Fuzzy Analysis of Delivery Outcome Attributes for Improving the Automated Fetal State Assessment

Figures & data

Table 1. The class labels of the delivery outcome attributes.

	Attribute
Delivery outcome	AP	BW	PH
Normal	≥ 7	≥ 10	≥ 7.2
Suspicious	[5,6]	(5,10)	(7.1, 7.2)
Abnormal	< 5	≤ 5	< 7.1

Table 2. Parameters of the membership functions that define classes of delivery outcome attributes.

Membership function	Normal			Suspicious			Abnormal
parameter	AP	BW	PH	AP	BW	PH	AP	BW	PH
a	6.00	−10.0	7.13	4.00	3.00	7.03	0.00	0.00	6.50
b	8.00	30.0	7.27	6.00	7.00	7.17	0.00	0.00	6.50
c	10.0	95.0	7.45	6.00	7.00	7.18	3.00	5.00	6.99
d	10.0	95.0	7.45	8.00	13.0	7.22	7.00	5.00	7.21

Figure 1. An example of a fuzzy rule indicating the suspicious delivery outcome. Because only one attribute is within the normal range and there are no attributes related to the abnormal state, the fuzzy rule output defines the suspicious delivery outcome.

Table 3. The set of the analyzed signal features.

bFHR	bpm	the mean value of the FHR baseline (Wrobel et al. 2013)
mFHR	bpm	the fluctuation range of baseline values calculated as a difference between the maximum and minimum values
ACC	1/h	the number of identified acceleration patterns
DEC	1/h	the number of identified deceleration patterns
LOW	%	the duration of the low variability of the FHR signal, expressed as the percentage of the signal length
STV	ms	the short-term FHR variability index
LTV	ms	the long-term FHR variability index
SLR	—	the ratio of STV and LTV
OSC	bpm	the amplitude of FHR oscillation
OSE	%	the percentage of silent FHR oscillation (OSC ≤ 5 bpm) in a whole trace
OSY	%	the percentage of saltatory FHR oscillation (OSC ≥ 25 bpm) in a whole trace
UTE	1/h	the number of detected uterine contractions
MOV	1/h	the number of fetal movements perceived by mother during the entire monitoring
AGE	—	the fetal gestational age during monitoring

Table 4. The efficiency of the LSVM classification for different definitions of the retrospective fetal-state assessment.

	AP		BW		PH		OR
Learning procedure	QI	CC	QI	CC	QI	CC	QI	CC
STDL	55.36	90.80	63.64	88.24	36.73	94.97	65.14	83.39
DTDT	62.42	88.78	67.25	87.03	44.68	95.40	70.39	79.92
FTDT	74.68	69.70	74.59	68.79	69.05	66.98	75.43	73.03
FTFT	81.62	82.47	81.53	82.67	81.12	82.16	81.87	82.65

Table 5. The confusion matrices of the procedures based on the fuzzy training. Mean values of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) for testing subsets are shown.

	FTDT				FTFT
Delivery outcomeattribute	TP	TN	FP	FN	TP	TN	FP	FN
AP	16.22	222.9	100.1	3.780	90.54	192.3	36.30	23.84
BW	22.32	213.6	102.4	4.680	89.66	193.9	34.78	24.66
PH	5.760	224.0	111.0	2.240	91.58	190.2	35.50	25.70
OR	36.42	214.1	82.92	9.580	91.34	192.1	36.24	23.28

Table 6. The specification of the learning procedures leading to the best classification quality for the OR approach. The classifier settings were determined using the parameter characterized by the maximum QI for the 10 first divisions.

	Inference parameters					Classification efficiency [%]
Learning procedure	p^(i)	Δ	k	γ	σ	SE	SP	PPV	NPV
STDL	—	—	—	7000	0.100	48.00	88.87	40.41	91.70
DTDT	−0.50	0.50	0.25	5000	0.090	60.00	83.01	35.63	93.07
FTDT	0.50	0.25	0.25	50.00	0.006	79.17	72.08	30.59	95.75
FTFT	0.25	0.25	0.25	6000	0.001	79.76	84.13	71.72	89.24

Table 7. The fuzzy reasoning parameters, which provide satisfactory learning results regardless of the applied definition of the testing reference assessment.

	Inference parameters			Classification efficiency [%]
Learning procedure	p^(i)	Δ	k	QIm	CCm	QImm	CCmm
DTDT	−0.50	0.50	0.25	60.58	87.79	61.19	87.78
FTDT	0.50	0.25	0.00	73.21	70.18	73.44	69.63
FTFT	0.25	0.25	0.25	81.53	82.46	81.53	82.49

Table 8. The efficiency of the LSVM classification with training based on the reproduction of the abnormal fetal-state patterns, which were characterized by the high value of the fuzzy score (the testing sets remained unchanged).

	Resampling rate [%]
	20		30		40		50
Delivery outcomeattribute	QI	CC	QI	CC	QI	CC	QI	CC
AP	54.46	91.47	60.04	88.69	69.36	79.74	68.98	77.90
BW	62.61	86.58	66.61	85.84	76.12	80.93	77.72	74.71
PH	44.20	91.14	46.15	89.31	49.56	88.52	56.87	71.29
OR	65.24	82.78	67.25	84.90	73.97	78.83	77.06	74.59

Table 9. The results of the statistical evaluation of QI differences between the methods based on the reproduction of the abnormal fetal-state patterns (p < 0.05) and the standard learning. The p-values of differences, which were statistically insignificant, are shown.

	Resampling rate [%]
Delivery outcome attribute	20	30	40	50
AP	0.54	+	+	+
BW	0.22	+	+	+
PH	+	+	+	+
OR	0.84	+	+	+

Table 10. The confusion matrix of the LSVM classifier with the 50% reproduction of the abnormal fetal state patterns in the training set. Mean values of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) for testing subsets are shown.

Delivery outcome attribute	TP	TN	FP	FN
AP	12.18	255.0	68.00	7.820
BW	22.06	234.2	81.80	4.940
PH	3.740	240.8	94.20	4.260
OR	37.24	218.6	78.38	8.760

Wrobel, J., K. Horoba, T. Pander, J. Jezewski, and R. Czabanski. 2013. Improving the fetal heart rate signal interpretation by application of myriad filtering. Biocybernetics and Biomedical Engineering 33:211–221. doi:10.1016/j.bbe.2013.09.004.

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