A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Description
2.2. Software Interface
2.3. Signal Preprocessing
- A stable segment is chosen as the starting point; in such a segment, five adjacent samples do not differ by more than 10 bpm, and missing data are excluded when the length of x(i) = 0 is equal to or more than 10 s.
- Values of x(i) ≤ 50 or x(i) ≥ 200 are considered data spikes and are removed using linear interpolation.
- We interpolate x(i) using spline interpolation again when the difference of x(i) and x(i − 1) exceeds 25 bpm, a value used to define an unstable segment.
2.4. Feature Extraction
2.4.1. Morphological
- Set_1: {meanBL, sdBL, minBL, maxBL, ACC, DEC_mild, DEC_prolong, DEC_severe}.
2.4.2. Time Domain
- Set_2: {meanRR, minRR, maxRR, medianRR, SDNN, SDANN, SDNNi, RMSSD, NNx, pNNx, STV, II, LTI, delta, delta_total, Tri, TINN}.
2.4.3. Frequency Domain
- Set_3: {Power_VLF, Power_LF, Power_MF, Power_HF, Power_Total, Percent_VLF, Percent_LF, Percent_MF, Percent_HF, Ratio_Band}.
2.4.4. Nonlinear
- Set_4: {FD_Hig, ApEn, SampEn, LZC, Hurst, alpha, AAC, ADC, APRS, DPRS, SD1, SD2}.
2.5. Feature Selection/Dimensionality Reduction and Classification
2.5.1. Feature Selection/Dimensionality Reduction
2.5.2. Classification and Performance Evaluation
- F-measure (FM, Harmonic mean):
- Balanced error rate (BER):
- Quality index (QI, Geometric mean):
- Matthews correlation coefficient (MCC):
3. Results
- Different feature domains contained different amounts of information regarding the fetal state.
- A combination of several feature domains could improve the performance, and the original feature set (Set_Complete) achieved the best performance.
- The classification capacity of AdaBoost was better than DT and SVM.
- The feature selection algorithms of ST and AUC improved the performance while the dimensionality reduction method of PCA reduced the performance.
- The classification abilities of the three classifiers ranked in the following order: AdaBoost > SVM > DT.
4. Discussion
- Each feature domain reflected physiological information to different degrees: Nonlinear > time domain > frequency domain > morphological.
- The more features we used, the better the performance. A combination of 47 features yielded better performance than other combinations of individual feature subsets.
- The suitable feature selection algorithms (ST and AUC) improved the performance, but the dimensionality reduction approach (PCA) reduced the performance.
- The classification capacity of the ensemble learning algorithm (AdaBoost) was more powerful than common base classifiers (DT and SVM).
- Due to the relationship between the FHR parameters and GA, the GA-based data normalization method achieved better performance than common min-max scaling method.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Information | Mean | Min | Max |
---|---|---|---|
Maternal age (MA, year) | 29.6 | 18 | 46 |
Gestational age (GA, week) | 40.0 | 37 | 43 |
pH | 7.23 | 6.85 | 7.47 |
BDecf (mmol/L) | 4.60 | −3.40 | 26.11 |
pCO2 | 7.07 | 0.70 | 12.30 |
BE | −6.38 | −26.80 | −0.20 |
Apgar 1 min | 8.3 | 1 | 10 |
Apgar 5 min | 9.1 | 4 | 10 |
Gravidity | 1.4 | 1 | 11 |
Parity | 0.4 | 0 | 7 |
Diabetes | No = 515, Yes = 37 | ||
Birth weight (BW, g) | 3401 | 1970 | 4750 |
Infant sex | Male = 286, Female = 266 | ||
Delivery type | Vaginal = 506, Cesarean section = 46 |
Positive | Negative | Evaluation Focus | |
---|---|---|---|
Predicted as positive | TP | FP | / |
Predicted as negative | FN | TN | / |
Acc | The overall efficiency of a classifier | ||
Se (Recall) | The efficiency of a classifier to categorize positively labeled data | ||
Sp | The efficiency of a classifier to categorize negatively labeled data | ||
Precision | The data with positive labels correctly classified by the classifier |
Set | Parameter (Unit) | Normal (447) | Pathological (105) | p |
---|---|---|---|---|
Set_1 | meanBL (bpm) | 136.0 (14.4) | 142.0 (16.2) | 0.004 |
sdBL (bpm) | 2.4 (1.3) | 3.2 (2.6) | 0.318 | |
minBL (bpm) | 132.0 (15.2) | 135.9 (17.2) | 0.008 | |
maxBL (bpm) | 140.7 (14.7) | 148.0 (16.6) | 0.002 | |
ACC | 1.45 (2.24) | 2.24 (3.10) | 0.185 | |
DEC_mild | 1.40 (1.84) | 2.0 (1.85) | 0.098 | |
DEC_prolong | 0.07 (0.30) | 0.17 (0.47) | 0.657 | |
DEC_severe | 0.03 (0.19) | 0.00 (0.00) | 1.000 | |
Set_2 | meanRR (ms) | 447.4 (43.3) | 431.0 (52.2) | 0.005 |
minRR (ms) | 370.4 (31.8) | 349.9 (27.2) | 0.019 | |
maxRR (ms) | 640.5 (158.2) | 654.1 (165.8) | 0.341 | |
medianRR (ms) | 439.3 (40.6) | 418.5 (50.2) | 0.002 | |
SDNN (ms) | 42.6 (27.8) | 51.4 (32.7) | 0.896 | |
SDANN (ms) | 29.7 (24.1) | 38.3 (27.4) | 0.983 | |
SDNNi (ms) | 25.1 (14.5) | 28.9 (18.1) | 0.879 | |
RMSSD (ms) | 10.7 (5.4) | 12.0 (7.3) | 0.912 | |
NNx | 12.5 (16.0) | 17.5 (22.2) | 0.596 | |
pNNx | 1.1 (1.3) | 1.5 (1.9) | 0.596 | |
STV (ms) | 12.1 (8.7) | 14.1 (17.6) | 0.005 | |
II | 0.9 (0.2) | 0.9 (0.2) | 0.079 | |
LTI (ms) | 640.5 (158.2) | 654.1 (165.8) | 0.732 | |
delta (ms) | 82.5 (42.3) | 92.9 (55.2) | 0.746 | |
delta_total (ms) | 270.1 (154.6) | 304.2 (158.3) | 0.927 | |
FHRVTi | 6.2 (2.6) | 6.6 (2.8) | 0.394 | |
TINN | 81.7 (40.6) | 81.9 (37.7) | 0.978 |
Set | Parameter (Unit) | Normal (447) | Pathological (105) | p |
---|---|---|---|---|
Set_3 | Power_VLF (ms2) | 1477 (2747) | 2708 (4734) | 0.757 |
Power_LF (ms2) | 639 (998) | 927 (1464) | 0.359 | |
Power_MF (ms2) | 180 (233) | 201 (300) | 0.527 | |
Power_HF (ms2) | 105 (139) | 120 (173) | 0.536 | |
Power_Total (ms2) | 2401 (3707) | 3956 (5989) | 0.498 | |
Percent_VLF (%) | 86.3 (8.4) | 87.5 (8.3) | 0.513 | |
Percent_LF (%) | 11.1 (7.0) | 10.1 (6.9) | 0.256 | |
Percent_MF (%) | 1.8 (1.2) | 1.8 (1.2) | 0.935 | |
Percent_HF (%) | 0.8 (0.7) | 0.7 (0.5) | 0.382 | |
Ratio_Band | 4.6 (1.7) | 4.4 (1.5) | 0.340 | |
Set_4 | FD_Hig | 1.54 (0.09) | 1.52 (0.11) | 0.005 |
ApEn | 0.41 (0.00) | 0.41 (0.00) | 0.168 | |
SampEn | 2.44 (0.38) | 2.37 (0.35) | 0.102 | |
LZC | 1.13 (0.11) | 1.14 (0.12) | 0.046 | |
Hurst | 0.93 (0.04) | 0.94 (0.03) | 0.099 | |
alpha | 1.32 (0.12) | 1.32 (0.13) | 0.006 | |
ACC (ms) | 8.95 (8.97) | 8.54 (8.74) | 0.603 | |
ADC (ms) | −8.10 (8.88) | −8.35 (8.21) | 0.449 | |
APRS | 2.98 (2.24) | 2.78 (2.56) | 0.774 | |
DPRS | −2.77 (2.29) | −2.45 (2.73) | 0.856 | |
SD1 (ms) | 8.45 (5.56) | 8.76 (5.21) | 0.579 | |
SD2 (ms) | 54.57 (44.59) | 55.13 (46.32) | 0.548 |
Reference (Year) | Database | Distribution (N/P) | Method | Performance (%) |
---|---|---|---|---|
[7] (2011) | Private | Imbalance (30/60) | FE: Empirical mode decomposition C: Support vector machine | Acc = 87 |
[8] (2012) | Private | Imbalance (123/94) | FE: 12 (nonlinear) FS: Information gain C: Support vector machine, naïve Bayes, C4.5 | Se = 73 Sp = 76 FM = 71 |
[54] (2014) | Private | Balance (255/255) | FE: 64 (morphological and linear) FS: genetic algorithm C: Support vector machine | Se = 83 Sp = 66 |
[9] (2015) | Private | Imbalance (30/15) | FE: Ratio and Hurst C: Statistical test (p-value) | AUC = 81 |
[55] (2014) | CTU-UHB | Imbalance (175/377) | FE: 33 (morpholical, linear and nonlinear) C: Latent class analysis+random forest | Se = 72 Sp = 78 |
[56] (2017) | CTU-UHB | Imbalance (508/44) | FE: 42 (morpholical, linear and nonlinear) FE: Relevance in estimating features C: Least square support vector machine | Se = 72 Sp = 65 |
[10] (2018) | CTU-UHB | Imbalance (439/113) | FE: Image-based time-frequency features FS: genetic algorithm C: Least square support vector machine | Se = 63 Sp = 66 |
Current work | CTU-UHB | Imbalance (447/105) | FE: 47 (morpholical, linear and nonlinear) FS: Statistcal test, AUC C: Adaptive boosting | Acc = 92 Se = 92 Sp = 90 AUC = 91 |
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Zhao, Z.; Zhang, Y.; Deng, Y. A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State. J. Clin. Med. 2018, 7, 223. https://doi.org/10.3390/jcm7080223
Zhao Z, Zhang Y, Deng Y. A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State. Journal of Clinical Medicine. 2018; 7(8):223. https://doi.org/10.3390/jcm7080223
Chicago/Turabian StyleZhao, Zhidong, Yang Zhang, and Yanjun Deng. 2018. "A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State" Journal of Clinical Medicine 7, no. 8: 223. https://doi.org/10.3390/jcm7080223