than eight. The accuracy of this method needs to be further improved when the compression ratio is 16. When the compression ratio is less than 8 and a sparse matrix is not used as the measurement matrix, the method proposed in this paper can directly evaluate the quality of ECG signals after compression sampling without reconstruction. Compared with the signal quality evaluation algorithm in the uncompressed domain, the experimental results of the proposed method are highly competitive and, under a certain compression ratio, the amount of transmitted data can be reduced while maintaning accuracy. Meanwhile, the proposed method also provides a new idea for signal quality evaluation in the compressed domain.

Detection of inferior myocardial infarction based on morphological characteristics

Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.

WAVELET-CONVERSION IN ELECTROCARDIO SIGNAL PROCESSING

In the present work, processing of an electro cardio signal using a wavelet transform is consi-dered. In electrocardiography, various digital signal-processing techniques are used to detect, extract, and analyze the various components of an electrocardiogram. Among them, the wavelet transform technique gives promising results in the analysis of the time-frequency characteristics of the electrocardiogram components. The urgency of solving the problem of improving the quality of life of people with the help of early diagnosis and timely treatment of various cardiac diseases is obvious. The process of automated analysis of a huge database of electrocardiographic data is especially important. Wavelet analysis can be successfully used to smooth and remove noise in the ECG signal. Electrocardiogram signal, cleaned from noise components, looks clearer, while its volume is from 10 to 5% of the original signal, which largely solves the problem of storing cardiac records. Aim. Development of an algorithm for threshold processing of wavelet coefficients and filtering of an electrocardiography signal. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the MATLAB environment that implements continuous and discrete wavelet transform. Results. The work shows the result of filtering the ECG signal with the addition of noise with a signal-to-noise ratio of 35 and 45 dB using the decomposition levels N = 2, N = 3, N = 4. Conclusion. Based on the analysis of the data obtained, it can be concluded that the second level of decomposition is the most optimal for filtering the ECG signal. With an increase in the level of decomposition, the output ratio decreases, at the level N = 4 the output signal-to-noise almost does not exceed the input one, therefore, the filtering becomes ineffective. The correlation coefficient to the fourth level is significantly reduced, which means a significant increase in the distortion introduced by the filtering algorithm.

Wireless Holter monitoring system with a dual-core processor

The article discusses the principles of building a wireless automated system for heart activity Holter monitoring using a WiFi module based on a dual-core processor. The system is focused on use in a medical cardiology center or at home. The article proposes the method of wireless transmission of an electro-cardio signal based on the paralleling of processing operations and Wi-Fi transmission of an electro-cardio signal. The paper presents algorithms for wavelet compression and filtering of the transmitted electro-cardio signal that ensure high reliability of the transmitted ECG information. A semi-realistic model of the Holter monitoring system has been developed, including real-time packages of the Matlab system, a PhysioBank ATM electro-cardio signal database, a hardware interface based on the PCI-1710HG Board and an external wireless ECG information transmission subsystem. The proposed methods and algorithms will provide high-quality and multi-day wireless transmission of ECG signals, characterized by low power consumption

МЕТОДИ СТАТИСТИЧНОГО ОПРАЦЮВАННЯ РИТМОКАРДІОСИГНАЛУ ІЗ ПІДВИЩЕНОЮ РОЗДІЛЬНОЮ ЗДАТНІСТЮ НА ОСНОВІ ЙОГО МОДЕЛІ У ВИГЛЯДІ ВЕКТОРА СТАЦІОНАРНИХ ВИПАДКОВИХ ПОСЛІДОВНОСТЕЙ

Робота присвячена методам статистичного оцінювання ймовірнісних характеристик ритмокардіосигналу із підвищеною роздільною здатністю на основі його моделі у вигляді вектора стаціонарних та стаціонарно пов’язаних випадкових процесів. Було підтверджено гіпотезу про нормальність закону розподілу компонент ритмокардіосигналу із підвищеною роздільною здатністю. В реальних комп’ютерних системах аналізу серцевого ритму завжди реєструється скінчена кількість циклів електрокардіосигналу, тому цей факт необхідно врахувати і при статистичному оцінюванні ймовірнісних характеристик ритмокардіосигналу із підвищеною роздільною здатністю. А саме, статистичне оцінювання імовірнісних характеристик ритмокардіосигналу із підвищеною роздільною здатністю полягає в отриманні реалізацій статистичних оцінок, які можуть бути прийняті за наближення до відповідних ймовірносних характеристик досліджуваного ритмокардіосигналу. Для врахування часової динаміки ритмокардіосигналу із підвищеною роздільною здатністю необхідно використовувати математичний апарат теорії випадкових процесів, а саме, розглядати його як вектор дискретних випадкових процесів. Обґрунтовано вектор діагносичних ознак в системах аналізу серцевого ритму за ритмокардіосигналами із підвищеною роздільною здатністю

Design FIR Band Pass Filter With Centered At 50 Hz For Medical Application

Digital signal processing techniques used widely to cancel undesired parts of the signals, like that interference with other frequency range. Electromagnetic signals can be interference with human body signals such as Electrocardio signal Electroencephalogram and Electromyography. These interferences can be affect on the diagnosis signal during the examination and when these happen with different frequencies,such as (EMG) noise, devices that have high vibration which give fault data or effect the final result. Filtering of signals interference is very useful in the diagnosis of biomedical cases, as instant as the ECG signal that represent weak signal. This work treat problems of all kinds of interference with ECG signal and reduce it based on the development of digital Finite Impulse Response (FIR)filter for ECG signal which has the frequency between (5Hz to 100 Hz)using fdtool by MATLAB instead of classical filters that often unaccurate. It is hard to get pure signal from human body without any interferences specially with that range of frequencies. In this paper the ECG signal has been presented from human body with its frequency spectrums before filtering then filtered from unwanted frequencies and show the elimination of the disturbance interference from this signal

Isoline Drift Correction in Digital Processing of the Electrocardiosignal

The article discusses reduction of electrocardiosignal (ECS) drift based on statistical signal processing, comparing the results of applying statistical methods to a limited time frame for correction of isoline drift based on the additive mixture of drift and ECS components. A mathematical regression formula is provided to approximate a ranged sample of values of one ECS cycle. The study presents an effective method of eliminating monotonous isoline drift, allowing the use of programs for fast data sorting.

Medical Monitoring Model of Internet of Things Based on the Adaptive Threshold Difference Algorithm

There are still problems such as low detection accuracy and poor noise immunity in the application of the standard threshold difference algorithm in the signal detection of electrocardiosignal (ECG), in this paper, a medical monitoring model based on the adaptive threshold difference is proposed. First we use a nonlinear filter to filter the P wave and T wave which are low frequency in ECG signal. Then complex wave QRS will be tested. Then the algorithm will be more accuracy through the detection of the R-R interval length and the adjustment of threshold. Finally, the ECG signal will be test with quadratic spline wavelet twice, and the error judgment will be known through adaptive threshold difference. In the simulation experiments, after judging error by wavelet transformation and making the standard threshold difference algorithm optimize adaptively, algorithm showed excellent detection accuracy with and without noise.

Research and Analysis of ECG Signal Feature Detection Based on Multi-Resolution Analysis and Baselines Wander

Electrocardiosignal feature extraction is the base of electrocard iologic automatic diagnosis. By using wavelet transform multi-resolution analysis, the noise in electrocard iosignal is removed; and by using proximity signals of wavelet transform the base linew ander is filtered. The high frequency noise is handled and eliminated with the default threshold; and the average value of the electrocardiosignals is set to zero. In detection of rpeak, because leak detection will occur when only 23 detail signals is considered, thus the 23 and 24 detail signals are integrated to avoid miss detection effectively. The methods avoiding error detection bring excellent effects. For calculating average cardiac electric axis, among the methods of area method, time voltage method and amplitude method, the area method offers the highest accuracy.

Development of a four-channel cardiovascular signals collector based on STM32 with digital-controlled gain

To get better methods on diagnosis of cardiovascular diseases and health assessment by comparison analysis of four kinds of cardiovascular signals, which are blood pressure signal (BPS), plus wave signal (PWS), electrocardiosignal (ECS) and cardiac sound signal (CSS), a four-channel signals collector is introduced in this article. This collector is composed of microcomputer STM32F103RB with integrated 12-bit analog to digital converters (ADCs) and some integrated chips (IC) such as PGA113, a programmable gain amplifier with internal calibration channels for system-level calibration. Therefore, the data of four kinds of cardiovascular signals can be both collected by this signal collector with a high precision and output to a computer by universal serial bus (USB) interface, which can also offers power for this instrument. In addition, the gain of input signal can be changed by computer software through USB interface under microchip commands. Thus it has many merits, such as the small volume, low cost, high performance and precision, digital-controlled gain and so on. Consequently, it is convenient to acquire the data of four kinds of cardiovascular signals by it, and a waveform of one sample is shown.

Analysis of Electrocardio Signal Based on Approximate Entropy

The research and detection of heart disease depends on the analysis of the characteristic of electrocardio signal. Current analysis methods mainly include: (1) time domain analysis is a common used approach. With experience learned by observation and calculation, researchers examine errors and interferences to calculate means and variances directly within time domain. Analysis quality of this method demands higher request for researchers’ experience and skill although it’s a direct and significant result. (2) Frequency domain analysis, such as spectrum estimation, is largely applied to electrocardio signal researches and clinical applications. The analysis reflects abundant electrocardio activities, but failed to show details of the characteristics due to lack of time information. (3) time-frequency domain analysis describes energy density under different time and frequency of electrocardio signal at one time. It clarifies the relationship of signal frequency’s changing along with time such as wavelet transform method. (4) Nonlinear analysis is generally applied to biomedicine signal research in recent years. Correlation dimension, kolmogorov entropy, lyapunov component are major research methods to estimate some nonlinear dynamic parameters to represent the characteristic of electrocardio signal.

Relationship of the maximum QRS vector with the left ventricular myocardial mass during the development of hypertrophy: Computer simulation and empirical data

A model of electrical activity of the heart has been used to demonstrate that, all other conditions remaining the same, the spatial vector of the heart changes, as a first approximation, proportionally to the increase in the surface area of the heart ventricles (rather than the myocardial mass) during proportional homothetic changes in the sizes of the heart and His-Purkinje system. In contrast, the electrocardiosignal (ECS) amplitude is determined, at any given moment, by the area of depolarized regions of the epicardium and endocardium, which agrees with the model of a double electrical layer on the surface of an electrically active myocardium.