scholarly journals Advances in Modern Capacitive ECG Systems for Continuous Cardiovascular Monitoring

10.14311/1456 ◽  
2011 ◽  
Vol 51 (5) ◽  
Author(s):  
A. Schommartz ◽  
B. Eilebrecht ◽  
T. Wartzek ◽  
M. Walter ◽  
S. Leonhardt
Keyword(s):  
Hardware Design ◽  
Redundant Information ◽  
Cardiovascular Monitoring ◽  
Ecg Signals ◽  
Everyday Objects ◽  
Robust Measurements ◽  
The One ◽  
Electronic Hardware ◽  
Channel Systems

The technique of capacitive electrocardiography (cECG) is very promising in a flexible manner. Already integrated into several everyday objects, the single lead cECG system has shown that easy-to-use measurements of electrocardiograms are possible without difficult preparation of the patients. Multi-channel cECG systems enable the extraction of ECG signals even in the presence of coupled interferences, due to the additional redundant information. Thus, this paper presents challenges for electronic hardware design to build on developments in recent years, going from the one-lead cECG system to multi-channel systems in order to provide robust measurements - e.g. even while driving an automobile.

Airborne Electronic Hardware Design Assurance

2017 ◽  
Author(s):  
Randall Fulton ◽  
Roy Vandermolen
Keyword(s):  
Hardware Design ◽  
Electronic Hardware

scholarly journals Approximating Ideal Filters by Systems of Fractional Order

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Ming Li
Keyword(s):  
Fractional Calculus ◽  
Fractional Order ◽  
Point Of View ◽  
Present Approach ◽  
The Other ◽  
Necessary Condition ◽  
Ecg Signals ◽  
Other Hand ◽  
The One ◽  
Electrocardiogram Ecg

The contributions in this paper are in two folds. On the one hand, we propose a general approach for approximating ideal filters based on fractional calculus from the point of view of systems of fractional order. On the other hand, we suggest that the Paley and Wiener criterion might not be a necessary condition for designing physically realizable ideal filters. As an application of the present approach, we show a case in designing ideal filters for suppressing 50-Hz interference in electrocardiogram (ECG) signals.

A LOW-POWER PARAMETERIZED HARDWARE DESIGN FOR THE ONE-DIMENSIONAL DISCRETE FOURIER TRANSFORM OF VARIABLE LENGTHS

2002 ◽  
Vol 11 (04) ◽  
pp. 405-426 ◽  
Author(s):  
JIUN-IN GUO ◽  
CHIEN-CHANG LIN ◽  
CHIH-DA CHIEN
Keyword(s):  
Fourier Transform ◽  
Low Power ◽  
Discrete Fourier Transform ◽  
Hardware Design ◽  
Ip Core ◽  
Distributed Arithmetic ◽  
One Dimensional ◽  
System Requirements ◽  
Block Based ◽  
The One

This paper presents a new low-power parameterized hardware design for the one-dimensional (1D) discrete Fourier transform (DFT) of variable lengths. By combining the cyclic convolution formulation, block-based distributed arithmetic (BDA), and Cooley–Tukey decomposition algorithm together, we have developed a parameterized hardware design for the DFT of variable lengths ranging from 256 to 4096 points and with different modes of performance. The proposed design can perform different lengths of DFT computation through the configuration of parameters, which not only provides the flexibility in computing different length DFT but also facilitates the performance-driven design considerations in terms of power consumption and processing speeds, that is, we can configure the proposed design in different modes of performance by setting different parameters. This feature is beneficial to developing a parameterized DFT soft Intellectual Property (IP) core or hard IP core for meeting the system requirements of different silicon-on-a-chip (SOC) applications as compared with the existing fixed length DFT designs.

Robust Method for Separation of Noisy Biomedical Signals

2010 ◽  
Vol 26-28 ◽  
pp. 5-8
Author(s):  
Yong Jian Zhao ◽  
Bio Qiang Liu
Keyword(s):  
Extraction Method ◽  
Robust Method ◽  
Biomedical Signals ◽  
Noise Interference ◽  
Ecg Signals ◽  
Blind Source Extraction ◽  
Physiological Processes ◽  
The One ◽  
Source Of Information ◽  
Source Extraction

Biomedical signals are a rich source of information about physiological processes, but they are often contaminated by noise. In order to separate biomedical signals from mixtures effectually, we propose a novel blind source extraction method via independent component analysis (ICA). The robustness with respect to noise of this method lies in two-fold: on the one hand, the method does not lead to biassed estimates and, on the other hand, it minimizes the amount of signal and noise interference on the estimated sources. Preliminary results tested with ECG signals have demonstrated that the proposed method may be promising for blindly separating biomedical signals in the presence of noise and further decompose the mixed signals into subcomponents.

scholarly journals Study of cardiac arrhythmia classification based on Convolutional Neural Network

2020 ◽  
Vol 17 (2) ◽  
pp. 445-458
Author(s):  
Yonghui Dai ◽  
Bo Xu ◽  
Siyu Yan ◽  
Jing Xu
Keyword(s):  
Neural Network ◽  
Cardiovascular Diseases ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Time Intervals ◽  
Ecg Signals ◽  
Layer Structures ◽  
Time Periods ◽  
The One

Cardiovascular disease is one of the diseases threatening the human health, and its diagnosis has always been a research hotspot in the medical field. In particular, the diagnosis technology based on ECG (electrocardiogram) signal as an effective method for studying cardiovascular diseases has attracted many scholars? attention. In this paper, Convolutional Neural Network (CNN) is used to study the feature classification of three kinds of ECG signals, which including sinus rhythm (SR), Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF). Specifically, different convolution layer structures and different time intervals are used for ECG signal classification, such as the division of 2-layer and 4-layer convolution layers, the setting of four time periods (1s, 2s, 3s, 10s), etc. by performing the above classification conditions, the best classification results are obtained. The contribution of this paper is mainly in two aspects. On the one hand, the convolution neural network is used to classify the arrhythmia data, and different classification effects are obtained by setting different convolution layers. On the other hand, according to the data characteristics of three kinds of ECG signals, different time periods are designed to optimize the classification performance. The research results provide a reference for the classification of ECG signals and contribute to the research of cardiovascular diseases.

scholarly journals Rancang Bangun ECG Simulator Menggunakan Digital to Analog Converter R-2R

2021 ◽  
Vol 7 (1) ◽  
pp. 156-168
Author(s):  
Mulyatno Saimi
Keyword(s):  
Life Cycle ◽  
System Development ◽  
Voltage Divider ◽  
Simulation Software ◽  
Grid Data ◽  
Microsoft Excel ◽  
Digital To Analog Converter ◽  
Ecg Signals ◽  
Development Life Cycle ◽  
The One

AbstrakPengujian atau mengkalibrasi alat Electro CardioGraph membutuhkan alat ECG simulator / phantom. Saat melakukan penelitian terdahulu, peneliti sangat tergantung dengan komponen DAC impor. Penelitian ini merupakan lanjutan penelitian terdahulu dengan menggunakan komponen yang banyak terdapat di Indonesia.Ini adalah penelitian eksperimen, menggunakan metode System Development Life Cycle, dengan tahapan merancang, membangun dan menguji,  Spesifikasi ECG simulator yang akan di bangun, mampu menghasilkan sinyal ECG Normal, Sinusiodal dan Square, dengan beat rate  30, 60, 80, 120, 240 dan 300 BPM, dan amplitude pada pengukuran Lead II 0,5 Volt, 1 Volt, 1,5 volt dan 2 Volt. Memiliki output 10 terminal  standard ECG.  Kontrol dan pengolahan sinyal menggunakan Arduino pro mini, data digitalisasi ECG diperoleh dengan memperbesar cetakan sinyal ECG pada kertas kotak-kotak/grid, data sinusoidal dibuat menggunakan fungsi sin microsoft excel, sedang data digitalisasi square memberikan nilai output high dan low. Kemudian data digitalisasi diumpan ke serial to pararel IC 74595 dan DAC R2R untuk menghasilkan sinyal analog dan terakhir ke pembagi tegangan untuk terminal. Nilai resistor-resistor pembagi tegangan didapat perhitungan dan percobaan dari penelitian kami sebelumnya.Hasil pengujian menggunakan software simulasi di layar komputer, masih terlihat gradiasi konversi yang belum sempurna, tetapi cetakan pada alat ECG tidak terlihat. Pengukuran BPM sangat akurat, 100% sama antara nilai pemilihan Simulator dengan yang tercetak pada kertas ECG. Pengukuran amplitudo pada Lead II, mendekati nilai yang dipilih simulator. Kata kunci : ECG Simulator, ECG Phantom.  Abstract Testing or calibrating the Electro Cardio Graph tool requires an ECG simulator / phantom tool. When conducting previous research, researchers were very dependent on imported DAC components. This study is a continuation of previous research using components that are widely available in Indonesia.This is an experimental research , using the System Development Life Cycle method, with the stages of designing, building and testing, the ECG simulator specifications to be built are capable of producing Normal, Sinusiodal and Square ECG signals, with beat rates of 30, 60, 80, 120, 240 and 300 BPM, and amplitude on Lead II measurements of 0.5 volts, 1 volts, 1.5 volts and 2 volts. Has an output of 10 standard ECG terminals. Control and signal processing uses Arduino pro mini, ECG digitization data is obtained by enlarging the ECG signal print on grid / grid paper, sinusoidal data is made using the sin() function of microsoft excel , while square digitization data provides high and low output values. Then the digitization data is fed to the serial to parallel IC 74595 and DAC R2R to produce an analog signal and finally to the voltage divider for the terminal. The values of the voltage divider resistors are calculated and tested from our previous research.The test results using simulation software on a computer screen show that the conversion gradient is not yet perfect, but the print on the ECG tool is not visible. The BPM measurement is very accurate, 100% the same between the Simulator selection value and the one printed on the ECG paper. The amplitude measurement on Lead II is close to the value selected by the simulator. Key Word : ECG Simulator, ECG Panthom

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