Intelligent Patient Monitoring for Arrhythmia and Congestive Failure Patients Using Internet of Things and Convolutional Neural Network

Domain:

healthcare

Record type:

paper

Creator:

KarTabDelDan

Editor:

EcoLabLabUni

Publisher:

CCSD

Host:

International audience In the current paper we present a low cost intelligent system capable to collect data from one lead Electrocardiogram (ECG) sensor, process the collected data and classify the signal into one of three categories: arrhythmia, congestive failure or normal heart beat with 100 % positive predictive value and 100% negative predictive value. The achieved performance uses only 1 min data recording for every patient which increases the probability to save the patient's life and outperform state of the art of similar systems. The proposed system can be used for a specified patient and can handle longer ECG records. The system can also be trained by other databases and can by then classify and monitor new types of recorded ECG signals, thanks to its simplicity and computational efficiency.

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