Open AccessBiosensor Interface Controller for Chronic Kidney Disease Monitoring Using Internet of Things (IoT)
Author(s) -
Govind Maniam,
Jahariah Sampe,
Azrul Azlan Hamzah,
Mohammad Faseehuddin,
Noorhidayah
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1933/1/012110
Subject(s) - qrs complex , computer science , real time computing , internet of things , signal (programming language) , controller (irrigation) , matlab , interface (matter) , heart rate , artificial intelligence , simulation , medicine , embedded system , cardiology , blood pressure , programming language , biology , agronomy , bubble , maximum bubble pressure method , parallel computing , operating system
This paper describes the simulation done on a low-cost biosensor interface controller for Chronic Kidney Disease (CKD) monitoring system using Internet of Things (IoT). Healthcare monitoring systems are devices that keep track of human activities and health conditions using biosensors. The developed monitoring system will aid in chronic disease patients for early detection of prevailing diseases. Early prevention can be done by monitoring the electrocardiogram (ECG). However, ECG signals typically contain contaminants that cause inaccuracy in the ECG signals produced and difficulty in diagnosing the heart’s activity. The objective is to design and simulate a system to perform pre-processing of ECG signals to prevent ECG measurements from signal contamination. Next, to calculate the heart rate using filtered ECG signals and the Pan-Tompkins algorithm . The simulation was done on MATLAB and Simulink by generating pre-recorded ECG signals that will be pre-processed to obtain viable results when compared to a normal ECG cycle wave. The results show that the filtered ECG produced has all the elements of a normal ECG cycle wave with less signal contamination within the range of 0.8 – 1.3mV. The filtered ECG signals were processed for QRS peak detection to obtain the heart rate. Results show that the heart rate displayed was within the range of the pre-recorded heart rate which is 79 – 82 beats per minute (BPM). The QRS peaks detected were also identical to the results from the Pan-Tompkins algorithm .