Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health.
Citation: Saadullah Farooq Abbasi, Qammer Hussain Abbasi, Faisal Saeed, Norah Saleh Alghamdi. A convolutional neural network-based decision support system for neonatal quiet sleep detection[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 17018-17036. doi: 10.3934/mbe.2023759
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Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health.
Let
● (Divisorial)
● (Flipping)
● (Mixed)
Note that the mixed case can occur only if either
We can almost always choose the initial
Our aim is to discuss a significant special case where the
Definition 1 (MMP with scaling). Let
By the
(Xj,Θj)ϕj→Zjψj←(Xj+1,Θj+1)gj↘↓↙gj+1S | (1.1) |
where
(2)
(3)
(4)
Note that (4) implies that
In general such a diagram need not exist, but if it does, it is unique and then
(X,Θ)ϕ→Zϕ+←(X+,Θ+)g↘↓↙g+S | (1.5) |
We say that the MMP terminates with
(6) either
(7) or
Warning 1.8. Our terminology is slightly different from [7], where it is assumed that
One advantage is that our MMP steps are uniquely determined by the starting data. This makes it possible to extend the theory to algebraic spaces [33].
Theorem 2 is formulated for Noetherian base schemes. We do not prove any new results about the existence of flips, but Theorem 2 says that if the MMP with scaling exists and terminates, then its steps are simpler than expected, and the end result is more controlled than expected.
On the other hand, for 3-dimensional schemes, Theorem 2 can be used to conclude that, in some important cases, the MMP runs and terminates, see Theorem 9.
Theorem 2. Let
(i)
(ii)
(iii)
(iv)
(v) The
We run the
(1)
(a) either
(b) or
(2) The
(3)
Furthermore, if the MMP terminates with
(4)
(5) if
Remark 2.6. In applications the following are the key points:
(a) We avoided the mixed case.
(b) In the fipping case we have both
(c) In (3) we have an explicit, relatively ample, exceptional
(d) In case (5) we end with
(e) In case (5) the last MMP step is a divisorial contraction, giving what [35] calls a Kollár component; no further flips needed.
Proof. Assertions (1-3) concern only one MMP-step, so we may as well drop the index
Let
∑hi(Ei⋅C)=−r−1(EΘ⋅C). | (2.7) |
By Lemma 3 this shows that the
∑hi(e′(Ei⋅C)−e(Ei⋅C′))=0. | (2.8) |
By the linear independence of the
Assume first that
ϕ∗(EΘ+rH)=∑i>1(ei+rhi)ϕ∗(Ei) |
is
Otherwise
g−1(g(supp(EΘ+rH)))=supp(EΘ+rH). | (2.9) |
If
Thus
Assume next that the flip
Finally, if the MMP terminates with
Lemma 3. Let
∑ni=1hivi=γv0 for some γ∈L. |
Then
Proof. We may assume that
∑ni=1hiai=γa0 and n∑i=1hibi=γb0. |
This gives that
∑ni=1hi(b0ai−a0bi)=0. |
Since the
Lemma 4. Let
Proof. Assume that
∑ni=1sihi=−(∑ni=1siei)⋅∑ni=0rihi. |
If
The following is a slight modifications of [3,Lem.1.5.1]; see also [17,5.3].
Lemma 5. Let
Comments on
Conjecture 6. Let
(1)
(2) The completion of
Using [30,Tag 0CAV] one can reformulate (6.2) as a finite type statement:
(3) There are elementary étale morphisms
(x,X,∑DXi)←(u,U,∑DUi)→(y,Y,∑DYi). |
Almost all resolution methods commute with étale morphisms, thus if we want to prove something about a resolution of
A positive answer to Conjecture 6 (for
(Note that [27] uses an even stronger formulation: Every normal, analytic singularity has an algebraization whose class group is generated by the canonical class. This is, however, not true, since not every normal, analytic singularity has an algebraization.)
Existence of certain resolutions.
7 (The assumptions 2.i-v). In most applications of Theorem 2 we start with a normal pair
Typically we choose a log resolution
We want
The existence of a
8 (Ample, exceptional divisors). Assume that we blow up an ideal sheaf
Claim 8.1. Let
Resolution of singularities is also known for 3-dimensional excellent schemes [10], but in its original form it does not guarantee projectivity in general. Nonetheless, combining [6,2.7] and [23,Cor.3] we get the following.
Claim 8.2. Let
Next we mention some applications. In each case we use Theorem 2 to modify the previous proofs to get more general results. We give only some hints as to how this is done, we refer to the original papers for definitions and details of proofs.
The first two applications are to dlt 3-folds. In both cases Theorem 2 allows us to run MMP in a way that works in every characteristic and also for bases that are not
Relative MMP for dlt 3-folds.
Theorem 9. Let
Then the MMP over
(1) each step
(a) either a contraction
(b) or a flip
(2)
(3) if either
Proof. Assume first that the MMP steps exist and the MMP terminates. Note that
KX+E+g−1∗Δ∼Rg∗(KY+Δ)+∑j(1+a(Ej,Y,Δ))Ej∼g,R∑j(1+a(Ej,Y,Δ))Ej=:EΘ. |
We get from Theorem 2 that (1.a-b) are the possible MMP-steps, and (2-3) from Theorem 15-5.
For existence and termination, all details are given in [6,9.12].
However, I would like to note that we are in a special situation, which can be treated with the methods that are in [1,29], at least when the closed points of
The key point is that everything happens inside
Contractions for reducible surfaces have been treated in [1,Secs.11-12], see also [12,Chap.6] and [31].
The presence of
The short note [34] explains how [15,3.4] gives 1-complemented 3-fold flips; see [16,3.1 and 4.3] for stronger results.
Inversion of adjunction for 3-folds. Using Theorem 9 we can remove the
Corollary 10. Let
This implies that one direction of Reid's classification of terminal singularities using 'general elephants' [28,p.393] works in every characteristic. This could be useful in extending [2] to characteristics
Corollary 11. Let
Divisor class group of dlt singularities. The divisor class group of a rational surface singularity is finite by [24], and [8] plus an easy argument shows that the divisor class group of a rational 3-dimensional singularity is finitely generated. Thus the divisor class group of a 3-dimensional dlt singularity is finitely generated in characteristic
Proposition 12. [21,B.1] Let
It seems reasonable to conjecture that the same holds in all dimensions, see [21,B.6].
Grauert-Riemenschneider vanishing. One can prove a variant of the Grauert-Riemenschneider (abbreviated as G-R) vanishing theorem [13] by following the steps of the MMP.
Definition 13 (G-R vanishing). Let
Let
(1)
(2)
Then
We say that G-R vanishing holds over
By an elementary computation, if
If
G-R vanishing also holds over 2-dimensional, excellent schemes by [24]; see [20,10.4]. In particular, if
However, G-R vanishing fails for 3-folds in every positive characteristic, as shown by cones over surfaces for which Kodaira's vanishing fails. Thus the following may be the type of G-R vanishing result that one can hope for.
Theorem 14. [5] Let
Proof. Let
A technical problem is that we seem to need various rationality properties of the singularities of the
For divisorial contractions
For flips
From G-R vanishing one can derive various rationality properties for all excellent dlt pairs. This can be done by following the method of 2 spectral sequences as in [19] or [20,7.27]; see [5] for an improved version.
Theorem 15. [5] Let
(1)
(2) Every irreducible component of
(3) Let
See [5,12] for the precise resolution assumptions needed. The conclusions are well known in characteristic 0, see [22,5.25], [12,Sec.3.13] and [20,7.27]. For 3-dimensional dlt varieties in
The next two applications are in characteristic 0.
Dual complex of a resolution. Our results can be used to remove the
Corollary 16. Let
Theorem 17. Let
(1)
(2)
(3)
Then
Proof. Fix
Let us now run the
Note that
We claim that each MMP-step as in Theorem 2 induces either a collapse or an isomorphism of
By [11,Thm.19] we get an elementary collapse (or an isomorphism) if there is a divisor
It remains to deal with the case when we contract
Dlt modifications of algebraic spaces. By [25], a normal, quasi-projective pair
However, dlt modifications are rarely unique, thus it was not obvious that they exist when the base is not quasi-projective. [33] observed that Theorem 2 gives enough uniqueness to allow for gluing. This is not hard when
Theorem 18 (Villalobos-Paz). Let
(1)
(2)
(3)
(4)
(5) either
I thank E. Arvidsson, F. Bernasconi, J. Carvajal-Rojas, J. Lacini, A. Stäbler, D. Villalobos-Paz, C. Xu for helpful comments and J. Witaszek for numerous e-mails about flips.
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