Application of spatio-temporal filtering to fetal electrocardiogram enhancement

Estimation of PQ distance dispersion for atrial fibrillation detection

BACKGROUND AND OBJECTIVE

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. It is associated with significantly increased morbidity and mortality. Diagnosis of the disease can be based on the analysis of the electrical atrial activity, on quantification of the heart rate irregularity or on a mixture of the both approaches. Since the amplitude of the atrial waves is small, their analysis can lead to false results. On the other hand, the heart rate based analysis usually leads to many unnecessary warnings. Therefore, our goal is to develop a new method for effective AF detection based on the analysis of the electrical atrial waves.

METHODS

The proposed method employs the fact that there is a lack of repeatable P waves preceding QRS complexes during AF. We apply the operation of spatio-temporal filtering (STF) to magnify and detect the prominent spatio-temporal patterns (STP) within the P waves in multi-channel ECG recordings. Later we measure their distances (PQ) to the succeeding QRS complexes, and we estimate dispersion of the obtained PQ series. For signals with normal sinus rhythm, this dispersion is usually very low, and contrary, for AF it is much raised. This allows for effective discrimination of this cardiologic disorder.

RESULTS

Tested on an ECG database consisting of AF cases, normal rhythm cases and cases with normal rhythm restored by the use of cardioversion, the method proposed allowed for AF detection with the accuracy of 98.75% on the basis of both 8-channel and 2-channel signals of 12 s length. When the signals length was decreased to 6 s, the accuracy varied in the range of 95%-97.5% depending on the number of channels and the dispersion measure applied.

CONCLUSIONS

Our approach allows for high accuracy of atrial fibrillation detection using the analysis of electrical atrial activity. The method can be applied to an early detection of the desease and can advantageously be used to decrease the number of false warnings in systems based on the analysis of the heart rate.

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.

Extraction of foetal ECG from abdominal ECG by nonlinear transformation and estimations

BACKGROUND AND OBJECTIVE

This paper proposes a simple yet effective method for the extraction of foetal ECG from abdominal ECG which is necessary due to similar spatial and temporal content of mother and foetal ECG.

METHODS

The proposed algorithm for extraction of foetal ECG (fECG) from abdominal signal uses single channel. Pre-processing of abdominal ECG (abdECG) has been done to eliminate noise and condition the signal. The maternal ECG R-peaks have been detected based on thresholding, first order Gaussian differentiation and zero cross detection on pre-processed signal. Having identified R-peaks and pre-processed signal as base, using Maximum Likelihood Estimation, one beat including QRS complex morphology of maternal ECG (mECG) has been constructed. Extraction of maternal ECG from abdECG is done based on the constructed beat, R-peak locations and its corresponding QRS complex of abdECG. Extracted mECG has been cancelled from abdECG. This results in foetal ECG with residual noise. The noise has been reduced by Polynomial Approximation and Total Variation (PATV) to improve SNR. This approach ensures no loss of partially or completely overlapped fECG signals due to mECG removal. The algorithm is tested on three database namely daISy (DB_I), Physiobank challenge 2013 (DB_II) and abdominal and direct foetal ECG database (adfecgdb) of Physiobank (DB_III).

RESULTS

The algorithm detected no false positives or false negatives with certain channel for DB_I, DB_II and DB_III which shows that the proposed algorithm can achieve good performance. Overall accuracy and sensitivity of the system is 98.53% and 100% for DB_I. Best accuracy and sensitivity of 97.77% and 98.63% are obtained for DB_II. Best accuracy of 92.41% and sensitivity of 93.8% are obtained for DB_III. Correlation coefficient between actual foetal heart rate (fHR) and estimated fHR of 0.66 for DB_II and 0.59 for DB_III is obtained. The method has obtained overall F1 score of 99.25% for DB_I, 96.04% for DB_II and 94.25% for DB_III. It has obtained a best MSE of fHR and overall MSE of R-R interval which is 10.8bpm² and 2.2 ms for DB_II, 12bpm² and 2.14 ms for DB_III.

CONCLUSION

The results for different public databases show that the proposed method is capable of providing good results. The foetal QRS, R-peaks and R-R intervals have also been obtained in this method. Thus, it gives a significant contribution in the required area of research.

Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal

Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.

Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.

A novel method to estimate safety factor of capture by a fetal micropacemaker

We have developed a rechargeable fetal micropacemaker in order to treat severe fetal bradycardia with comorbid hydrops fetalis, a life-threatening condition in pre-term non-viable fetuses for which there are no effective treatment options. The small size and minimally invasive form factor of our design limit the volume available for circuitry and a power source. The device employs a fixed-rate and fixed-amplitude relaxation oscillator and a tiny, rechargeable lithium ion power cell. For both research and clinical applications, it is valuable to monitor the electrode-myocardium interface in order to determine that adequate pacemaker output is being provided. This is typically accomplished by observing the minimal stimulus strength that achieves threshold for pacing capture. The output of our simple micropacemaker cannot be programmatically altered to determine this minimal capture threshold, but a safety factor can be inferred by determining the refractory period for ventricular capture at a given stimulus strength. This is done by measuring the minimal timing between naturally occurring QRS complexes and pacing stimuli that successfully generate a premature ventricular contraction. The method was tested in a pilot study in four fetal sheep and the data demonstrate that a relative measure of threshold is obtainable. This method provides valuable real-time information about the electrode-tissue interface.

Monitoring the fetal heart rate variability during labor

In respect to the main goal of our ongoing work for estimating the heart rate variability (HRV) from fetal electrocardiogram (FECG) signals for monitoring the health of the fetus, we investigate in this paper the possibility of extracting the fetal heart rate variability (HRV) directly from the abdominal composite recordings. Our proposed approach is based on a combination of two techniques: Periodic Component Analysis (PiCA) and recursive least square (RLS) adaptive filtering. The Fetal HRV of the estimated FECG signal is compared to a reference value extracted from an FECG signal recorded by using a spiral electrode attached directly to the fetal scalp. The results obtained show that the fetal HRV can be directly evaluated from the abdominal composite recordings without the need of recording an external reference signal.

Fetal beat detection in abdominal ECG recordings: global and time adaptive approaches

We present a method for location of fetal QRS in maternal abdominal ECG recordings. This method's initial, global approach was proposed in the context of the 2013 PhysioNet/Computing in Cardiology Challenge where it was tested on the 447 four channel one-minute recordings.The first step is filtering to eliminate baseline wander and high frequency noise. Upon detection, maternal QRS is removed on each channel using a filter applied to the other three channels. Next we locate fetal QRS on each channel and select the channel with the best set of detections. The method was awarded the third-best score in the Challenge event 1 with 278.755 (beats/minute) and the fourth-best score on event 2 with 28.201 ms.The 5 min long recordings of the Abdominal and Direct Fetal ECG Database were used to further test the method. This database contains five recordings obtained from women in labor. Results in these longer recordings were not satisfactory. This appears to be particularly the case in recordings with a more clearly non-stationary nature. In a new approach to our method, some changes are introduced. Two features are updated over time: the filter used to eliminate maternal QRS and the channel used to detect fetal beats. These changes significantly improved the QRS detection performance on longer recordings, but the scores on the 1 minute Challenge recordings were degraded.