Dami S. Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic. World J Clin Cases 2022; 10(26): 9207-9218 [PMID: 36159404 DOI: 10.12998/wjcc.v10.i26.9207]
Corresponding Author of This Article
Sina Dami, PhD, Assistant Professor, Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Ashrafi Esfahani Highway-End of Shahid Azari Street, Tehran 1468763785, Iran. dami@wtiau.ac.ir
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Sep 16, 2022; 10(26): 9207-9218 Published online Sep 16, 2022. doi: 10.12998/wjcc.v10.i26.9207
Table 1 The most important features of electrocardiogram signals in the UCI dataset
Features
Values
Age
Yr
Sex
Male = 0, female = 1
Height
cm
Weight
Kg
QRS length
Average QRS length in milliseconds
Distance P-R
Average time interval between the start of waves P and Q in milliseconds
Distance Q-T
Average time interval between start of wave Q and end of wave T in milliseconds
Distance T
Average time interval of wave T in milliseconds
Distance P
Average P wave distance in milliseconds
QRS
Degree vector angles on the screen
T
Degree vector angles on the screen
P
Degree vector angles on the screen
QRST
Degree vector angles on the screen
J
Degree vector angles on the screen
Heart rate
Heart rate per minute
Table 2 Cardiac arrhythmia classes in the UCI dataset
Class No.
Class name
Number of classes
C1
Normal
245
C2
Ischemic changes (coronary artery diseases)
44
C3
Old anterior myocardial infarction
15
C4
Old inferior myocardial infarction
15
C5
Sinus tachycardy
13
C6
Sinus bradycardy
25
C7
Ventricular premature contraction (pvc)
3
C8
Supraventricular premature contraction
2
C9
Left bundle branch block
9
C10
Right bundle branch block
50
C11
1 Degree antrioventricular block
0
C12
2 Degree AV block
0
C13
3 Degree AV block
0
C14
Left ventricule hypertrophy
4
C15
Atrial fibrillation or flutter
5
C16
Others
22
Table 3 The confusion matrix
True results
Positive
Negative
Test results
Positive
TP
FP
Negative
FN
TN
Table 4 Long short-term memory model training and test times with/without rough set theory feature selection
Time
LSTM
RST-LSTM
Time reduction
Training
217154 ms
69247 ms
68.11%
Test
23854 ms
3856 ms
83.83%
Table 5 Positive prediction value of detection of the proposed system by cardiac arrhythmia classes
Class No.
LSTM
RST-LSTM
Class No.
LSTM
RST-LSTM
C1
97.65
98.44
C9
NaN
NaN
C2
89.54
90.23
C10
98.49
99.83
C3
98.76
99.08
C11
NaN
NaN
C4
99.14
99.12
C12
NaN
NaN
C5
98.26
98.74
C13
NaN
NaN
C6
94.29
96.63
C14
98.16
98.94
C7
NaN
NaN
C15
NaN
NaN
C8
NaN
NaN
C16
87.63
89.90
Average PPV
LSTM
95.76
Average PPV
RST-LSTM
96.77
Table 6 Negative prediction value of detection of the proposed system by cardiac arrhythmia classes
Class No.
LSTM
RST-LSTM
Class No.
LSTM
RST-LSTM
C1
96.74
98.14
C9
NaN
NaN
C2
84.27
86.45
C10
97.45
98.32
C3
98.79
99.16
C11
NaN
NaN
C4
97.56
98.87
C12
NaN
NaN
C5
98.23
98.12
C13
NaN
NaN
C6
90.29
92.64
C14
98.34
99.05
C7
NaN
NaN
C15
NaN
NaN
C8
NaN
NaN
C16
83.11
85.36
Average NPV
LSTM
93.86
Average NPV
RST-LSTM
95.12
Table 7 Sensitivity of detection of the proposed system by cardiac arrhythmia classes
Class No.
LSTM
RST-LSTM
Class No.
LSTM
RST-LSTM
C1
98.54
99.16
C9
NaN
NaN
C2
86.73
88.57
C10
98.24
98.65
C3
99.54
99.52
C11
NaN
NaN
C4
97.98
98.86
C12
NaN
NaN
C5
98.36
98.87
C13
NaN
NaN
C6
89.56
92.19
C14
97.92
99.03
C7
NaN
NaN
C15
NaN
NaN
C8
NaN
NaN
C16
81.57
82.87
Average sensitivity
LSTM
94.27
Average sensitivity
RST-LSTM
95.30
Citation: Dami S. Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic. World J Clin Cases 2022; 10(26): 9207-9218