Mj Michiel Rooijakkers

Low-complexity R-peak detection for ambulatory fetal monitoring

Non-invasive fetal health monitoring during pregnancy is becoming increasingly important because of the increasing number of high-risk pregnancies. Despite recent advances in signal-processing technology, which have enabled fetal monitoring during pregnancy using abdominal electrocardiogram (ECG) recordings, ubiquitous fetal health monitoring is still unfeasible due to the computational complexity of noise-robust solutions. In this paper, an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, without reducing the R-peak detection performance compared to the existing R-peak detection schemes. Validation of the algorithm is performed on three manually annotated datasets. With a detection error rate of 0.23%, 1.32% and 9.42% on the MIT/BIH Arrhythmia and in-house maternal and fetal databases, respectively, the detection rate of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

A Low-Voltage Chopper-Stabilized Amplifier for Fetal ECG Monitoring With a 1.41 Power Efficiency Factor

This paper presents a low-voltage current-reuse chopper-stabilized frontend amplifier for fetal ECG monitoring. The proposed amplifier allows for individual tuning of the noise in each measurement channel, minimizing the total power consumption while satisfying all application requirements. The low-voltage current reuse topology exploits power optimization in both the current and the voltage domain, exploiting multiple supply voltages (0.3, 0.6 and 1.2 V). The power management circuitry providing the different supplies is optimized for high efficiency (peak charge-pump efficiency = 90%).The low-voltage amplifier together with its power management circuitry is implemented in a standard 0.18 μm CMOS process and characterized experimentally. The amplifier core achieves both good noise efficiency factor (NEF=1.74) and power efficiency factor (PEF=1.05). Experiments show that the amplifier core can provide a noise level of 0.34 μVrms in a 0.7 to 182 Hz band, consuming 1.17 μW power. The amplifier together with its power management circuitry consumes 1.56 μW, achieving a PEF of 1.41. The amplifier is also validated with adult ECG and pre-recorded fetal ECG measurements.

Influence of electrode placement on signal quality for ambulatory pregnancy monitoring.

Noninvasive fetal health monitoring during pregnancy has become increasingly important in order to prevent complications, such as fetal hypoxia and preterm labor. With recent advances in signal processing technology using abdominal electrocardiogram (ECG) recordings, ambulatory fetal monitoring throughout pregnancy is now an important step closer to becoming feasible. The large number of electrodes required in current noise-robust solutions, however, leads to high power consumption and reduced patient comfort. In this paper, requirements for reliable fetal monitoring using a minimal number of electrodes are determined based on simulations and measurement results. To this end, a dipole-based model is proposed to simulate different electrode positions based on standard recordings. Results show a significant influence of bipolar lead orientation on maternal and fetal ECG measurement quality, as well as a significant influence of interelectrode distance for all signals of interest.

A continuous wavelet transform-based method for time-frequency analysis of artefact-corrected heart rate variability data

Time-frequency analysis of heart rate variability (HRV) provides relevant clinical information. However, time-frequency analysis is very sensitive to artefacts. Artefacts that are present in heart rate recordings may be corrected, but this reduces the variability in the signal and therefore adversely affects the accuracy of calculated spectral estimates. To overcome this limitation of traditional techniques for time-frequency analysis, a new continuous wavelet transform (CWT)-based method was developed in which parts of the scalogram that have been affected by artefact correction are excluded from power calculations. The method was evaluated by simulating artefact correction on HRV data that were originally free of artefacts. Commonly used spectral HRV parameters were calculated by the developed method and by the short-time Fourier transform (STFT), which was used as a reference. Except for the powers in the very low-frequency and low-frequency (LF) bands, powers calculated by the STFT proved to be extremely sensitive to artefact correction. The CWT-based calculations in the high-frequency and very high-frequency bands corresponded well with their theoretical values. The standard deviations of these powers, however, increase with the number of corrected artefacts which is the result of the non-stationarity of the R-R interval series that were analysed. The powers calculated in the LF band turned out to be slightly sensitive to artefact correction, but the results were acceptable up to 20% artefact correction. Therefore, the CWT-based method provides a valuable alternative for the analysis of HRV data that cannot be guaranteed to be free of artefacts.

A low-power noise scalable instrumentation amplifier for fetal monitoring applications

This paper proposes a low-power noise scalable instrumentation amplifier (IA) for fetal monitoring applications. The noise specification of the IA is made adaptive to the peak-peak value of the fetal electrocardiography (fECG) signal, which varies for different gestational age and measurement settings. Contrary to the currently available point solution IAs, the proposed IA is scalable for a noise range from 30nV/VHz to 250nV/VHz while consuming 15μW to 1μW respectively. A new IA architecture is proposed to achieve a better noise efficiency factor (NEF), while allowing noise scalability. The IA is designed in TSMC 0.18μm CMOS process. Simulation results show that the IA achieves a NEF of 3.4 to 5.5 over the noise scalable range, a CMRR of 100dB, and an input impedance (Zin) of 1GO.

Low-complexity R-peak detection in ECG signals: A preliminary step towards ambulatory fetal monitoring

Non-invasive fetal health monitoring during pregnancy has become increasingly important. Recent advances in signal processing technology have enabled fetal monitoring during pregnancy, using abdominal ECG recordings. Ubiquitous ambulatory monitoring for continuous fetal health measurement is however still unfeasible due to the computational complexity of noise robust solutions. In this paper an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, while increasing the R-peak detection quality compared to existing R-peak detection schemes. Validation of the algorithm is performed on two manually annotated datasets, the MIT/BIH Arrhythmia database and an in-house abdominal database. Both R-peak detection quality and computational complexity are compared to state-of-the-art algorithms as described in the literature. With a detection error rate of 0.22% and 0.12% on the MIT/BIH Arrhythmia and in-house databases, respectively, the quality of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

Decreasing the false alarm rate of arrhythmias in intensive care using a machine learning approach

We present a novel algorithm for classifying true and false alarms of five life-threatening arrhythmias in intensive care. This algorithm was entered in the PhysioNet/Computing in Cardiology Challenge 2015 Reducing False Arrhythmia Alarms in the ICU. The algorithm performs a binary classification of the alarms for a specified arrhythmia type by combining signal quality information and physiological features from multiple sources, such as electrocardiogram (ECG), photoplethysmogram (PPG), and arterial blood pressure (ABP). Signals were selected for feature computation by first assessing the quality for available signals. Random Forest classifiers were trained separately for every type of arrhythmia with arrhythmia-specific features. Hence, the complete algorithm leverages five different predictive models. Classification sensitivities of true alarms 75-99 % (average 93 %) on the training set with cross-validation and 22-100 %(average 90 %) on the unrevealed test set. Classification specificities on the training and test set were 76-94% (average 80%) and 75-100% (average 82%), respectively. The best performance was for extreme bradycardia, whereas the poorest results were for ventricular arrhythmias. The results are for the real-time category when only information prior to the alarm is considered. The final challenge score was 75.54.

Feasibility Study of a New Method for Low-Complexity Fetal Movement Detection From Abdominal ECG Recordings.

Fetal movement counting can provide valuable information on the fetal health, as a strong decrease in the number of movements can be seen as a precursor to fetal death. Typically, assessment of fetal health by fetal movement counting relies on the maternal perception of fetal activity. The percentage of detected movements is strongly subject dependent and with undivided attention of the mother varies between 37% and 88%. Various methods to assist in fetal movement detection exist based on a wide spectrum of measurement techniques. However, these are unsuitable for ambulatory or long-term observation. In this paper, a novel low-complexity method for fetal movement detection is presented based on amplitude and shape changes in the abdominally recorded fetal ECG. This method was compared to a state-of-the-art method from the literature. Using ultrasound-based movement annotations as ground truth, the presented method outperforms the state-of-the-art abdominal-ECG based method, with a sensitivity, specificity, and accuracy of 56%, 68%, and 63%, respectively. Additionally, a significant reduction in algorithm complexity is achieved, possibly enabling continuous ambulatory fetal movement detection and early detection of reduced fetal motility.

A multiple-channel frontend system with current reuse for fetal monitoring applications

This paper proposes a multiple-channel frontend system with current reuse for fetal monitoring applications. The structure and specifications of the proposed frontend system are determined while taking into consideration the algorithms used for fetal electrocardiogram (fECG) detection. Two amplifier topologies based on a middle rail current source/sink (MCS) are proposed for fECG and electrohysterogram (EHG) recording. The proposed amplifiers explore power optimization in both current and voltage domain and thus achieve a better effective noise efficiency factor (NEF) while providing multiple-channels. The frontend system is designed in a 0.18μm CMOS process. Simulation results show that the frontend system provides 3 fECG and 4 EHG recoding channels with a total power consumption of 3.1μW. The IA for fECG monitoring achieves an equivalent NEF of 1.17/1.21 for low noise and low power settings respectively.

A Noise Reconfigurable Current-Reuse Resistive Feedback Amplifier With Signal-Dependent Power Consumption for Fetal ECG Monitoring

This paper presents a noise-reconfigurable resistive feedback amplifier with current-reuse technique for fetal ECG monitoring. The proposed amplifier allows for both tuning of the noise level and changing the power consumption according to the signal properties, minimizing the total power consumption while satisfying all application requirements. The amplifier together with its amplitude detector and dynamical biasing circuit is implemented in a standard 0.18-μm CMOS process. Measurements demonstrate that the proposed current-reuse resistive feedback topology improves the power efficiency of the conventional resistive feedback amplifier, achieving at the same time a good noise efficiency factor (NEF = 2.8) and an input impedance of 20 MQ. The amplitude detector and the dynamical biasing circuit, which tunes the current in the amplifier according to the signal amplitude, save up to 40% of the total power consumption. The amplifier achieves a measured noise level of 0.34 μVrms in a 0.6-175-Hz band, consuming 6.3-μW power.