Smart Healthcare for ECG Telemonitoring System

Saeed, Jwan Najeeb; Ameen, Siddeeq Y.

doi:10.30880/jscdm.2021.02.02.007

Cited by 4 publications

(2 citation statements)

References 60 publications

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“…Finally, computerized ECG analysis utilizing widely available telecommunication infrastructure enables using all above benefits in areas with a lack of human experts or in telemedicine applications. In the latter, computer-aided systems play a crucial role by supporting the fast assessment of a huge amount of ECG records ( Saeed and Ameen, 2021 ).…”

Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

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Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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“…The classification of the extraction feature is developed using Euclidian distance where a Nyman Pearson classification is developed in [10]. In [11] a wavelet-based transformation is developed for feature extraction and a temporal relation is used in the developing the feature selection. A feature extraction and selection approach are outlined in [12] where P, T peaks were used in diagnosis of heart disease.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Classification Approach using Feature Fusion Model for Heart Disease Diagnosis

Vasantrao¹,

Rangasamy²,

Shelke³

2022

IJACSA

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Early Diagnosis has a very critical role in medical data processing and automated system. In medical diagnosis, automation is focused in different area of applications, in which heart disease diagnosis is a prominent domain. An early detection of heart disease can save many lives or criticality issues in diagnosing patients. In the process of heart disease diagnosis spatial and frequency domain features are used in making decision by the automation system. The processing features are observed to time variant or invariant in nature and the criticality of the observing feature varies with the diagnosis need. Wherein, the current automation system utilizes the features extracted in a large count to attain a higher accuracy, the processing overhead, and delay are considerable. Different regression approaches were developed in recent past to minimize the processing feature overhead the features are optimized based on gain performance or distance factors. The characteristic variation of feature and the significance of the feature vector are not addressed. This paper outlines a method of feature selection for heart disease diagnosis, based on weighted method of feature vector in consideration of feature significance and probability of estimate. A new optimizing function for feature selection is proposed as a dual function of probability factor and feature weight value. Simulation results illustrate the improvement of accuracy and speed of computation using proposed method compared to other existing methods.

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