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Recording and monitoring electrically-excitable cells is critical to understanding the complex cellular networking within organs as well as the processes underlying many electro-physiological pathologies. Biopotential recording using an optical-electrode (optrode) is a novel approach which has potential to significantly improve interface-instrumentation impedance mismatching as recording contact-sizes become smaller and smaller. Optrodes incorporate a conductive interface that can sense extracellular potential and an underlying layer of liquid crystals that passively transduces electrical signals into measurable optical signals. This study investigates the impedance properties of this optical technology by varying the diameter of recording sites and observing the corresponding changes in the impedance values. The results show that the liquid crystals in this optrode platform exhibit input impedance values (1 MΩ – 100 GΩ) that are three orders of magnitude higher than the corresponding interface impedance, which is appropriate for voltage sensing. The automatic scaling of the input impedance enabled within the optrode system maintains a relatively constant ratio between input and total system impedance of about one for sensing areas with diameters ranging from 40 µm to 1 mm, at which the calculated signal loss is predicted to be <1%. This feature preserves the interface-transducer impedance ratio, regardless of the size of the recording site, allowing development of passive optrode arrays capable of very high spatial-resolution recordings. [ABSTRACT FROM AUTHOR]

Objective: Noise and disturbances hinder effective interpretation of recorded ECG. To identify the clean parts of a recording, free from such disturbances, various quality indicators have been developed. Previous instances of these indicators focus on human-defined desirable properties of a clean signal. The reliance on human-specified properties places an inherent limitation on the potential power of signal quality indicators. To move away from this limitation, we propose a data-driven quality indicator. Methods: We use an unsupervised deep learning model, the auto-encoder, to derive the quality indicator. For different quality assessment settings we compare the performance of our quality indicator with traditional indicators. Results: The data-driven method performs consistently strong across tasks while performance of the traditional indicators varies strongly from task to task. Conclusion: This strong performance indicates the potential of data-driven quality indicators for use in ECG processing, removing the reliance on expert-specified desirable properties. Significance: The proposed methodology can easily be extended towards learning quality indicators in other data modalities. [ABSTRACT FROM AUTHOR]

Objective: Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection. Methods: The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG. Results: SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5% for pre-ejection period (PEP), 67.2% for left ventricular ejection time (LVET), and 57.7% for PEP/LVET—a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage. Conclusion: These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation. Significance: Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings. [ABSTRACT FROM AUTHOR]

Objective: Local oscillation of the chest wall in response to events during the cardiac cycle may be captured using a sensing modality called seismocardiography (SCG), which is commonly used to infer cardiac time intervals (CTIs) such as the pre-ejection period (PEP). An important factor impeding the ubiquitous application of SCG for cardiac monitoring is that morphological variability of the signals makes consistent inference of CTIs a difficult task in the time-domain. The goal of this work is therefore to enable SCG-based physiological monitoring during trauma-induced hemorrhage using signal dynamics rather than morphological features. Methods: We introduce and explore the observation that SCG signals follow a consistent low-dimensional manifold structure during periods of changing PEP induced in a porcine model of trauma injury. Furthermore, we show that the distance traveled along this manifold correlates strongly to changes in PEP (Δ PEP). Results: Δ PEP estimation during hemorrhage was achieved with a median R2 of 92.5% using a rapid manifold approximation method, comparable to an ISOMAP reference standard, which achieved an R2 of 95.3%. Conclusion: Rapidly approximating the manifold structure of SCG signals allows for physiological inference abstracted from the time-domain, laying the groundwork for robust, morphology-independent processing methods. Significance: Ultimately, this work represents an important advancement in SCG processing, enabling future clinical tools for trauma injury management. [ABSTRACT FROM AUTHOR]

Objective: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. Methods: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. Results: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. Conclusion: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. Significance: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals. [ABSTRACT FROM AUTHOR]

Objective: Respiratory rate (RR) estimation algorithms based on the photoplethymogram (PPG) and electrocardiogram (ECG) lack clinical robustness. This is because the PPG and ECG respiratory modulations are dependent on patient physiology, regardless of general signal quality. The present work describes an RR estimation algorithm using respiratory quality indices (RQIs) that assess the presence or absence of the PPG- and ECG-derived respiratory modulations. Methods: Six respiratory waveforms are derived from the amplitude modulation, frequency modulation, and baseline wander of the PPG and ECG. The respiratory quality of each modulation is assessed by using RQIs based on the fast Fourier transform, autoregression, and autocorrelation. The individual RQIs are fused to obtain a single RQI per modulation per time window. Based on a tunable threshold, the RQIs are used to discard poor modulations and weight the remaining modulations to provide a single RR estimation per time window. Results: The proposed method was tested on two independent datasets and found that using a conservative threshold, the mean absolute error was 0.71 $\pm$ 0.89 and 3.12 $\pm$ 4.39 brpm while discarding only 1.3% and 23.2% of all time windows, for each dataset, respectively. Conclusion: These errors are either better than or comparable to current methods, and the number of windows discarded is far lower demonstrating improved robustness. Significance: This work describes a novel preprocessing algorithm that can be implemented in conjunction with other RR estimation techniques to improve robustness by specifically considering the quality of the respiratory information. [ABSTRACT FROM AUTHOR]

Objective: The noninvasive fetal ECG (NI-FECG) from abdominal recordings offers novel prospects for prenatal monitoring. However, NI-FECG signals are corrupted by various nonstationary noise sources, making the processing of abdominal recordings a challenging task. In this paper, we present an online approach that dynamically assess the quality of NI-FECG to improve fetal heart rate (FHR) estimation. Methods: Using a naive Bayes classifier, state-of-the-art and novel signal quality indices (SQIs), and an existing adaptive Kalman filter, FHR estimation was improved. For the purpose of training and validating the proposed methods, a large annotated private clinical dataset was used. Results: The suggested classification scheme demonstrated an accuracy of Krippendorff's alpha = \text0.65 in determining the overall quality of NI-FECG signals. The proposed Kalman filter outperformed alternative methods for FHR estimation achieving \text75.6\% accuracy. Conclusion: The proposed algorithm was able to reliably reflect changes of signal quality and can be used in improving FHR estimation. Significance: NI-ECG signal quality estimation and multichannel information fusion are largely unexplored topics. Based on previous works, multichannel FHR estimation is a field that could strongly benefit from such methods. The developed SQI algorithms as well as resulting classifier were made available under a GNU GPL open-source license and contributed to the FECGSYN toolbox. [ABSTRACT FROM PUBLISHER]

An automated algorithm to assess electrocardiogram (ECG) quality for both normal and abnormal rhythms is presented for false arrhythmia alarm suppression of intensive care unit (ICU) monitors. A particular focus is given to the quality assessment of a wide variety of arrhythmias. Data from three databases were used: the Physionet Challenge 2011 dataset, the MIT-BIH arrhythmia database, and the MIMIC II database. The quality of more than 33 000 single-lead 10 s ECG segments were manually assessed and another 12 000 bad-quality single-lead ECG segments were generated using the Physionet noise stress test database. Signal quality indices (SQIs) were derived from the ECGs segments and used as the inputs to a support vector machine classifier with a Gaussian kernel. This classifier was trained to estimate the quality of an ECG segment. Classification accuracies of up to 99% on the training and test set were obtained for normal sinus rhythm and up to 95% for arrhythmias, although performance varied greatly depending on the type of rhythm. Additionally, the association between 4050 ICU alarms from the MIMIC II database and the signal quality, as evaluated by the classifier, was studied. Results suggest that the SQIs should be rhythm specific and that the classifier should be trained for each rhythm call independently. This would require a substantially increased set of labeled data in order to train an accurate algorithm. [ABSTRACT FROM PUBLISHER]

A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG), arterial blood pressure (ABP) measurements, and ECG showed that the SQI is monotonically related to signal-to-noise ratio (simulated by adding white Gaussian noise) and to subjective human quality assessment of 1361 multichannel waveform epochs. A receiver-operating-characteristic (ROC) curve analysis, with the human “bad” quality label as positive and the “good” quality label as negative, yielded areas under the ROC curve of 0.86 (PPG), 0.82 (ABP), and 0.68 (ECG). [ABSTRACT FROM PUBLISHER]

Detection and classification of ventricular complexes from the ECG is of considerable importance in Holter and critical care patient monitoring, being essential for the timely diagnosis of dangerous heart conditions. Accurate detection of premature ventricular contractions (PVCs) is particularly important in relation to life-threatening arrhythmias. In this paper, we introduce a model-based dynamic algorithm for tracking the ECG characteristic waveforms using an extended Kalman filter. The algorithm can work on single or multiple leads. A "polargram" - a polar representation of the signal - is introduced, which is constructed using the Bayesian estimations of the state variables. The polargram allows the specification of a polar envelope for normal rhythms. Moreover, we propose a novel measure of signal fidelity by monitoring the covariance matrix of the innovation signals throughout the filtering procedure. PVCs are detected by simultaneous tracking the signal fidelity and the polar envelope. Five databases, including 40 records from MIT-BIH arrhythmia database, are used for differentiating normal, PVC, and other beats. Performance evaluation results show that the proposed method has an average detection accuracy of 99.10%, aggregate sensitivity of 98.77%, and aggregate positive' predictivity of 97.47%. Furthermore, the method is capable of 100% accuracy for records that contain only PVCs and normal sinus beats. The results illustrate that the method can contribute to, and enhance the performance of clinical PVC detection. [ABSTRACT FROM AUTHOR]