IEEE Circuits and Systems Magazine - Q3 2021 - 76
shown Fig. 6. Following characteristics features are
extracted and used as inputs of the unsupervised learning
algorithm:
■ Maximum intra-lung pressure ()Pp
■ Expired volume ()Pv
in millibar.
in litres.
■ Area under the pressure curve ().Ap
■ Area under the volume curve ().Av
■ Pressure pulse duration ()Dp
■ Volume pulse duration ()Dv
in seconds.
in seconds.
B. K-Means Unsupervised Learning
and Detection Algorithm
Considering that the ventilator will be used with different
patients and settings, learning use case reference
situation is necessary for later detecting issues. This
is done in unsupervised learning using a K-Means optimized
algorithm, classifying features in real time
without storing any dataset [10]-[12]. This allows an
embedded implementation in a microcontroller, improving
system reliability. Training and detection algorithms
are presented in Figs. 7(a) and 7(b).
Learning phase is preceded by an initialisation
phase. First k samples are used for evaluating data
Volume
2
Volume Peak
Volume Area
Volume
1.6
1.2
0.8
0.4
222
224
Volume Pulse Duration
Pressure
Pressure
Pressure Area
Pressure Peak
10
20
30
40
222
224
226
Figure 6. Volume and pressure features extraction.
76
IEEE CIRCUITS AND SYSTEMS MAGAZINE
THIRD QUARTER 2021
Pressure Pulse Duration
228
230
232
226
228
230
232
dispersion by calculating the standard deviation for
each feature ([ ,, ,, ,]).
vv vv vv v
r = PP AA DD
pv pv pv
Then in training phase, sets of features (composed
by 6 elements having been described in Section VI-A)
are assigned to existing clusters, if they are close to
an existing one, or to a new one if not. A cluster Cj
(where j is the cluster ID), represents a behaviour
of the couple {ventilator-patient}. A cluster is described
by its centroid
and its dispersion CP PA AD D
j
=
Notations CC C6
1
j = f [, ,] and CC C
j
used for simplifying equations. nj
=
pv pv pv
vv f v j
vv vv vv v,, ].
j
CP PA AD D
j [, ,,pv pv pv
=
j [, ,, ,, ]
jj jj jj
= 16
pv pv pv
jj jj jj
j [, ,] are
is the number of
samples classified in cluster j. Each time a feature
has been extracted, distance D between its signature
[, ,, ,, ]
SP PA AD D and each cluster is evaluated
using Eq. 2:
D =
/eo
6
i =1
SC
C
i
-
v
i
j
i
j
2
(2)
Distance D is compared to a threshold DMax
optimized
offline using deep learning. Its value is equal to 1.25.
This optimization is not the purpose of this paper.
IEEE Circuits and Systems Magazine - Q3 2021
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