A Baseline Wander Tracking System for Artifact Rejection in Long-Term Electrocardiography

A PDMS-based microneedle array electrode for long-term ECG recording

To acquire high-quality electrocardiogram (ECG) signals, traditional Ag/AgCl wet electrodes used together with conductive gel can effectively reduce electrode-skin interface impedance (EII) in a short term. However, their weaknesses of poor flexibility and instability can no longer meet the long-term monitoring requirements of intelligent wearable devices. Owing to the flexible dry electrode without conductive gel, it is a good choice to solve the critical problem on drying-out of conductive gel. Therefore, we develop a flexible microneedle array electrode (FMAE) based on polydimethylsiloxane (PDMS) substrate, which obtains reliable bioelectrical signals by way of penetrating into the stratum corneum (SC) of the skin. The fabrication process, including silicon mold, twice PDMS shape-transferring and encapsulation, has advantages of low cost, repeatable production and good biocompatibility. Afterwards, by comparing the performance with different electrodes, impedance test results indicate that the impedance of FMAE are smaller and more stable, and ECG tests in long term and at resting/jogging states also verify that FMAE can obtain durable, stable and reliable signals. In conclusion, FMAE is promising in long-term ECG monitoring.

Multichannel esophageal signals to monitor respiratory rate in preterm infants

BACKGROUND

Apnea of prematurity cannot be reliably measured with current monitoring techniques. Instead, indirect parameters such as oxygen desaturation or bradycardia are captured. We propose a Kalman filter-based detection of respiration activity and hence apnea using multichannel esophageal signals in neonatal intensive care unit patients.

METHODS

We performed a single-center observational study with moderately preterm infants. Commercially available nasogastric feeding tubes containing multiple electrodes were used to capture signals with customized software. Multichannel esophageal raw signals were manually annotated, processed using extended Kalman filter, and compared with standard monitoring data including chest impedance to measure respiration activity.

RESULTS

Out of a total of 405.4 h captured signals in 13 infants, 100 episodes of drop in oxygen saturation or heart rate were examined. Median (interquartile range) difference in respiratory rate was 0.04 (-2.45 to 1.48)/min between esophageal measurements annotated manually and with Kalman filter and -3.51 (-7.05 to -1.33)/min when compared to standard monitoring, suggesting an underestimation of respiratory rate when using the latter.

CONCLUSIONS

Kalman filter-based estimation of respiratory activity using multichannel esophageal signals is safe and feasible and results in respiratory rate closer to visual annotation than that derived from chest impedance of standard monitoring.

A 4-μW Analog Front End Achieving 2.4 NEF for Long-Term ECG Monitoring

An ultra-low-power low-noise analog front end (AFE) is presented in this work, aiming for long-term ECG with clear P-waves for clinical diagnose. The chopper amplifier with passive noise filter and PWM based offset cancellation results in an input-reference noise of 0.39 μVrms, which shows 3.7X noise improvements among the state-of-the-art designs. With the digital offset cancellation by the pulse-width modulation wave, the AFE achieves a low input-referred dc offset of 0.4 μV among 9 tested chips and a low drift under 30 μV. A dynamic scale ADC with low-power comparator strategy prevents the instability and signal-loss, achieving an SFDR of 71.6 dB. The proposed AFE achieves a noise-efficient-factor (NEF) of 2.4 with a power consumption of 4 μW. The fabricated chip is demonstrated in a miniature prototype for long-term ECG monitoring application, presenting a clear ECG waveform with visible P-wave. The simultaneously ECG recording with a medical grade 12-lead ECG Holter shows the effective acquisition of the prototype, proofing the better noise performance.

Deep learning for digitizing highly noisy paper-based ECG records

Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the paper-based ECG records into a high-quality signal is critical for further analysis. We formulated the digitization problem as a segmentation problem and proposed a deep learning method to digitize highly noisy ECG scans. Our method extracts the ECG signal in an end-to-end manner and can handle different paper record layouts. In the experiment, our model clearly extracted the ECG waveform with a Dice coefficient of 0.85 and accurately measured the common ECG parameters with more than 0.90 Pearson's correlation. We showed that the end-to-end approach with deep learning can be powerful in ECG digitization. To the best of our knowledge, we provide the first approach to digitize the least informative noisy binary ECG scans and potentially be generalized to digitize various ECG records.

Multichannel Esophageal Heart Rate Monitoring of Preterm Infants

OBJECTIVE

Autonomic dysregulation in preterm infants requires continuous monitoring of vital signs such as heart rate over days to months. Unfortunately, common surface electrodes are prone to electrocardiography (ECG) signal artifacts and cause serious skin irritations during long-term use. In contrast, esophageal ECG is known to be very sensitive due to the proximity of electrodes and heart and insensitive to external influences. This study addresses if multichannel esophageal ECG qualifies for heart rate monitoring in preterm infants.

METHODS

We recorded esophageal leads with a multi-electrode gastric feeding tube in a clinical study with 13 neonates and compared the heartbeat detection performance with standard surface leads. A computationally simple and versatile ECG wave detection algorithm was used.

RESULTS

Multichannel esophageal ECG manifested heartbeat sensitivity and positive predictive value greater than 98.5% and significant less false negative (FN) ECG waves as compared to surface ECG due to site-typical electrode motion artifacts. False positive bradycardia as indicated with more than 13 consecutive FN ECG waves was equally expectable in esophageal and surface channels. No adverse events were reported for the multi-electrode gastric feeding tube.

CONCLUSION

Heart rate monitoring of preterm infants with multiple esophageal electrodes is considered as feasible and reliable. Less signal artifacts will improve the detection of bradycardia, which is crucial for immediate interventions, and reduce alarm fatigue.

SIGNIFICANCE

Due to the possibility to integrate the multichannel ECG into a gastric feeding tube and meanwhile omit harmful skin electrodes, the presented system has great potential to facilitate future intensive care of preterm infants.

CEEMDAN-IMFx-PCA-CICA: an improved single-channel blind source separation in multimedia environment for motion artifact reduction in ambulatory ECG

Long-term monitoring of ECG via wearable monitoring systems has already been widely adopted to detect and prevent heart diseases. However, one of the main issues faced by wearable ECG monitoring systems is that motion artifacts significantly affect the systems' stability and reliability. Therefore, motion artifact reduction is a very challenging task in filtering and processing physiological signals. Based on the existing algorithms and ECG prior knowledge, in this paper, we propose an algorithm, CEEMDAN-IMFx-PCA-CICA, for motion artifact reduction in ambulatory ECG signals using single-channel blind source separation technique. Our algorithm first utilizes CEEMDAN to decompose the mixed signals into IMFs (intrinsic mode function) containing different source signal features, thereby forming new multi-dimensional signals. Using the correlation between IMFx (IMF component with the most ECG features) and each IMF, and PCA are then applied to reduce the dimension of each IMF. Finally, the blind separation of the source ECG signals is achieved by using CICA with IMFx as the constraint reference component. The results of our experiments indicate that our algorithm outperformed CEEMDAN-CICA, CEEMDAN-PCA-CICA, and improved CEEMDAN-PCA-CICA. Besides, the number of iterations of the CICA is significantly reduced; the separated source signal is better; the obtained result is stable. Furthermore, the separated ECG signal has a higher correlation with the source ECG signal and a lower RRMSE, especially in the case of high noise-to-signal ratios.

Toward a novel semi-invasive activation mapping tool for the diagnosis of supraventricular arrhythmias from the esophagus

AIMS

Supraventricular arrhythmia diagnosis using the surface electrocardiogram (sECG) is often cumbersome due to limited atrial signal quality. In some instances, use of esophageal electrocardiography (eECG) may facilitate the diagnosis. Here, we present a novel approach to reconstruct cardiac activation maps from eECG recordings.

METHODS

eECGs and sECGs were recorded from 19 individuals using standard acquisition tools. From the recordings, algorithms were developed to estimate the esophageal ECG catheter's position and to reconstruct high-resolution mappings of the cardiac electric activity projected in the esophagus over time.

RESULTS

Esophageal two-dimensional activation maps were created for five healthy individuals and 14 patients suffering from different arrhythmias. The maps are displayed as time-dependent contour plots, which not only show voltage over time as conventional ECGs, but also the location, direction, and projected propagation speed of the cardiac depolarization wavefront in the esophagus. Representative examples of sinus rhythm, atrial flutter, and ventricular pre-excitation are shown.

CONCLUSION

The methodology presented in this report provides a high-resolution view of the cardiac electric field in the esophagus. It is the first step toward a three-dimensional mapping system, which shall be able to reconstruct a three-dimensional view of the cardiac activation from recordings within the esophagus.

A Two-Wired Ultra-High Input Impedance Active Electrode

This paper presents a novel two-wired active electrode that achieves ultrahigh input impedance using power supply bootstrapping. The proposed circuit reduces the input capacitance of a buffer amplifier while enabling measurements using leads with only two wires, providing a low-complexity and low-cost solution for interference rejection and artifact reduction in dc-coupled dry-contact biopotential measurements. An implemented prototype shows that, even using standard operational amplifiers, an input capacitance as low as 71 fF can be obtained, maintaining a high impedance in a 0-1 kHz bandwidth, sufficient for ECG, EEG, and EMG measurements. The circuit has a simple and easily replicable implementation that requires no individual adjustment. A common mode rejection ratio (CMRR) above 103 dB at 50 Hz was achieved and the increased rejection to interference due to the potential divider effect was experimentally tested maintaining a 92-dB CMRR at 50 Hz with a 1.2-M source impedance unbalance. ECG measurements were conducted to validate the active electrode against a traditional alternative, and a test with dry-contact EEG electrodes was successfully conducted. Although the proposed circuit is intended to acquire superficial electrophysiological signals using dry electrodes, it can be used for measurement from other high-impedance sources, such as micropipette electrodes.

A Wireless ExG Interface for Patch-Type ECG Holter and EMG-Controlled Robot Hand

This paper presents a wearable electrophysiological interface with enhanced immunity to motion artifacts. Anti-artifact schemes, including a patch-type modular structure and real-time automatic level adjustment, are proposed and verified in two wireless system prototypes of a patch-type electrocardiogram (ECG) module and an electromyogram (EMG)-based robot-hand controller. Their common ExG readout integrated circuit (ROIC), which is reconfigurable for multiple physiological interfaces, is designed and fabricated in a 0.18 μm CMOS process. Moreover, analog pre-processing structures based on envelope detection are integrated with one another to mitigate signal processing burdens in the digital domain effectively.

Graphics-processor-unit-based parallelization of optimized baseline wander filtering algorithms for long-term electrocardiography

Long-term electrocardiogram (ECG) often suffers from relevant noise. Baseline wander in particular is pronounced in ECG recordings using dry or esophageal electrodes, which are dedicated for prolonged registration. While analog high-pass filters introduce phase distortions, reliable offline filtering of the baseline wander implies a computational burden that has to be put in relation to the increase in signal-to-baseline ratio (SBR). Here, we present a graphics processor unit (GPU)-based parallelization method to speed up offline baseline wander filter algorithms, namely the wavelet, finite, and infinite impulse response, moving mean, and moving median filter. Individual filter parameters were optimized with respect to the SBR increase based on ECGs from the Physionet database superimposed to autoregressive modeled, real baseline wander. A Monte-Carlo simulation showed that for low input SBR the moving median filter outperforms any other method but negatively affects ECG wave detection. In contrast, the infinite impulse response filter is preferred in case of high input SBR. However, the parallelized wavelet filter is processed 500 and four times faster than these two algorithms on the GPU, respectively, and offers superior baseline wander suppression in low SBR situations. Using a signal segment of 64 mega samples that is filtered as entire unit, wavelet filtering of a seven-day high-resolution ECG is computed within less than 3 s. Taking the high filtering speed into account, the GPU wavelet filter is the most efficient method to remove baseline wander present in long-term ECGs, with which computational burden can be strongly reduced.