Signal Quality Metrics Research Articles

Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.

Empirical Wavelet Transform Based ECG Signal Filtering Method

The electrocardiogram (ECG) is a diagnostic tool that provides insights into the heart’s electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, Liouville–Weyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWT’s strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.

Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients

We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video “Bang! You’re Dead” and performed a recognition memory test for movie content. 3 T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.

Measurement of the conductive fabric contact impedance for bioelectrical signal acquisition purposes

Information about the physiological state of the human organism can be obtained by detecting bioelectrical signals. The detection electrodes are a critical point in the measurement chain. The use of textile electrodes is advantageous due to comfort and less stressful measurement of bioelectrical signals for the patient. A unique automatized measurement system was proposed to measure contact impedance magnitude and phase. This research investigated several conductive textile materials in terms of impedance at the artificial electrode–skin interface to select the most appropriate material for ECG signal acquisition. The most suitable samples for further investigation appeared to be polyamide fibre covered with pure silver, without/with antioxidant treatment. These materials were further compared to conventional Ag/AgCl electrodes while measuring ECG on living subjects. Signal quality metrics show a high level of similarity between the ECG signals measured by textile electrodes and conventional Ag/AgCl electrodes.

Controlling of the bidirectional amplifier chain for optical frequency distribution based on a two-dimensional noise detector.

In the paper we present a solution for controlling the chain of bidirectional optical amplifiers, intended for long-haul fiber links used to distribute signals produced by optical atomic clocks. The solution is based on a dedicated two-channel noise detector, which allows independent measurement of the noise contributions related to interferometric signal fading and additive wideband noise. New signal quality metrics, based on two-dimensional noise detector, allows to distribute properly the needed gain among the chained amplifiers. Experimental results performed both in laboratory conditions and on a real 600 km-long link, confirming proper operation of proposed solutions, are presented.

A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study

We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE). ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG. This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.

Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals

Due to the simplicity and convenience of PPG signal acquisition, the detection of the respiration rate based on the PPG signal is more suitable for dynamic monitoring than the impedance spirometry method, but it is challenging to achieve accurate predictions from low-signal-quality PPG signals, especially in intensive-care patients with weak PPG signals. The goal of this study was to construct a simple model for respiration rate estimation based on PPG signals using a machine-learning approach fusing signal quality metrics to improve the accuracy of estimation despite the low-signal-quality PPG signals. In this study, we propose a method based on the whale optimization algorithm (WOA) with a hybrid relation vector machine (HRVM) to construct a highly robust model considering signal quality factors to estimate RR from PPG signals in real time. To detect the performance of the proposed model, we simultaneously recorded PPG signals and impedance respiratory rates obtained from the BIDMC dataset. The results of the respiration rate prediction model proposed in this study showed that the MAE and RMSE were 0.71 and 0.99 breaths/min, respectively, in the training set, and 1.24 and 1.79 breaths/min, respectively, in the test set. Compared without taking signal quality factors into account, MAE and RMSE are reduced by 1.28 and 1.67 breaths/min, respectively, in the training set, and reduced by 0.62 and 0.65 breaths/min in the test set. Even in the nonnormal breathing range below 12 bpm and above 24 bpm, the MAE reached 2.68 and 4.28 breaths/min, respectively, and the RMSE reached 3.52 and 5.01 breaths/min, respectively. The results show that the model that considers the PPG signal quality and respiratory quality proposed in this study has obvious advantages and application potential in predicting the respiration rate to cope with the problem of low signal quality.

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still not straightforward. The best algorithm selection improves the signal-to-noise ratio (SNR) and therefore improves the accuracy of the recognition and classification of vital signs. We recorded more than 300 temporal rPPG signals where the noise was not motion-induced. Then, we investigated the best digital filter in pre-processing temporal rPPG data and compared the performances of 10 filters with 10 orders each (i.e., a total of 100 filters). The performances are assessed using a signal quality metric on three levels. The quality of the raw signals was classified under three categories; Q1 being the best and Q3 being the worst. The results are presented in SNR scores, which show that the Chebyshev II orders of 2nd, 4th, and 6th perform the best for denoising rPPG signals.

Modulation Recognition Using Signal Enhancement and Multistage Attention Mechanism

Robustness against noise is critical for modulation recognition (MR) approaches deployed in real-world communication systems. In MR systems, a corrupted signal is normally enhanced using low-level signal enhancement (SE) before signal classification (SC). Many existing approaches address signal distortion problems by compartmentalizing SE from SC. While those approaches allow for efficient development, they also dictate compartmentalized performance metrics, without feedback from the SC module. For example, SE modules are designed using perceptual signal quality metrics but not with SC in mind. To improve the effectiveness of SE on MR, this paper proposes a joint learning framework consisting of three cascaded modules: dual-channel spectrum fusion, SE, and SC. Instead of separately processing SE and SC, these three modules are integrated into one framework and jointly trained with a single recognition loss. In contrast to estimating clean signals, the SE module in the proposed joint learning framework is trained to predict a ratio mask and find important time-frequency bins for the SC module. We integrate a multistage attention mechanism into the framework to further increase the robustness. The multistage attention mechanism is deployed to strengthen the recognition-related features learned from context information in channel, time, and frequency domains. We evaluate the recognition performance of the proposed framework and its modules on two benchmark datasets: RadioML2016.10a and RadioML2016.10b. The experiment results show that the proposed joint learning framework outperforms the separate learning framework. Moreover, comparisons are performed with several existing learning-based MR methods in the literature. The proposed joint learning framework leads to significant performance improvement, especially for modulated signals corrupted by channel noise.

A comparative study of the performance of methods for f-wave extraction

Objective. This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach. We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features. Main results. The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

Optimization Algorithms based compensation of mismatches in Time interleaved Analog to Digital Converters

Time interleaved analog to digital converters (TIADCs) play a significant role in signal processing wherever higher sampling rates are required. However, TIADCs suffer from various mismatches like sampling time, dc offset, gain, bandwidth etc. This results in generation of erroneous signal. Numerous methods were proposed for estimation of these mismatches and for correction of the erroneous signal. Optimization algorithms like genetic algorithm (GA), differential evolution (DE) algorithm were also used for estimation of mismatches. An overview of these algorithms and their performance comparison, with respect to various signal quality metrics like signal to noise ratio (SNR), signal to noise, distortion ratio (SNDR) etc, is given in this article.

Objective evaluation of stimulation artefact removal techniques in the context of neural spike sorting

Objective. We present a framework to objectively test and compare stimulation artefact removal techniques in the context of neural spike sorting. Approach. To this end, we used realistic hybrid ground-truth spiking data, with superimposed artefacts from in vivo recordings. We used the framework to evaluate and compare several techniques: blanking, template subtraction by averaging, linear regression, and a multi-channel Wiener filter (MWF). Main results. Our study demonstrates that blanking and template subtraction result in a poorer spike sorting performance than linear regression and MWF, while the latter two perform similarly. Finally, to validate the conclusions found from the hybrid evaluation framework, we also performed a qualitative analysis on in vivo recordings without artificial manipulations. Significance. Our framework allows direct quantification of the impact of the residual artefact on the spike sorting accuracy, thereby allowing for a more objective and more relevant comparison compared to indirect signal quality metrics that are estimated from the signal statistics. Furthermore, the availability of a ground truth in the form of single-unit spiking activity also facilitates a better estimation of such signal quality metrics.

Secure audio signal transmission based on color image watermarking

AbstractThis paper presents a new color image watermarking system with encrypted audio signals using singular value decomposition (SVD), Arnold, and chaotic techniques for secure audio transmission over open channels. This system is based on embedding encrypted audio watermarks after transforming them into a 2‐D format in the singular values of the color cover image components. Three different audio watermarks are embedded in the three color image components after separating the cover image into its red, green and blue components. The three resulting watermarked components are merged again to produce the final color watermarked image. To maintain the audio signal quality, we estimate some audio signal quality metrics after converting the audio signal into a 1‐D format. The transformation from the 1‐D to 2‐D formats is performed through lexicographic ordering. Simulation results demonstrate that the proposed system preserves the high quality of the RGB cover image. In addition, the audio watermark extraction and decryption are possible, even in the presence of attacks. Furthermore, the results show an enhancement in the watermark detectability in the presence of severe attacks, such as rotation, blurring, cropping, histogram equalization, and noise addition to the watermarked images.

An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.

Today’s wearable medical devices are becoming popular because of their price and ease of use. Most wearable medical devices allow users to continuously collect and check their health data, such as electrocardiograms (ECG). Therefore, many of these devices have been used to monitor patients with potential heart pathology as they perform their daily activities. However, one major challenge of collecting heart data using mobile ECG is baseline wander and motion artifacts created by the patient’s daily activities, resulting in false diagnoses. This paper proposes a new algorithm that automatically removes the baseline wander and suppresses most motion artifacts in mobile ECG recordings. This algorithm clearly shows a significant improvement compared to the conventional noise removal method. Two signal quality metrics are used to compare a reference ECG with its noisy version: correlation coefficients and mean squared error. For both metrics, the experimental results demonstrate that the noisy signal filtered by our algorithm is improved by a factor of ten.

Measuring signal quality in low power wearable ultrasound imaging

Frequency Modulated Continuous Wave (FMCW) ultrasound is an emerging imaging method adapted from radar that can be implemented in wearable ultrasound systems due to the reduced size, power, and signal processing requirements. Acoustic reflections are encoded in the frequency domain, and thus evaluating signal quality currently requires extracting frequency information from a time-varying acquisition signal, which requires additional signal processing. Question: Can we differentiate FMCW ultrasound signal quality using an acquired FMCW time-series? We collected FMCW ultrasound images with a custom ultrasound transducer contacting and losing contact with tissue, to simulate moments of poor signal quality. We used two potential time-domain signal quality metrics: the mean absolute value (MAV) of the signal, and Sample Entropy. Sample Entropy is an information theory method for assessing complexity in a time-series. Both MAV and Sample Entropy could differentiate when the transducer was in good contact with the tissue. MAV required less processing time, but Sample Entropy had less noise when the transducer lost contact. FMCW ultrasound imaging is a promising imaging method that has application in wearable ultrasound systems, and FMCW ultrasound signal quality can be assessed in the time-domain without extracting frequency information. [Work supported by NIH 5U01EB027601-02.]

Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis

Objective. Brain-computer interfaces (BCIs) that record neural activity using intracortical microelectrode arrays (MEAs) have shown promise for mitigating disability associated with neurological injuries and disorders. While the chronic performance and failure modes of MEAs have been well studied and systematically described in non-human primates, there is far less reported about long-term MEA performance in humans. Our group has collected one of the largest neural recording datasets from a Utah MEA in a human subject, spanning over 5 years (2014–2019). Here we present both long-term signal quality and BCI performance as well as highlight several acute signal disruption events observed during the clinical study. Approach. Long-term Utah array performance was evaluated by analyzing neural signal metric trends and decoding accuracy for tasks regularly performed across 448 clinical recording sessions. For acute signal disruptions, we identify or hypothesize the root cause of the disruption, show how the disruption manifests in the collected data, and discuss potential identification and mitigation strategies for the disruption. Main results. Neural signal quality metrics deteriorated rapidly within the first year, followed by a slower decline through the remainder of the study. Nevertheless, BCI performance remained high 5 years after implantation, which is encouraging for the translational potential of this technology as an assistive device. We also present examples of unanticipated signal disruptions during chronic MEA use, which are critical to detect as BCI technology progresses toward home usage. Significance. Our work fills a gap in knowledge around long-term MEA performance in humans, providing longevity and efficacy data points to help characterize the performance of implantable neural sensors in a human population. The trial was registered on ClinicalTrials.gov (Identifier NCT01997125) and conformed to institutional requirements for the conduct of human subjects research.

Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?

Despite a historical focus on prosthetics, the incorporation of electromyography (EMG) sensors into less obtrusive wearable designs has recently gained attention as a potential human–computer interaction scheme for general consumer use. Because consumers are more used to wrist-worn devices, this article presents a comprehensive and systematic investigation of the feasibility of hand gesture recognition using EMG signals recorded at the wrist. A direct comparison of signal and information quality is conducted between concurrently recorded wrist and forearm signals. Both signals were collected simultaneously from 21 subjects while they performed a selection of 17 different single-finger gestures, multifinger gestures, and wrist gestures. Wrist EMG signals yielded consistently higher ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p&lt; 0.05$</tex-math></inline-formula> ) signal quality metrics than forearm signals for gestures that involved fine finger movements, while maintaining comparable quality for wrist gestures. Similarly, the performance of both individual state-of-the-art EMG features and a standard feature set was found to be significantly better when using wrist signals for single and multifinger gestures, and comparable for wrist gestures. Classifiers trained and tested using wrist EMG signals achieved average accuracy levels of 92.1% for single-finger gestures, 91.2% for multifinger gestures, and 94.7% for the conventional wrist gestures. In conclusion, this article clearly demonstrates the feasibility of using wrist EMG signals for hand gesture recognition. Results highlight not only the promise of this approach, but also the viability of incorporating prior knowledge from the prosthetics field in the design of wrist-based EMG pattern recognition systems.

Sensor Fusion for Unobtrusive Respiratory Rate Estimation in Dogs

Respiration is vital to land-dwelling mammals; besides, salient information is encoded in the respiratory rate. Objective assessment of the respiratory rate is difficult in dogs; in particular, if the unobtrusive measurement is desired. The goal of this work was to develop and evaluate a method for unobtrusive sensing of respiratory rate in dogs. For this, the “FlexPock” multisensor system, originally developed for unobtrusive estimation of heart rate and respiratory rate in humans via magnetic impedance; accelerometry; and optical measurements, was used to assess canine respiratory rate. In a proof-of-concept study with 10 healthy dogs of different breeds and sizes, a total of 240 minutes of data was recorded in the phases standing, sitting, lying down, and walking. An algorithm was developed that estimates the respiratory rate by fusing the information from multiple sensors for increased accuracy and robustness. To discard unusable data, a simple yet effective signal quality metric was introduced. Impedance pneumography recorded using adhesive electrodes was used as a reference. Analysis of the raw FlexPock data revealed that the magnetic impedance and accelerometry were the best individual sensing modalities and fusion of these data further increased the accuracy. Using leave-one-dog-out cross-validation, the average estimation error was 9.5% at a coverage of 50.1%. However, strong variation between dogs and phases was observed. During the walking phase, neither reference nor unobtrusive sensor reported usable results, while the sitting phase exhibited the best performance. In conclusion, the fusion of magnetic impedance and accelerometry can be used for unobtrusive respiratory rate estimation in stationary dogs.

Robust contactless pulse transit time estimation based on signal quality metric

Abstract The pulse transit time (PTT) can provide valuable insight into cardiovascular health, specifically regarding arterial stiffness and blood pressure. Traditionally, PTT is derived by calculating the time difference between two photoplethysmography (PPG) measurements, which require a set of body-worn sensors attached to the skin. Recently, remote photoplethysmography (rPPG) has been proposed as a contactless monitoring alternative. The main problem with rPPG based PTT estimation is that motion artifacts affect the shape of waveform leading to the shift or over-detected peaks, which decreases the accuracy of PTT. To overcome this problem, this paper presents a robust pulse-by-pulse PTT estimation framework using a signal quality metric. By exploiting the local temporal information and global periodic characteristics, the metric automatically assesses pulse quality of signal on a pulse-by-pulse basis, and calculates the probabilities of the pulse peak being the actual peak. Furthermore, in order to cope with over-detected and shift pulse peaks, Kalman filter complemented by the proposed signal quality metric is used to adaptively adjust the peaks based on the estimated probability. All the refined peaks are finally used for pulse-by-pulse PTT estimation. The experiment results are promising, suggesting that the proposed framework provides a robust and more accurate PTT estimation in real applications.

Optimal referencing for stereo-electroencephalographic (SEEG) recordings

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.