Low-Cost ECG-Based Heart Monitoring System with Ubidots Platform

Vamseekrishna, A.; Prasad, M. Siva Ganga; Krishna, Pravin; Bhargavi, P.; Rohit, S.; Tanmayi, B.

doi:10.1007/978-981-19-1484-3_12

Cited by 1 publication

(1 citation statement)

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“…The AD8232 module is used to display the ECG signal in real time. Many additional studies employed ECG signal classifications based on artificial intelligence algorithms to detect cardiac problems early and treat them appropriately [2,4,17]. The feature extraction stage is critical in classification, and several techniques for identifying and categorizing ECG heartbeats have been widely employed, including wavelet transform, filter banks, hidden Markov models, support vector machines, and others.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Intelligence-Based Ecg Acquisition System for the Detection of Cardiac Abnormalities

Benba

Attaoui

Sandabad

2023

IAPGOS

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The electrocardiogram (ECG) is a common test that measures the electrical activity of the heart. On the ECG, several cardiac abnormalities can be seen, including arrhythmias, which are one of the major causes of cardiac mortality worldwide. The objective for the research community is accurate and automated cardiovascular analysis, especially given the maturity of artificial intelligence technology and its contribution to the health area. The goal of this effort is to create an acquisition system and use artificial intelligence to classify ECG readings. This system is designed in two parts: the first is the signal acquisition using the ECG Module AD8232; the obtained signal is a single derivation that has been amplified and filtered. The second section is the classification for heart illness identification; the suggested model is a deep convolutional neural network with 12 layers that was able to categorize five types of heartbeats from the MIT-BIH arrhythmia database. The results were encouraging, and the embedded system was built.

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Section: Introductionmentioning

confidence: 99%