Introduction to Heart

2014 ◽  
pp. 96-112
Keyword(s):  
The Body ◽  
Ecg Signal ◽  
Electrocardiogram Signals ◽  
Signal Characteristics ◽  
Improve Measurement ◽  
Computer Based ◽  
The Common ◽  
Diagnosis Tool ◽  
Heart Disorders ◽  
Electrocardiogram Ecg

The heart is an important organ in the human body, for pumping the blood throughout the body. An electrocardiogram (ECG) is a diagnosis tool that reports the electrical operation of the heart, recorded by skin electrodes at specific locations on the body. The introduction of computer-based methods for the purpose of quantifying different ECG signal characteristics is the result of a desire to improve measurement accuracy as well as reproducibility. In this chapter, the author explains the basic definitions in heart studies and the electrocardiogram signals. In addition, the importance of interpretation and measuring the effective features in heart signals to detect the heart disorders is described. Finally, some of the common disorders of heart are briefly explained.

scholarly journals Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2835 ◽  
Author(s):  
Zhongjie Hou ◽  
Jinxi Xiang ◽  
Yonggui Dong ◽  
Xiaohui Xue ◽  
Hao Xiong ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Experimental Tests ◽  
Circuit Board ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Capacitively Coupled ◽  
Ecg Signals ◽  
Printed Circuit ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Ecg

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

ECG Signal In-Band Noise De-Noising Base on EMD

2018 ◽  
Vol 28 (01) ◽  
pp. 1950017 ◽  
Author(s):  
Hui Xiong ◽  
Chunhou Zheng ◽  
Jinzhen Liu ◽  
Limei Song
Keyword(s):  
Mean Squared Error ◽  
Random Permutation ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Squared Error ◽  
Band Noise ◽  
Mode Decomposition ◽  
Interference Frequency ◽  
Heart Disorders ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) signal is widely used for diagnosis of heart disorders. However, ECG signal is a kind of weak signal to be interfered with heavy background interferences. Moreover, there are some overlaps between the interference frequency sub-bands and the ECG frequency sub-bands, so it is difficult to inhibit noise in the ECG signal. In this paper, the ECG signal in-band noise de-noising method based on empirical mode decomposition (EMD) is proposed. This method uses random permutation to process intrinsic mode functions (IMFs). It abstracts QRS complexes to separate them from noise so that the clean ECG signal is obtained. The method is validated through simulations on the MIT-BIH Arrhythmia Database and experiments on the measured test data. The results indicate that the proposed method can restrain noise, improve signal noise ratio (SNR) and reduce mean squared error (MSE) effectively.

scholarly journals Detection and Classification of ECG Signal through Machine learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3221-3227
Keyword(s):  
The Body ◽  
Wave Form ◽  
Ecg Signal ◽  
Software Application ◽  
Ecg Signals ◽  
Temporal Features ◽  
Associate Anomaly ◽  
The Individual ◽  
Electrocardiogram Ecg

The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.

Epicardial (EPI) and intravascular (IV) electrocardiogram (ECG) signal characteristics in beagle dogs: A comprehensive one-year review

2014 ◽  
Vol 70 (3) ◽  
pp. 333
Author(s):  
Kyle O'Donohue ◽  
Tara Posthumus ◽  
Michael Eliel ◽  
David Gauvin ◽  
Jill Dalton ◽  
...  
Keyword(s):  
Ecg Signal ◽  
Beagle Dogs ◽  
Signal Characteristics ◽  
One Year ◽  
Electrocardiogram Ecg

scholarly journals Utilizing ECG Waveform Features as New Biometric Authentication Method

2018 ◽  
Vol 8 (2) ◽  
pp. 658 ◽  
Author(s):  
Ahmed Younes Shdefat ◽  
Moon-Il Joo ◽  
Sung-Hoon Choi ◽  
Hee-Cheol Kim
Keyword(s):  
Template Matching ◽  
The Body ◽  
Classification Task ◽  
Ecg Signal ◽  
Recording Device ◽  
Svm Algorithm ◽  
Linear Svm ◽  
Electrocardiogram Ecg ◽  
Matching Techniques ◽  
Electrical Activities

<p>In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.</p>

A Fourier-Bessel Expansion-Based Method for Automated Detection of Atrial Fibrillation From Electrocardiogram Signals

2019 ◽  
pp. 248-277
Author(s):  
Ashish Sharma ◽  
Shivnarayan Patidar
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Signal ◽  
Clinical Environment ◽  
Test Dataset ◽  
Ecg Signals ◽  
Rr Interval ◽  
Electrocardiogram Signals ◽  
Detection And Identification ◽  
Bagged Decision Trees ◽  
Electrocardiogram Ecg

This chapter presents a new methodology for detection and identification of cardiovascular diseases from a single-lead electrocardiogram (ECG) signal of short duration. More specifically, this method deals with the detection of the most common cardiac arrhythmia called atrial fibrillation (AF) in noisy and non-clinical environment. The method begins with appropriate pre-processing of ECG signals in order to get the RR-interval and heart rate (HR) signals from them. A set of indirect features are computed from the original and the transformed versions of RR-interval and HR signals along with a set of direct features that are obtained from ECG signals themselves. In all, 47 features are computed and subsequently they are fed to an ensemble system of bagged decision trees for classifying the ECG recordings into four different classes. The proposed method has been evaluated with 2017 PhysioNet/CinC challenge hidden test dataset (phase II subset) and the final F1 score of 0.81 is obtained.

Identification of electrocardiogram signals using internet of things based on combinatory classification

2017 ◽  
Vol 08 (03) ◽  
pp. 1750035 ◽  
Author(s):  
Marzieh Faridi Masouleh ◽  
Mohammad Ali Afshar Kazemi ◽  
Mahmood Alborzi ◽  
Abbas Toloie Eshlaghy
Keyword(s):  
Feature Extraction ◽  
Internet Of Things ◽  
Health Systems ◽  
Information Provision ◽  
Ecg Signal ◽  
Electrocardiogram Signals ◽  
Digital Identification ◽  
Electrocardiogram Ecg ◽  
Service Customization ◽  
Taking Care

Combination of computer sciences and electronics has resulted in one of the most remarkable technologies of the recent years called internet of things, considered as a challenge in electronic health systems for taking care of patients. Internet of things presents a promising paradigm for management of digital identification in the form of service customization. The effect of internet of things on healthcare is still in its preliminary stages and requires a substantial development. Various equipment and services are developed and utilized for health systems by providing different things to establish communication and information provision to users at any conditions or places. In this paper, attempts have been made to detect electrocardiogram (ECG) signal through a wireless simple sensing network of body using internet of things operating based on classification and feature extraction.

The Common as Body Without Organs

2010 ◽  
Vol 4 (supplement) ◽  
pp. 46-63
Author(s):  
Vidar Thorsteinsson
Keyword(s):  
The Body ◽  
Fundamental Importance ◽  
Recent Book ◽  
Social Production ◽  
Deleuze And Guattari ◽  
Political Thinking ◽  
Conceptual Apparatus ◽  
Body Without Organs ◽  
Social Factory ◽  
The Common

The paper explores the relation of Michael Hardt and Antonio Negri's work to that of Deleuze and Guattari. The main focus is on Hardt and Negri's concept of ‘the common’ as developed in their most recent book Commonwealth. It is argued that the common can complement what Nicholas Thoburn terms the ‘minor’ characteristics of Deleuze's political thinking while also surpassing certain limitations posed by Hardt and Negri's own previous emphasis on ‘autonomy-in-production’. With reference to Marx's notion of real subsumption and early workerism's social-factory thesis, the discussion circles around showing how a distinction between capital and the common can provide a basis for what Alberto Toscano calls ‘antagonistic separation’ from capital in a more effective way than can the classical capital–labour distinction. To this end, it is demonstrated how the common might benefit from being understood in light of Deleuze and Guattari's conceptual apparatus, with reference primarily to the ‘body without organs’ of Anti-Oedipus. It is argued that the common as body without organs, now understood as constituting its own ‘social production’ separate from the BwO of capital, can provide a new basis for antagonistic separation from capital. Of fundamental importance is how the common potentially invents a novel regime of qualitative valorisation, distinct from capital's limitation to quantity and scarcity.

Our Bodies, Whose Property?

2013 ◽  
Author(s):  
Anne Phillips
Keyword(s):  
Labor Markets ◽  
Personal Autonomy ◽  
The Body ◽  
Body Parts ◽  
Complex Issue ◽  
Human Organs ◽  
Property Owners ◽  
The Common ◽  
The Difference ◽  
The Right

No one wants to be treated like an object, regarded as an item of property, or put up for sale. Yet many people frame personal autonomy in terms of self-ownership, representing themselves as property owners with the right to do as they wish with their bodies. Others do not use the language of property, but are similarly insistent on the rights of free individuals to decide for themselves whether to engage in commercial transactions for sex, reproduction, or organ sales. Drawing on analyses of rape, surrogacy, and markets in human organs, this book challenges notions of freedom based on ownership of our bodies and argues against the normalization of markets in bodily services and parts. The book explores the risks associated with metaphors of property and the reasons why the commodification of the body remains problematic. The book asks what is wrong with thinking of oneself as the owner of one's body? What is wrong with making our bodies available for rent or sale? What, if anything, is the difference between markets in sex, reproduction, or human body parts, and the other markets we commonly applaud? The book contends that body markets occupy the outer edges of a continuum that is, in some way, a feature of all labor markets. But it also emphasizes that we all have bodies, and considers the implications of this otherwise banal fact for equality. Bodies remind us of shared vulnerability, alerting us to the common experience of living as embodied beings in the same world. Examining the complex issue of body exceptionalism, the book demonstrates that treating the body as property makes human equality harder to comprehend.

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