Atrial Electrical Activity Detection Using Linear Combination of 12-Lead ECG Signals

2014 ◽  
Vol 61 (4) ◽  
pp. 1034-1043 ◽  
Author(s):  
Or Perlman ◽  
Amos Katz ◽  
Noam Weissman ◽  
Guy Amit ◽  
Yaniv Zigel
Keyword(s):  
Electrical Activity ◽  
Linear Combination ◽  
Activity Detection ◽  
Ecg Signals

Abstract 370: Electrical Features Of Pulseless Electrical Activity Associated With Cardiac Arrest Outcomes

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Steve Balian ◽  
David Alanis Garza ◽  
Mikel Leturiondo ◽  
Joshua R Lupton ◽  
James K Russell ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Electrical Activity ◽  
Frequency Spectrum ◽  
Prediction Models ◽  
Pulseless Electrical Activity ◽  
Median Frequency ◽  
Signal Power ◽  
Ecg Signals ◽  
Regression Methods ◽  
Band Pass

Introduction: The cardiac arrest rhythm of pulseless electrical activity (PEA) poses various diagnostic and therapeutic challenges. PEA may represent a spectrum of arrest conditions with variable responses to resuscitation care. Aim: We analyzed PEA rhythms to identify diagnostic patterns associated with survival in cardiac arrest. Methods: In this retrospective cohort study, we utilized the Portland Resuscitation Outcomes Consortium database of out-of-hospital cardiac arrests compiled by the Tualatin Valley Fire and Rescue from 2006-2016. Recordings from defibrillation pads included compression waveforms, electrocardiogram, and transthoracic impedance signals. For each patient, we analyzed the first two pauses in chest compressions, characterized by flat compression and impedance signals. Features extracted from raw ECG signals included contraction frequency and variability. Signal Fourier transformation and 0-100 Hz band pass filtering yielded signals’ distribution across a frequency spectrum from which signal power was extracted. Extraction of the three most prominent frequencies was performed from the Gaussian filtered frequency spectrum. Non-parametric tests (Mann-Whitney, Fisher) and logistic regression methods were used for analysis. Results: Fifty-nine ECG recordings were analyzed corresponding to 7 (11.9%) survivors and 52 (88.1%) non-survivors. Median age was 72 (IQR 20), and 28.8% (17/59) were female. No significant differences were noted in sex or median age between survivors and non-survivors. Analysis of the first ECG pause showed a higher first peak median frequency among survivors (2.15 vs 0.06 Hz, p=0.049). We did not find a significant association between the second peak median frequency of the first ECG segment (6.46 vs 1.49 Hz, p=0.882) or the signal power of the second ECG segment (108.04 vs 100.77 Hz, p=0.647) with survival. Regression analysis did not provide reliable outcome prediction models for survival in this preliminary cohort. Conclusion: Computerized analysis of PEA ECG waveforms offers alternate approaches to bedside signal interpretation that may correlate with survival. Our preliminary work offers a potential approach to PEA analysis that will require application to a larger PEA arrest cohort.

scholarly journals IMPROVEMENT OF THE CREDIBILITY OF ANALYSIS OF ELECTROCARDIOGRAMS FOR BIOMETRIC PERSONAL IDENTIFICATION

2020 ◽  
pp. 125-130
Author(s):  
Яковенко І.О. ◽  
Рудий О.Д. ◽  
Турчина М.О.
Keyword(s):  
Electrical Activity ◽  
Age Groups ◽  
Personal Identification ◽  
Physical Parameters ◽  
Biometric Authentication ◽  
High Demand ◽  
Biometric Data ◽  
Ecg Signals ◽  
High Level ◽  
Electrocardiogram Ecg

Nowadays there is a high demand for biometric authentication. These systems possess a high level of protection, as they evaluate not only the physical parameters, but also personality characteristics. The paper analyzes a biometric scheme based on the electrical activity of the human heart in the form of electrocardiogram (ECG) signals. The study was performed using standard laboratory measurements KL-720 has all age groups. As a result, an electrical activity signal was obtained. The aim of this work was to filter the captured signal for further use with biometric data.

ECG Data Analysis

2010 ◽  
pp. 122-144 ◽  
Author(s):  
Saurabh Pal ◽  
Swanirbhar Majumder
Keyword(s):  
Heart Failure ◽  
Data Analysis ◽  
Electrical Activity ◽  
Automatic Classification ◽  
Classification Techniques ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Electrocardiogram Ecg ◽  
Acquisition Technique ◽  
Ecg Data

In this chapter authors explain an idea for automation of heart failure with the help of ECG signals. An electrocardiogram (ECG) is a test that records the electrical activity of the heart. A brief description on automatic classification techniques is also given. ECG being the most vital physiological signal, its acquisition technique, noise and artifacts elimination methodologies are discussed in this chapter.

scholarly journals Digital ECG Phantom Design to Represent the Human Heart Signal for Early Test on ECG Machine in Hospital

2021 ◽  
Vol 1 (1) ◽  
pp. 14-19
Author(s):  
Sella Octa Ardila ◽  
Endro Yulianto ◽  
Sumber Sumber
Keyword(s):  
Signal Processing ◽  
Electrical Activity ◽  
Human Heart ◽  
Analog Circuit ◽  
Digital Signal ◽  
Calibration Procedure ◽  
Ecg Signal ◽  
Recording Device ◽  
Ecg Signals ◽  
Measurement Results

Electrocardiograph (ECG) is a diagnostic tool that can record the electrical activity of the human heart. By analyzing the resulting waveforms of the recorded electrical activity of the heart, it is possible to record and diagnose disease. Given the importance of the ECG recording device, it is necessary to check the function of the ECG recording device, namely by performing a device calibration procedure using the Phantom ECG which aims to simulate the ECG signal. The purpose of this research is to check the ECG device during repairs, besides that the Electrocardiograph (EKG) tool functions for research purposes on ECG signals or for educational purposes. Electrocardiograph (EKG) simulator or often called Phantom ECG is in principle a signal generator in the form of an ECG like signal or a recorded ECG signal. This device can be realized based on microcontroller and analog circuit. The advantage of this simulator research is that the ECG signal displayed is the original ECG recording and has an adequate ECG signal database. ECG This simulator also has the advantage of providing convenience for research on digital signal processing applications for ECG signal processing. In its application this simulator can be used as a tool to study various forms of  ECG signals. Based on the measurement results, the error value at BPM 30 and 60 is 0.00% at the sensitivity of 0.5mV, 1.0mV, and 2.0mV, then the measurement results for the error value at BPM 120 are 0.33% and at the BPM 180 value, the error value is 0.22%. From these results, it can be concluded that the highest error value is at BPM 120 with sensitivities of 0.5mV, 1.0mV, and 2.0mV.  

Filtration and Classification of ECG Signals

2018 ◽  
pp. 72-94 ◽  
Author(s):  
Satya Ranjan Dash ◽  
Asim Syed Sheeraz ◽  
Annapurna Samantaray
Keyword(s):  
Electrical Activity ◽  
Electrical Conduction ◽  
Diagnostic Tool ◽  
Noise Level ◽  
Medical Professionals ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Electrocardiogram Ecg ◽  
Structure And Functions

Electrocardiogram (ECG) is a kind of process of recording the electrical activity/signals of the heart with respect to the time. ECG conveys a wide amount of information related to the structure and functions of the heart, its electrical conduction processes. ECG is a diagnostic tool that the doctors and medical professionals use to measure patients' heart activity by paying attention to the electric current flowing in the heart. Due to the presence of noises, it needs to carry out the filtration process. Filtration is the process of keeping the components of the signals of desired frequencies by setting up an “fc” value and removing the components apart from the said “fc” frequency. It is required to eliminate the noise level from the ECG signal, such that the resultant ECG signal must be free from noises. All these techniques and algorithms have their advantages and limitations which are discussed in this chapter.

scholarly journals Central Monitor Based On Personal Computer Using One Wireless Receiver

2019 ◽  
Vol 1 (1) ◽  
pp. 7-13
Author(s):  
Muhammad Nezar Abdullah Mufarid ◽  
Bambang Guruh Irianto ◽  
Andjar Pudji
Keyword(s):  
Electrical Activity ◽  
Wireless Systems ◽  
Scientific Paper ◽  
Tolerance Limit ◽  
The Body ◽  
Measuring Instrument ◽  
Wireless Receiver ◽  
Ecg Signals ◽  
Monitor Display ◽  
Health Field

Central monitor is a tool in the health field that serves to monitor the patient's condition which is centralized in one monitor display centrally. In this scientific paper raised wireless systems for sending data to one monitor. In this module there are Electrocardiograph (EKG) parameters which are a parameter to detect and measure the electrical activity of the heart muscle using measurements of biopotential signals obtained from the surface of the body. From these measurements, an ECG signal will be obtained to produce a heart rate per minute (BPM). ECG signals are obtained from measurements of the electrical activity of the heart through electrodes placed on the patient's skin using the bipolar lead method. ECG signals will be processed using a  microcontroller circuit as processors. Then the data will be sent to the PC using wireless HC-11. The data received by the PC, then processed using the Delphi application which will then display ECG charts and BPM results and abnormalities indicators if the BPM is in a condition above or below normal. By comparing the module with a standard measuring instrument, the biggest error is 0.99% which is still in tolerance because the tolerance limit is 5%

Effect of a Motivational Song on the Autonomic Nervous System and the Heart of Indian Male Volunteers

2018 ◽  
pp. 299-317 ◽  
Author(s):  
Gitika Yadu ◽  
Suraj Kumar Nayak ◽  
Debasisha Panigrahi ◽  
Sirsendu Sekhar Ray ◽  
Kunal Pal
Keyword(s):  
Heart Rate ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Electrical Activity ◽  
Stimulus Condition ◽  
Cardiac Activity ◽  
Ecg Signals ◽  
Autonomic Nervous ◽  
Cardiac Electrical Activity ◽  
Hrv Analysis

This chapter investigates the effect of a motivational song (stimulus) on the physiology of the autonomic nervous system and the electrical activity of the heart. Five min electrocardiogram (ECG) signals were acquired from 19 volunteers during the resting and the post-stimulus conditions. The RR intervals (RRIs) were extracted. Recurrence analysis of the RRI time series indicated a higher alteration (acceleration or deceleration) in the heart rate along with the reduction of the causality and patterned behavior of the RRIs. The exact alteration in the ANS physiology was examined using heart rate variability (HRV) analysis. The results of the HRV analysis suggested an increase in the parasympathetic activity in the post-stimulus condition. The alteration in the cardiac activity was analyzed using time domain and joint time-frequency domain analyses of ECG signals. The results suggested an alteration in the cardiac electrical activity of the heart in the post-stimulus condition.

scholarly journals Pengembangan Perangkat EKG 12 Lead dan Aplikasi Client-Server untuk Distribusi Data

2015 ◽  
Vol 3 (2) ◽  
pp. 91
Author(s):  
SUGONDO HADIYOSO ◽  
MUHAMMAD JULIAN ◽  
ACHMAD RIZAL ◽  
SUCI AULIA
Keyword(s):  
Electrical Activity ◽  
Switching Time ◽  
Digital Data ◽  
Ecg Signal ◽  
Client Server ◽  
Ecg Signals ◽  
Device Development ◽  
Electrocardiogram Signal ◽  
Client Computer ◽  
Server Computer

ABSTRAKElektrokardiograf adalah perangkat untuk mengukur aktifitas kelistrikan jantung. Sinyal yang ditampilkan oleh perangkat elektrokardiograf adalah sinyal elektrokardiogram (EKG). Untuk monitoring ECG  minimal diperlukan satu lead sementara untuk standar klinis diperlukan 12 lead. Untuk realisasi perangkat EKG 12 lead diperlukan strategi agar jumlah perangkat keras yang dibutuhkan semakin sedikit sehingga dimensi menjadi lebih kecil. Untuk mengatasi permasalahan tersebut, pada penelitian ini dirancang perangkat EKG 12 lead dengan teknik multipleksing. Kombinasi sadapan sinyal EKG 12 lead dikontrol oleh multiplekser 4051 melalui mikrokontroler secara bergantian. Data dijital hasil konversi ADC selanjutnya dikirim secara serial ke komputer server dan dapat dilihat pada komputer client yang terhubung. Hasil yang didapat menunjukkan bahawa perangkat analog telah berhasil mengakuisisi sinyal EKG dengan baik dari Lead I sampai Lead V6. Dengan waktu pensakelaran sebesar 5 ms, sinyal tidak dapat ditampilkan secara simultan 12 lead. Sinyal dapat diakuisisi dengan baik jika waktu pensakelaran sebesar 5 detik namun seluruh sadapan sinyal EKG tidak dapat ditampilkan secara simultan.Kata kunci: Elektrokardiograf, 12 Lead, Multipleksing, Server, Client. ABSTRACTElectrocardiograph is device for measuring electrical activity of heart. Electrocardiograph displays electrocardiogram signal (ECG). For monitoring ECG, at least need one ECG lead meanwhile for standard clinical ECG need 12 lead. For realization of 12 lead ECG devices, it is need strategy to reduce number of hardware to make dimension of ECG device smaller. To solve this problem, we use multiplexing method for ECG device development. Combination of 12 lead ECG signal is controlled by the multiplexer 4051 through microcontroller sequentially. Digital data of ADC is sent serially to the server computer and can be viewed on client computer that connected to the network. From the results obtained indicate that analog devices have been successfully acquired ECG signals Lead I to Lead V6. With 5 ms switching time, the 12 lead ECG signal can not be displayed simultaneously. The signal can be acquired properly with 5 seconds switching time, but the whole of ECG signals can not be displayed simultaneously.Keywords: Electrocardiograph, 12 Lead, Multiplexing, Server, Client.

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