Journal of Life Sciences and Technologies Vol. 1, No. 3, September 2013
Classification System for Fetal Heart Rate Variability Measures Based on Cardiotocographies João A. L. Marques University Lusiada of Angola/Department of Informatics, Lobito, Angola University of Leicester/Department of Engineering, Leicester, United Kingdom Email:
[email protected]
Paulo C. Cortez and João P. V. Madeiro Federal University of Ceará/Department of Engineering of Teleinformatics, Fortaleza, Brazil Email: {cortez, joaopaulo}@deti.ufc.br
Fernando S. Schlindwein University of Leicester/Department of Engineering, Leicester, United Kingdom Email:
[email protected]
Abstract—The Fetal Heart Rate interpretation based on Cardiotocographies (CTG) is the most common practice of obstetrician medical staffs. Computerized CTG Systems are used with the aim to reduce subjective aspects of these diagnostics. The Fetal Heart Rate Variability (FHRV) analysis using the CTG signal is an unusual approach. This work proposes a FHRV analysis based on the evaluation of time domain parameters (statistic measures); frequency domain parameters; and the short and long term variability obtained from the Poincaré plot. A normal distribution is presumed for each parameter and a normality criterion is proposed. Specific and overall classifications are proposed to help improve the fetal conditions interpretation, expanding the conventional FHR analysis.
Index Terms—fetal heart rate cardiotocography (CTG), diagnostic
I.
variability
(FHRV),
INTRODUCTION
The cardiologic and autonomic nervous systems (ANS) continuously look for a dynamic balance where the parasympathetic and sympathetic systems act as opposite forces influencing the heart rhythm modulation. The first one increases the heart rate and decreases the variability while the second system does the opposite action [1]. The Fetal Heart Rate Variability (FHRV) can be obtained by the Cardiotocography (CTG), as it is considered a gold standard exam for the detection of fetal heart rate (FHR). The Doppler sensor has similar accuracy when compared with the abdominal ECG for the fetal heart beat detection [2]. The CTG records continuously and simultaneously the FHR and the uterine tonus (for uterine contractions monitoring). Fetal movements can also be recorded Manuscript received June 3, 2013; revised August 25, 2013. 2013 Engineering and Technology Publishing doi: 10.12720/jolst.1.3.184-189
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manually by the mother. These monitoring allow the detection of a large set of diseases or changes in the fetal health status [3]. Usually, the CTG exam is done in risky pregnancies because fetal distress can be earlier detected. Depending on the situation, the exam is applied before labour, period of time named as antepartum, and also during labour, the intrapartum period [4]. Previous works are using the FHVR analysis acquiring the fetal ECG Signal. Lebrun (2003) states that the FHRV analysis during the last trimester can provide important clinical information after birth [5]. A fetal development indice based on time and frequency parameters is suggested based on the FHR decrease and variability increase during the pregnancy. Sibony et al. (1994) present that the FHRV can be used to detect the fetal status also during labour [6]. They propose the identification of new frequency intervals and two evaluation criteria based on the FHRV spectral analysis. Other works consider the frequency domain parameters as part of an overall comparison with other monitored systems to determine fetal status [7]. The time domain HRV analysis considers geometric and statistic approaches [8]. The geometric metrics are based on the histogram of the set of normal intervals between QRS complexes. In statistical analysis there are several metrics divided in three groups. Each metric is defined in Table I. The first one evaluates the heart rate behaviour as a whole, i.e., considers the whole set of samples for its calculations. The metrics are the SDNN and the SDANN. The RMSSD measure considers the interval between heart beats and belongs to the second group and reflects the high frequency characteristics of the signal. Finally, the long term variability (LTV) and short term variability (STV) are obtained from the Poincaréplot.
Journal of Life Sciences and Technologies Vol. 1, No. 3, September 2013
GmBH, in Munich, Germany. The database is identified as CTG-A, and has 80 examinations in antepartum period of time, i.e., before labour, with gestational age varying from the 28th to the 34th week. From these, 58 examinations are classified as control (normal fetal and low level of suspicious status) and 22 as study (high level of suspicious or pathological). There are no uterine contractions and the occurrence of FHR accelerations may indicate normality.
The parasympathetic stimulation results in a fast and short term answer in the heart beats, affecting immediately the interval between them. It can be evaluated when considering parameters such as RMSSD. The sympathetic stimulation is slower with a latency period that can vary from 5 to 20 seconds [1]. Parameters considering many RR intervals, such as SDNN and SDANN are used to evaluate both systems as a whole. TABLE I. Measure Unit
HRV MEASURES CONSIDERED AS CLASSIFICATION CRITERIA
B. FHRV Analysis A block diagram with all the steps to perform the FHR variability analysis is presented in Fig. 1. After the time and frequency domain parameters are determined, the long and short term variability can be obtained from the Poincaréplot.
Description
SDNN
ms
Standard deviation considering all NN intervals
SDANN
ms
Mean of all standard deviations of 5-minutes segments of NN.
RMSSD
ms
The square root of the mean squared difference of successive NNs
LF
ms2
Low frequency power (0,04 – 0,15 Hz)
HF
ms2
High frequency Power (0,15 – 0,40 Hz)
LF/HF
--
LF and HF high frequency ratio
The frequency domain parameters usually considered in heart rate variability analysis are divided into frequency intervals, such as Ultra Low Frequency (ULF), Very Low Frequency (VLF), Low Frequency (LF) and High Frequency (HF) [1]. In this work we consider only the LF and HF intervals. The HF component corresponds to changes in the heart rate related with the respiratory cycles, which are tipically managed by the parasympathetic system. On the other hand, the LF component is influenced by both systems. Actually, the HRV in time and frequency domain are different expressions of the same phenomenon, some correlation among those parameters can be demonstrated. The SDNN parameter for example is related to the total power of the spectral analysis. The time domain RMSSD is correlated with the high frequency component in the frequency domain since it considers the difference between two RR adjacent intervals, quantifying fast changes of the heart rate. Another example is the correlation between the SDANN and the ULF frequency band. This work presents a FHRV analysis based on the signal obtained by the CTG examination. Classification criteria for the FHRV parameters and for the examination as a whole are proposed.
Figure 1. The block diagram of FHRV analysis
After calculating several different parameters, this work considers the subset defined in Table I. The frequency ranges considered in this work are also presented. The LF and HF are expressed in normalized units. The LF/HF ratio is considered in all the results because it shows the balance between the sympathetic and parasympathetic systems. C. Classification Criteria There are no previously determined normality criteria for the FHRV. This work presents a classifier based on a set of criteria. Considering a normal statistical distribution for each of the chosen parameters (Pi), the following criteria are considered in this analysis: Normality: µPi - σPi ≥ Pi ≤ µPi + σPi Suspicious: µPi + σPi < Pi ≤ µPi + 2(σPi) µPi - 2(σPi) ≤ Pi < µPi - σPi Abnormality: Pi > µPi + 2(σPi) Pi < µPi - 2(σPi) where µPi is the mean and σPi is the standard deviation for each parameter Pi. For the overall classification of the exam, four different possibilities are considered: If all parameters are classified as normal, then the exam is considered as “FRHV Normality”.
II. MATERIALS AND METHODS A. Database and Development Environment The Matlab software version 7.6.0.324 R2008a is used as the development environment [9]. The whole set of HRV parameters (time and frequency domain) and the STV and LTV based on Poincaré plot were calculated using the system proposed by Madeiro [10]. The results were obtained from one previously identified database from the Trium Analysis Online 2013 Engineering and Technology Publishing
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If one parameter is suspicious then the exam is labeled as “Attention”. If two or more are suspicious, then the label is “Suspicious”. If any parameter is classified as abnormal, the exam is classified as “FRHV Abnormality”. It is important to notice that these criteria are not comparable with the conventional classification and is proposed to act as a complementary tool. The conventional analysis can find pathologies where the FHR does not and vice versa.
to the normality classification contain the most part of the exams, while only three were considered abnormal.
III. RESULTS AND DISCUSSION
Figure 2. LF/HF classification
In this section, the results for all of the parameters is presented, such as for the time domain or the frequency domain. The mean µ, the standard deviation σ, the maximum value Max, the minimum value Min and the Pearson variability are presented in Table II and Table III. TABLE II. HRV MEASURES CONSIDERED AS CLASSIFICATION CRITERIA Measure
SDNN
SDANN
RMSSD
µ
31.80
22.13
3.36
σ
10.81
8.67
0.96
Max
81.18
53.61
8.25
Min
15.07
5.05
1.93
Pearson Variability
34.01
39.20
28.71
TABLE III. HRV MEASURES CONSIDERED AS CLASSIFICATION CRITERIA Measure
SDNN
SDANN
RMSSD
µ
31.80
22.13
3.36
σ
10.81
8.67
0.96
Max
81.18
53.61
8.25
Min
15.07
5.05
1.93
Pearson Variability
34.01
39.20
28.71
Figure 3. Varaiability classifications: (a) LTV and (b) STV
The scatter plot for the variability parameters, LTV and STV, are presented in Fig. 3 (a) and Fig. 3 (b). There are two significant outliers in both plots. These exams must be carefully analysed as they may indicate fetal distress. Finally, the time domain statistics are presented in Fig. 4 (a), (b) and (c). As also shown in other previous graphics, there are only a few abnormal and a significant number of suspicious classification. Besides, there are not abnormal exams under the lower suspicious values. After this classification based on each parameter, the overall analysis of each examination is performed. There are four different outputs and the “Suspicious” was the most common classification with 35% of the occurrences while 21% were classified with the “Attention” label. This means that 56% of the whole examinations set had at least one FRHV parameter out of the normality classification criteria. There were 30% of “Normal” and 14% of “Abnormal” outputs. These results are presented in Fig. 5. A group of four exams previously classified as normal are presented in Table IV. All of them are also classified
The Pearson Variability indicates that in this database there are significant variations in almost the whole set of parameters, especially when analysing the time domain parameters. This indice is also high for the ratio LF/HF. For a better comprehension of how the parameters are related to each other the correlation coefficient ρ are also calculated. For the long and short term variability parameters, STV and LTV, is found ρ = 0.5739, showing a strong correlation between them. For the SDNN and SDANN parameters there is a stronger correlation, with ρ = 0.6984. For the RMSSD parameter, which is by definition, related with high frequency components, there is a correlation with the HF parameter, ρ = 0.4706. After these preliminary results, the detailed classification process for each of the parameters is then performed, assuming that all of them follow a normal distribution. In Fig. 2, the LF/HF values are plotted classified as normal, suspicious and abnormal. The results according 2013 Engineering and Technology Publishing
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as normal for the proposed classification using the FHRV parameters. For these exams, there is a match between the visual analysis and the FHRV analysis, considering the normality classification proposed in this work. In a general point of view, there are 17 exams among 58 with this match of classification. Also, eight exams previously classified as normal received the “Attention” label.
In Table V, a set of three exams previously classified as control, i.e., pathological or suspicious. For these exams, at least two parameters are in the suspicious classification based on the proposed (µ ± σ) analysis. The third exam, for example, has STV, RMSSD and LF/HF out of the proposed normality interval. All the three exams classified as abnormal according to the FHRV analysis and also were classified as pathological by the conventional CTG analysis: ctg20011218_2348371; ctg20001213_0948395 and ctg20000709_043356. TABLE IV. STATISTICAL AND FREQUENCY DOMAIN PARAMETERS FOR NORMAL EXAMINATIONS Exam
STV
HF (n.u.)
LF/HF SDNN
ctg200003040409053
0.81
13.72
6.28
21.68
2.95
ctg200002090834583
0.90
10.62
8.41
26.69
2.81
ctg200002020408315
0.98
11.94
7.37
31.86
3.41
ctg200002281258193
1.09
10.87
8.19
30.73
3.64
ctg200003290541413
0.85
13.15
6.60
22.57
2.80
RMSSD
TABLE V. STATISTICAL AND FREQUENCY DOMAIN PARAMETERS FOR SUSPICIOUS OR PATHOLOGICAL EXAMINATIONS
Overall Classification 30% 21%
Abnormal Suspicious
35%
Attention Normal
Figure 5. Overall classification
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STV
HF LF/HF SDNN (n.u.)
RMSSD
ctg20000729-2151501
1.22
13.31
3.94
28.40
3.42
ctg20011204-0845235
0.78
9.51
9.50
21.72
2.18
ctg20000630_0916173
0.66
8.49
5.74
60.55
2.09
Nevertheless, the FHRV the results presents that those parameters must not be used as the unique analysis of the fetal state. Other exams show a divergence if you compare the two classification system. For example, the exams ctg20000521-1402455, ctg20000203-1942093 and ctg20000518-2034363 belongs to the proposed normality classification but were previously classified as suspicious or pathological. In all these cases, if the FHRV was considered alone, fetal health problems could not be detected. On the other hand, the FHRV analysis may expands the conventional analysis. The ctg20010223-1429403 exam is previously classified as normal in the conventional analysis. Although, this exam presents the lowest short term variability, STV=0.61, and the lowest RMSSD, 1.93. The LTV=10.69 and HF=8.49 are considered low values. This may indicate low variability and a very small contribution of the high frequency components, related to the parasympathetic system. According to this analysis, the exam could be classified as suspicious or pathological. Another example is the ctg20010626-2358115 exam, also classified as normal before. It presents the highest SDNN value, 81.18 and high values for the HF, 22.09
Figure 4. Time domain classifications: (a) SDNN, (b) SDANN and (c) RMSSD
14%
Exam
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[10] J. P. do V. Madeiro, “Sistema automático para análise de variabilidade da freqüência cardí aca,” Master´s Thesis, Federal University Federal of Ceará, 2007.
and the RMSSD, 4.70. This may indicate a suspicious fetal health status, with a strong influence of the parasympathetic system over the desired balance. IV. CONCLUSIONS
J. A. L. Marques was born in Fortaleza-CE, Brazil, in 1973. Dr. Marques graduated in Electrical Engineering at the Federal University of Ceará (UFC) in 1996. He then concluded his Master’s studies at UFC in 2006 and his doctoral studies at UFC in 2010 (with an internship at Trium Analysis Online GmBH, in Munich, Germany - 2007). He concluded postdoctoral studies at the University of Leicester, UK in 2012. He is currently with the Department of Informatics, University Lusíada of Angola, in Lobito, Angola, where he heads the research group of Health Informatics and is the Director of the Research, Studies and Post-Graduation Center. His current research is focused on biological time series analysis (such as heart, brain and others) using digital signal processing techniques based on linear and nonlinear approaches. He is also heading a research project: the “NeuroSapiens Project”, a joint research with the University of Cape Town, South Africa, a biological signal monitoring and analysis system for cognitive and affective neuroscience studies in Angola with postcivil war students and families.
The interval between heart beats can be considered an important parameter for the detection of the fetal health status. The FHRV analysis based on the CTG examination is a viable approach for the obstetric practice, since the visual analysis is very subjective. For computerized CTG systems this analysis can be applied as a second level and complimentary detector of fetal distress. The results presented require that the FHRV analysis must not be considered as the only monitored parameters. The conventional analysis is strictly necessary. Future works could consider other statistical indices, such as percentiles or quartiles for the classification criteria and also other statistical and geometrical measures can be considered, to improve the proposed analysis.
P. C. Cortez was graduated in Electrical Engineering at the Federal University of Ceará (UFC) in 1982. He then concluded his Master’s and Doctoral studies at UFC in 1992 and 1996 respectivelly at Federal University of Paraiba – Campina Grande. His is an Associate Professor Level III from the Department of Teleinformatics Engineering at UFC. His research is focused on Artificial Vision, primarily working with 2-D and 3-D contours poligonal modeling, pattern recognition, digital imaging segmentation digital signal processing, biomedical images, computeraided intelligent systems for biomedical signal analysis, telemedicine applications and embedded systems.
ACKNOWLEDGMENT The authors thank the Trium Analysis Online GmBH in Munich, CNPq (Brazil), Funcap/FINEP (Brazil) for funding PAPPE Project and the Centre of Bioengineering at University of Leicester, Leicester, UK. REFERENCES [1]
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Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability-standards of measurement, physiological interpretation, and clinical use, European Heart Jounal, vol. 17, pp. 354-381, 1996. M. Signorini, G. Magenes, S. Cerutti, and D. Arduini, “Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings,” IEEE Transactions on Biomedical Engineering, vol. 50, pp. 365-374, 2003. I. Ingemarsson, E. Ingemarsson, Spencer, A. D. John, Fetal Heart Rate Monitoring–A Practical Guide, New York: Oxford Medical Publications, Oxford University Press, 1993. J. A. L. Marques, P. C. Cortez, and J. P. do V. Madeiro, “Detecção de alterações da frequência cardí aca fetal e do tônus uterino materno em exames cardiotocográficos utilizando transformada de hilbert. porto de galinhas,” Brazilian Conference in Biomedical Engineering, 2008. D. N. Lebrun, Analysis of Neonatal Heart Rate Variability and Cardiac Orienting Responses, Dissertação (Mestrado)-University of Florida, 2003. O. Sibony, J. Fouillot, M. Benaoudia, A. Benhalla, J. Oury, C. Sureau, and P. Blot, “Quantification of fetal heart rate variability by spectral analysis of fetal well-being and fetal distress,” European Journal of Obstretics and Gynecology and Reproductive Biology, vol. 54, pp. 103-108, 1994. M. Ferrario, M. Signorini, G. Magenes, and S. Cerutti, “Comparison of entropy-based regularity estimators: Application to the fetal heart rate signal for the identification of fetal distress,” IEEE Transactions on Biomedical Engineering, vol. 53, pp. 119125, 2006. M. Malik and A. J. Camm, “Components of the heart rate variability-What they really mean and what we really measure,” American Journal of Cardiology, vol. 72, pp. 821-822, 1993. Mathworks. Matlab. Nov. (2011). [Online]. Available: http://www.mathworks.com
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J. P. do V. Madeiro was graduated in Electrical Engineering at the Federal University of Ceará (UFC) in 2006. He then concluded his Master’s and Doctoral studies at UFC in 2007 and 2013 also at the Federal University of Ceara – Department of Teleinformatics Engineering. He also worked at the University of Leicester during his Doctoral studies workins with electrograms during persistent atrial fibrilation. He works at Ministério Público Federal and his research is focused on digital signal processing, computer-aided diagnostic systems, automatic ECG parameter extraction, electrograms and the application of nonlinear techniques for cardiologic signals.
Fernando S. Schlindwein was born in Porto Alegre, Brazil, in 1956. He graduated with a First Class Honours degree as an Electronic Engineer in 1979 from the Federal University of Rio Grande do Sul, Brazil, with an extension degree in Nuclear Engineering. After a short time in industry (Aços Finos Piratini, a steel mill) he obtained an MSc in Biomedical Engineering from the Coordination of Post-Graduation Programmes in Engineering of the Federal University of Rio de Janeiro (COPPE/UFRJ), Brazil in 1982, a PhD in Biomedical Engineering from the Department of Surgery of the University of Leicester, England in 1990, and a DSc in Biomedical Engineering from the Federal University of Rio de Janeiro (UFRJ) in 1992. He was a Senior Lecturer associated with UFRJ from August 1980 until 1992, when he joined the Department of Engineering at the University of Leicester where he is a Reader in Bioengineering. He did his military service at Colégio Militar of Porto Alegre where he was First Cadet in Infantry. He has also been a Senior Lecturer of the
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Department of Electronics of the Brazilian Navy Academy for Officers in Rio de Janeiro, Brazil in the early 1980s. His current research interests are real-time digital signal processing, with more intense research activities in i) cardiac arrhythmias, especially atrial fibrillation;
2013 Engineering and Technology Publishing
ii) heart rate variability and automatic arrhythmia monitoring using the ECG, and iii) microprocessor-, microcomputer- and Digital Signal Processor-based systems.
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