Real-time signal processing for fetal heart rate monitoring

An algorithm based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring. A microcontroller-based system has been used to implement the algorithm in real-time. A Doppler ultrasound fetal monitor was used for statistical comparison on five volunteers with low risk pregnancies, between 35 and 40 weeks of gestation. Results showed an average percent root mean square difference of 5.32% and linear correlation coefficient from 0.84 to 0.93. The fetal heart rate curves remained inside a 5-beats-per-minute limit relative to the reference ultrasound method for 84.1% of the tim

Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus

Long-term fetal heart rate (FHR) monitoring is necessary to ensure that any FHR abnormality, which may appear at any time during pregnancy and labor, can be detected. An ambulatory electrocardiogram (ECG) recorder employing three abdominal surface electrodes has been developed towards achieving such a monitoring. The difficulties encountered in determining the FHR from the maternal abdominal signal are mainly the interference due to the electromyogram and motion artifact, and relatively small amplitude of the fetal ECG compared to that of the maternal. Thus improvement to existing abdominal signal processing algorithm is necessary to increase the percentage of successful monitoring. A real-time algorithm has been developed for the simultaneous measurement of the fetal and maternal heart rates from the abdominal signal. The algorithm is based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima. A filtering technique has been utilized in the proposed algorithm to extract the fetal signal from the maternal abdominal signal. This is an alternative to a previous method which subtracts the maternal complexes from the abdominal signal with a need to overcome the problem of matching a template to the complexes. The proposed algorithm is capable of continuous ambulatory FHR monitoring either off-line, by using recorded signals, or on-line by a clinician during antenatal examination. The performance of the algorithm has been evaluated of the heart rates tracing processed from the abdominal signals. The resulting average accuracy is 83% for the FHR detection. The detection of the FHR from the maternal abdominal signal by the developed algorithm has also been compared with a short-term monitoring commercial instrument IFM-500 for the assessment of the reliability of the algorithm. The performance achieved from the comparison shows non-significant differences of means, low error percentages and linear correlation coefficient. A portable system based on the developed algorithm has the potential for increased percentage of real-time FHR detection thus enabling successful long-term fetal monitoring

Time-scale analysis of antepartum fetal heart rate variability

Reliable evaluation of fetal condition and early detection of fetal distress is one of the largest challenges in modern obstetrics. Safely protected within the maternal womb, the fetus is rather inaccessible for physiological measurements. One of few physiological phenomena that can be measured antenatal, is fetal heart activity. The heart plays an essential role in the transportation of oxygen to the tissues, but is only one of multiple factors that influence oxygen supply. Consequently, fetal heart activity provides direct, but rather limited information for the evaluation of fetal condition. Cardiotocography, the simultaneous recording of fetal heart rate and uterine activity, has been the standard in fetal monitoring for more than 30 years. Nevertheless, cardiotocography is insufficiently capable of predicting bad fetal outcome and therefore its value in clinical practice is limited. As a result, any additional information that can contribute to reliably evaluating fetal condition, is highly appreciated. The beat-to-beat variability of the fetal heart rate is an expression of cardiovascular control by the autonomic part of the fetal central nervous system. As this cardiovascular control will respond to changes in fetal condition, fetal heart rate variability will indirectly reflect fetal condition. Fetal heart rate activity therefore contains potentially useful information that cardiotocography does not reveal. As modulation by different parts of the autonomic nervous system occurs on characteristic timescales, timefrequency analysis of fetal heart rate variability might provide additional information that can be used to more reliably assess fetal well-being. However, interpretation of this information is complicated by the complexity of the physiological mechanisms for cardiovascular control. Additionally, to obtain accurate spectral information, the beat-to-beat fetal heart rate is required, which can only be obtained in clinical practice by measuring the fetal electrocardiogram directly from the fetal scalp. This currently limits the application of the method to intrapartum measurements. To further explore the potential of time-frequency analysis of fetal heart rate variability for monitoring fetal condition, application of the analysis technique to antepartum measurements is highly appreciated. The first goal of this doctoral dissertation therefore is to: 1. Obtain the beat-to-beat fetal heart rate throughout pregnancy Given the limited successes in literature, it is expected that the obtained fetal heart rate will contain considerably more artifacts than the fetal heart rate obtained from scalp ECG measurements during labor does. Standard techniques for time-frequency analysis, such as the fast Fourier transform, will then fail to provide accurate spectral information. The second goal of this dissertation therefore is to: 2. Obtain accurate spectral information on antepartum fetal heart rate variability To measure fetal heart activity antepartum, a dedicated data-acquisition system has been developed for electrophysiological measurements on the abdomen of a pregnant woman (chapter 2). A novel method developed by a coworker was chosen to remove the dominating maternal electrocardiogram from the recorded signals. An online software implementation of this method has been realized to process the recorded signals real-time. To achieve the first goal of the dissertation, chapter 3 presents an algorithm that uses a priori knowledge on the physiology of the fetal heart to enhance the fetal ECG components in multi-lead abdominal fetal ECG recordings, before QRS-detection. Evaluation of the method on generated fetal ECG recordings with controlled signal-to-noise ratios showed excellent results. However, for actual recordings, evaluation of the results by experts learned that fine-tuning of the algorithm is necessary. In chapter 4, a more theoretical approach has been used to exploit the spatial correlation of multi-channel fetal ECG recordings for increasing the signal-to-noise ratio of the retrieved fetal electrocardiogram. A threedimensional representation of the fetal vectorcardiogram is constructed by erse Dower matrix. An ellipse is fitted to the QRS loop of several overlayed heartbeats and the axes of the ellipse are calculated to determine the source signals of the fetal electrocardiogram. In future work, this technique could be used for calculating the linear combinations that are used in the algorithm of chapter 3, which will increase the accuracy of the heart rate detection. The suitability of non-invasive fetal ECG recordings for fetal monitoring in clinical practice was evaluated by using the developed technology in a longitudinal patient study (chapter 5). Repeated measurements on pregnant patients learned that the performance of the method for removing the maternal electrocardiogram was good and remained more or less constant throughout pregnancy. Between 20 and 25 weeks of gestational age, the quality of the retrieved fetal ECG waveforms generally was very high, and the beat-to-beat fetal heart rate could be accurately detected. For this stage of pregnancy, abdominal measurement of the fetal electrocardiogram offers an opportunity to obtain unique cardiac information on the fetus. However, to increase the performance of the technology throughout pregnancy, the noise in the electrophysiological recordings must be significantly reduced. Still, it remains uncertain whether this will be adequate when isolating sections of the vernix caseosa reduce the amplitude of the fetal electrocardiogram that is measured on the maternal abdomen. For stages of pregnancy in which abdominal recording of the fetal electrocardiogram fails to provide the beat-to-beat heart rate, chapter 6 offers an alternative. By processing Doppler waveforms of ultrasound signals reflected at the fetal heart, the beat-to-beat fetal heart rate can be obtained. However, the measurement requires a skilled operator and is very sensitive to fetal movement. The presence of artifacts in the beat-to-beat fetal heart rate obtained from either abdominal recordings of the fetal electrocardiogram or Doppler ultrasound recordings, is common and cannot be prevented. To obtain the second goal of the dissertation, a continuous wavelet based analysis method has been developed to reliably calculate the power within the scales of interest (chapter 7). This method provides accurate results when up to 20 % of the dataset is missing due to artifacts. In chapter 8, the continuous wavelet based method has been applied for time-scale analysis of the recordings from chapter 5. The results of this analysis correspond with literature on the development of the fetal autonomic nervous system. In addition, the results suggest that functional development of the sympathetic nervous system takes place around 22 weeks of gestational age. The final chapter reflects on the realization of the goals of this dissertation and provides specific directions for future work. Although additional clinical research might contribute to obtaining clinically relevant information from time-scale analysis of fetal heart rate variability, focus should be on solving the technical limitations of the used instrumentation for abdominal recording of the fetal electrocardiogram

Hybrid methods based on empirical mode decomposition for non-invasive fetal heart rate monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy(ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.Web of Science8512185120

Signal processing methodologies for an acoustic fetal heart rate monitor

Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use

Mathematical tools for identifying the fetal response to physical exercise during pregnancy

In the applied mathematics literature there exist a significant number of tools that can reveal the interaction between mother and fetus during rest and also during and after exercise. These tools are based on techniques from a number of areas such as signal processing, time series analysis, neural networks, heart rate variability as well as dynamical systems and chaos. We will briefly review here some of these methods, concentrating on a method of extracting the fetal heart rate from the mixed maternal-fetal heart rate signal, that is based on phase space reconstructio

Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection

There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen