An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

Abstract

Background: During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems.

Objective: To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients.

Methodology: We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal.

Results: Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments.

Conclusion: Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.

Keywords: Artificial intelligence; Defuzzification; Features extraction; Finite impulse response; Fuzzy membership and rules; Global system for mobile communication; Internet of things; MIT-BIH database; QRS peaks; SARS-CoV-2.

Graphical abstract

Fig. 1

Hardware setup of the proposed methodology.

Fig. 2

Proposed network for our system using an IoT gateway.

Fig. 3

Stages in ECG signal processing.

Fig. 4

Plot of the membership functions of the climate example.

Fig. 5

Plot of the membership functions of the climate example, highlighting temperature values: 21, 23 and 29 degrees Celsius.

Fig. 6

Block diagram of the fuzzy logic.

Fig. 7

Block diagram of the intelligent system.

Fig. 8

Output membership functions and assignments.

Fig. 9

Plot of the membership functions of the output variable $z$ in the hospital example.

Fig. 10

Cut of the membership function of the fuzzy set $F$ in the hospital example.

Fig. 11

Cut of the membership function of the fuzzy set $G$ in the hospital example.

Fig. 12

Cut of the membership function of the fuzzy set $H$ in the hospital example.

Fig. 13

Region over which the centroid method is applied in the hospital example.

Fig. 14

Stages of the system training.

Fig. 15

Design of the intelligent system.

Fig. 16

Proposed IoT-based intelligent methodology.

Fig. 17

Flowchart of the proposed system.

Fig. 18

The QRS peaks of the ECG of signal.

Fig. 19

Recorded normal ECG signal with 1000 samples (a); recorded first arrhythmia ECG signal (b); recorded second arrhythmia ECG signal (c); and recorded third arrhythmia ECG signal (d).

Fig. 20

The filtered outputs for normal status.

Fig. 21

The filtered outputs for first arrhythmia.

Fig. 22

The filtered outputs for second arrhythmia.

Fig. 23

The filtered outputs for third arrhythmia.

Fig. 24

Features plot for the normal signal.

Fig. 25

Features plot for the first arrhythmia.

Fig. 26

Features plot for the second arrhythmia.

Fig. 27

Features plot for the third arrhythmia.

Fig. 28

Plot for features values of original signals and six filter banks.

Fig. 29

Assignment of membership functions to SF0 (a), SF1 (b), KF0 (c), and KF1 (d).

Fig. 30

Features of the third arrhythmia signal as input.

Fig. 31

Results of arrhythmia signals being highly likely.

Fig. 32

Decision surface between SF1-SF0 (a) and SF0-KF0 (b).

Fig. 33

Decision surface for diagnosis of arrhythmia patients between SF1 and KF1.

Fig. 34

Last 1000 samples recorded for the third arrhythmia ECG signal.

Fig. 35

Features of the last 1000 samples of the third arrhythmia patient as input.

Fig. 36

Results of the third arrhythmia patient being highly likely for the last 1000 samples.

Fig. 37

Results of the second arrhythmia patient for the first 1000 samples.

Fig. 38

Results of the second arrhythmia patient being highly likely for first 1000 samples.

Fig. 39

Results of the second arrhythmia patient for the last 1000 samples.

Fig. 40

Features values of the last 1000 samples of the second arrhythmia patient as input.

Fig. 41

Results of the second arrhythmia patient being highly likely for the last 1000 samples.