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

Efficacy of Various Dry Electrode-Based ECG Sensors: A Review

Long-term electrocardiogram (ECG) monitoring is crucial for detecting and diagnosing cardiovascular diseases (CVDs). Monitoring cardiac health and activities using efficient, noninvasive, and cost-effective techniques such as ECG can be vital for the early detection of different CVDs. Wet electrode-based traditional ECG techniques come with unavoidable limitations of the altered quality of ECG signals caused by gel volatilization and unwanted noise followed by dermatitis. The limitation related to the wet electrodes for long-term ECG monitoring in static and dynamic postures reminds us of the urgency of a suitable substitute. Dry electrodes promise long-term ECG monitoring with the potential for significant noise reduction. This review discusses traditional and alternative techniques to record ECG in terms of meeting the efficient detection of CVDs by conducting a detailed analysis of different types of dry electrodes along with materials (substrate, support, matrix, and conductive part) used for fabrication, followed by the number of human subjects they have been used for validation. The degradation of these electrodes has also been discussed briefly. This review finds a need for more validation on a sufficient number of subjects and the issue of cost and noise hindering the commercialization of these dry electrodes.

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.

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.

Estimation of the Cardiac Field in the Esophagus Using a Multipolar Esophageal Catheter

The rapid progress of invasive therapeutic options for cardiac arrhythmias increases the need for accurate diagnostics. The surface electrocardiogram (ECG) is still the standard of noninvasive diagnostics but lacks atrial signal resolution. By contrast, esophageal electrocardiography (EECG) yields atrial signals of high amplitude and with a high signal-to-noise ratio. Esophageal electrocardiography has become fast and safe, but the mechanical constraints of esophageal measuring catheters and the "random" motion of the catheter inside the subject's esophagus limit the spatial resolution of EECG signals. In this paper, we propose a method to estimate the electrical field projected onto the esophagus with an increased spatial resolution, using commonly available esophageal catheters. In a first step, we estimate the time-varying catheter position, and in a second step, we estimate the projected electrical field with enhanced spatial resolution. The proposed algorithm comprises several consecutive optimization steps, where each intermediate step produces not just a single point estimate, but a cost function over multiple solutions, which reduces the information loss at each processing step. We conclude with examples from a clinical trial, where the fields of cardiac arrhythmias are presented as two-dimensional contour plots.

Mobile GPU-based implementation of automatic analysis method for long-term ECG

Background

Long-term electrocardiogram (ECG) is one of the important diagnostic assistant approaches in capturing intermittent cardiac arrhythmias. Combination of miniaturized wearable holters and healthcare platforms enable people to have their cardiac condition monitored at home. The high computational burden created by concurrent processing of numerous holter data poses a serious challenge to the healthcare platform. An alternative solution is to shift the analysis tasks from healthcare platforms to the mobile computing devices. However, long-term ECG data processing is quite time consuming due to the limited computation power of the mobile central unit processor (CPU).

Methods

This paper aimed to propose a novel parallel automatic ECG analysis algorithm which exploited the mobile graphics processing unit (GPU) to reduce the response time for processing long-term ECG data. By studying the architecture of the sequential automatic ECG analysis algorithm, we parallelized the time-consuming parts and reorganized the entire pipeline in the parallel algorithm to fully utilize the heterogeneous computing resources of CPU and GPU.

Results

The experimental results showed that the average executing time of the proposed algorithm on a clinical long-term ECG dataset (duration 23.0 ± 1.0 h per signal) is 1.215 ± 0.140 s, which achieved an average speedup of 5.81 ± 0.39× without compromising analysis accuracy, comparing with the sequential algorithm. Meanwhile, the battery energy consumption of the automatic ECG analysis algorithm was reduced by 64.16%. Excluding energy consumption from data loading, 79.44% of the energy consumption could be saved, which alleviated the problem of limited battery working hours for mobile devices.

Conclusion

The reduction of response time and battery energy consumption in ECG analysis not only bring better quality of experience to holter users, but also make it possible to use mobile devices as ECG terminals for healthcare professions such as physicians and health advisers, enabling them to inspect patient ECG recordings onsite efficiently without the need of a high-quality wide-area network environment.

High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal

Long-term electrocardiogram (ECG) has become one of the important diagnostic assist methods in clinical cardiovascular domain. Long-term ECG is primarily used for the detection of various cardiovascular diseases that are caused by various cardiac arrhythmia such as myocardial infarction, cardiomyopathy, and myocarditis. In the past few years, the development of an automatic heartbeat classification method has been a challenge. With the accumulation of medical data, personalized heartbeat classification of a patient has become possible. For the long-term data accumulation method, such as the holter, it is difficult to obtain the analysis results in a short time using the original method of serial design. The pressure to develop a personalized automatic classification model is high. To solve these challenges, this paper implemented a parallel general regression neural network (GRNN) to classify the heartbeat, and achieved a 95% accuracy according to the Association for the Advancement of Medical Instrumentation. We designed an online learning program to form a personalized classification model for patients. The achieved accuracy of the model is 88% compared to the specific ECG data of the patients. The efficiency of the parallel GRNN with GTX780Ti can improve by 450 times.

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

Long-term electrocardiogram (ECG) signals might suffer from relevant baseline disturbances during physical activity. Motion artifacts in particular are more pronounced with dry surface or esophageal electrodes which are dedicated to prolonged ECG recording. In this paper we present a method called baseline wander tracking (BWT) that tracks and rejects strong baseline disturbances and avoids concurrent saturation of the analog front-end. The proposed algorithm shifts the baseline level of the ECG signal to the middle of the dynamic input range. Due to the fast offset shifts, that produce much steeper signal portions than the normal ECG waves, the true ECG signal can be reconstructed offline and filtered using computationally intensive algorithms. Based on Monte Carlo simulations we observed reconstruction errors mainly caused by the non-linearity inaccuracies of the DAC. However, the signal to error ratio of the BWT is higher compared to an analog front-end featuring a dynamic input ranges above 15 mV if a synthetic ECG signal was used. The BWT is additionally able to suppress (electrode) offset potentials without introducing long transients. Due to its structural simplicity, memory efficiency and the DC coupling capability, the BWT is dedicated to high integration required in long-term and low-power ECG recording systems.