Anatomizing electrocardiogram using fractal features and GUI based detection of P and T waves

Subramanian, Balambigai; Ramya, K.; Asokan, R.

doi:10.1109/wispnet.2016.7566178

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…In the proposed method, the input ECG signal is taken from the MIT-BIH arrhythmia database (R-R interval) (5 records). (Subramanian et al, 2016) have worked with the abnormal ECG signal from PhysioNet.org, an online database -(i)The MIT-BIH Normal Sinus Rhythm Database (ii)The MIT-BIH arrhythmia Database (P and T amplitude) (iii)The Intra-cardiac Atrial Fibrillation Database) (Subramanian et al, 2016).…”

Section: (A)data Acquisitionmentioning

confidence: 99%

“…Such indication may happen frequently or randomly in time scale (Gupta et al, 2010). However, analysis of irregular data set is time-consuming and challenging task, as heart is one of the most important body organs and is responsible for pumping blood in cardiovascular systems, if heart has gone out of its natural rhythm, blood circulation cycle is affected, and this can result in severe risks for the individuals (Subramanian et al, 2016). Hence, reliable and protective diagnosis of cardiac abnormalities is vital.…”

Section: Introductionmentioning

confidence: 99%

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Development of Graphical User Interface to Classify Cardiac Abnormalities using ECG Signal

Sharanya

et al. 2019

ijrps

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Analysis of Electrocardiogram (ECG) signal can lead to better detection of cardiac arrhythmia. The important steps involved in the ECG signal analysis include acquisition of data, pre-processing of signal to remove artefacts, feature extraction of attributes and finally identifying abnormalities. This work proposes an efficient implementation of the R-R interval-based ECG classification technique for detecting abnormalities in heart functioning. ECG signals from an online database (PhysioNet.org) was analysed after noise removal for R-R interval, as R peak has the maximum prominent amplitude in ECG wave. Deviation in the R-R interval values obtained from unhealthy was observed and compared with healthy subjects. This observation of cardiac activity can be visualised in our developed Graphical User Interface (GUI). The GUI platform requires only the input of the ECG signal that is to be analysed for abnormalities, which can provide the clinician with the result of cardiac abnormality classification and can help in diagnosis.

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Section: (A)data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Graphical User Interface to Classify Cardiac Abnormalities using ECG Signal

Sharanya

et al. 2019

ijrps

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show abstract

“…ECG signals are the product of a wide range of clinical ECG technologies. The electrocardiogram represents cardiac function and graphic signals [2], which are important means of diagnosing abnormal cardiac activity.…”

Section: Introductionmentioning

confidence: 99%

Visualization of ECG Data on Variant Maps

Hou¹,

Zheng²

2020

Practical Applications of Electrocardiogram

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This chapter presents variant maps for showing potential features in ECG data sets. The variant map is a visualization method different from a traditional ECG. In this chapter, the ECG data sets obtained by clinical ECG monitoring are used as the data source, and the corresponding variant maps are obtained by the variant statistics method. This chapter mainly introduces the variant statistics method about converting ECG data into variant maps. From sample results, various visual properties can be observed, and further explorations are required.

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“…Mainly relying on the detection of ECG signals to promote research on related issues of cardiovascular diseases. The electrocardiogram represents cardiac function and graphic signals [2], which is an important means of diagnosing abnormal cardiac activity.…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis of ECG Sequences on Variant Maps

Hou

Zheng

2018

Variant Construction From Theoretical Foundation to Applications

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This chapter presents the variant measurement based on the variant logic, which uses the ECG sequence as the signal source, and outputs the variant maps of ECG sequences. It provides a supplementary study for ECG detection. Samples of ECG signal are collected from the First People's Hospital of Yunnan Province. Under variant maps, main parameters of various interval values are checked and corresponding maps are illustrated.

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Anatomizing electrocardiogram using fractal features and GUI based detection of P and T waves

Cited by 3 publications

References 8 publications

Development of Graphical User Interface to Classify Cardiac Abnormalities using ECG Signal

Development of Graphical User Interface to Classify Cardiac Abnormalities using ECG Signal

Visualization of ECG Data on Variant Maps

Visual Analysis of ECG Sequences on Variant Maps

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