Cardiac Health Prediction using Electrocardiography

Amongst various physiological signals, that can be collected from the human body, Electrocardiogram (ECG) is one widely used signal that gives an overview of individual’s health non-invasively. Some prognostic tools, based on ECG, have already been introduced in the past. However, the diagnostic information contained in ECG is still under used. In the present study, we propose an algorithm that predicts the cardiac health (both present and future) by analyzing subject’s ECG. The prediction is based on diagnostic information like Blood Pressure (BP), Arrhythmia and Heart Rate Variability (HRV), where BP and Arrhythmia are used to predict the present cardiac health, and Arrhythmia and HRV are used to predict the future cardiac health associated with an individual. To verify the algorithm, we use: (1) Linear Regression Model to extract BP based on parameters extracted from ECG; (2) Neural Network Pattern Recognition Application to detect Arrhythmia- Right and Left bundle branch block beat, Atrial premature contraction beat, Premature ventricular contraction beat and Premature or ectopic supraventricular beats, in any ECG signal; (3) SelfOrganized Maps for HRV analysis using ECG. These models are used on ECG of 30 subjects chosen from an existing database. Based on the outputs of these models our algorithm predicts the present as well as the future cardiac health of 30 subjects under study. Our predictions are compared with the present and future cardiac health of these subjects already documented in the database. The prediction accuracy showed that present and future cardiac health risk of an individual can be satisfactorily determined using the proposed algorithm, which, in future, can be easily incorporated in any health monitoring device which can record ECG.

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FRACTAL ANALYSIS OF THE ELECTROCARDIOGRAM SIGNAL

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519414500559 ◽

2014 ◽

Vol 14 (04) ◽

pp. 1450055 ◽

Cited By ~ 2

Author(s):

IBTICEME SEDJELMACI ◽

F. BEREKSI-REGUIG

Keyword(s):

Premature Ventricular Contraction ◽

Multifractal Spectrum ◽

Bundle Branch Block ◽

Ecg Signals ◽

Linear Approach ◽

Atrial Premature Beat ◽

The Subject ◽

Electrocardiogram Signal ◽

Electrocardiogram Ecg

In this paper, the analysis of the electrocardiogram (ECG) signal is carried out according a non-linear approach. This concerns the eventual fractal behavior of such signal and the correlation of such behavior with normal and pathological ECG signals. The analysis is carried out on different ECG signals taken from the MIT-BIH arrhythmia database. In fact these signals are those of six subjects with different ages and presenting both normal and abnormal arrhythmias situations. The abnormal situations are atrial premature beat (APB), premature ventricular contraction (PVC), right bundle branch block (RBBB) and left bundle branch block (LBBB). The fractal behavior of these signals is analyzed according to the determination of the multifractal spectrum and the fractal dimension variations and looking for eventually a fractal signature of each heart disease and age of the subject. The obtained results show a fractal signature according to the age and the pathologies for the studied cases. However further investigations are required on larger databases to confirm such results.

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Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

Remote Sensing ◽

10.3390/rs12101685 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1685 ◽

Cited By ~ 3

Author(s):

Amin Ullah ◽

Syed Muhammad Anwar ◽

Muhammad Bilal ◽

Raja Majid Mehmood

Keyword(s):

Premature Ventricular Contraction ◽

Careful Study ◽

Spectral Image ◽

Bundle Branch Block ◽

Ecg Signals ◽

The One ◽

Short Time ◽

Electrocardiogram Ecg ◽

Better Than

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

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Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier

Advances in Medical Technologies and Clinical Practice - Advanced Classification Techniques for Healthcare Analysis ◽

10.4018/978-1-5225-7796-6.ch004 ◽

2019 ◽

pp. 74-88 ◽

Cited By ~ 2

Author(s):

Chandan Kumar Jha ◽

Maheshkumar H. Kolekar

Keyword(s):

Support Vector Machine ◽

Premature Ventricular Contraction ◽

Abnormal Behavior ◽

Support Vector ◽

Bundle Branch Block ◽

Learning Classifier ◽

Beat Classification ◽

Multi Resolution Analysis ◽

Resolution Analysis ◽

Electrocardiogram Ecg

Abnormal behavior of heart muscles generates irregular heartbeats which are collectively known as arrhythmia. Classification of arrhythmia beats plays a prominent role in electrocardiogram (ECG) analysis. It is widely used in online and long-term patient monitoring systems. This chapter reports a classification technique to recognize normal (N) and five arrhythmia beats (i.e., left bundle branch block [LBBB], right bundle branch block [RBBB], premature ventricular contraction [V], paced [P], and atrial premature contraction [A]). The technique utilizes features of heartbeats extracted by the wavelet multi-resolution analysis. The feature vectors are used to train and test the classifier based on the support vector machine which has been emerged as a benchmark in machine learning classifier. It accomplishes the beat classification very efficiently. ECG records of the MIT-BIH arrhythmia database are utilized to acquire the different types of heartbeats. Performance of the proposed classifier outperforms the contemporary arrhythmia beats classification techniques.

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