Artifact removal in physiological signals--practices and possibilities

Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal

The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda (λ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.

A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data

The Big data as Electroencephalography (EEG) can induce by artifacts during acquisition process which will obstruct the features and quality of interest in the signal. The healthcare diagnostic procedures need strong and viable biomedical signals and elimination of artifacts from EEG is important. In this research paper, an improved ensemble approach is proposed for single channel EEG signal motion artifacts removal. Ensemble Empirical Mode Decomposition and Canonical Correlation Analysis (EEMD-CCA) filter combination are applied to remove artifact effectively and further Stationary Wavelet Transform (SWT) is applied to remove the randomness and unpredictability due to motion artifacts from EEG signals. This new filter combination technique was tested against currently available artifact removal techniques and results indicate that the proposed algorithm is suitable for use as a supplement to algorithms currently in use.

Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring

Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology.

Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics

This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

So far, many approaches have been developed for motion artifact (MA) reduction from photoplethysmogram (PPG). Specifically, single-input MA reduction methods are useful to apply wearable and mobile healthcare systems because of their low hardware costs and simplicity. However, most of them are insufficiently developed to be used in real-world situations, and they suffer from a phase distortion problem. In this study, we propose a novel single-input MA reduction algorithm based on time-variant forward-backward harmonic notch filtering. To verify the proposed method, we collected real PPG data corrupted by MA and compared it with existing single-input MA reduction methods. In conclusion, the proposed zero-phase line enhancer (ZLE) was found to be superior for MA reduction and exhibited zero phase response.

Adaptive common average reference for in vivo multichannel local field potentials

For in vivo neural recording, local field potential (LFP) is often corrupted by spatially correlated artifacts, especially in awake/behaving subjects. A method named adaptive common average reference (ACAR) based on the concept of adaptive noise canceling (ANC) that utilizes the correlative features of common noise sources and implements with common average referencing (CAR), was proposed for removing the spatially correlated artifacts. Moreover, a correlation analysis was devised to automatically select appropriate channels before generating the CAR reference. The performance was evaluated in both synthesized data and real data from the hippocampus of pigeons, and the results were compared with the standard CAR and several previously proposed artifacts removal methods. Comparative testing results suggest that the ACAR performs better than the available algorithms, especially in a low SNR. In addition, feasibility of this method was provided theoretically. The proposed method would be an important pre-processing step for in vivo LFP processing.

Applying dynamic data collection to improve dry electrode system performance for a P300-based brain-computer interface

OBJECTIVE

Dry electrodes have an advantage over gel-based 'wet' electrodes by providing quicker set-up time for electroencephalography recording; however, the potentially poorer contact can result in noisier recordings. We examine the impact that this may have on brain-computer interface communication and potential approaches for mitigation.

APPROACH

We present a performance comparison of wet and dry electrodes for use with the P300 speller system in both healthy participants and participants with communication disabilities (ALS and PLS), and investigate the potential for a data-driven dynamic data collection algorithm to compensate for the lower signal-to-noise ratio (SNR) in dry systems.

MAIN RESULTS

Performance results from sixteen healthy participants obtained in the standard static data collection environment demonstrate a substantial loss in accuracy with the dry system. Using a dynamic stopping algorithm, performance may have been improved by collecting more data in the dry system for ten healthy participants and eight participants with communication disabilities; however, the algorithm did not fully compensate for the lower SNR of the dry system. An analysis of the wet and dry system recordings revealed that delta and theta frequency band power (0.1-4 Hz and 4-8 Hz, respectively) are consistently higher in dry system recordings across participants, indicating that transient and drift artifacts may be an issue for dry systems.

SIGNIFICANCE

Using dry electrodes is desirable for reduced set-up time; however, this study demonstrates that online performance is significantly poorer than for wet electrodes for users with and without disabilities. We test a new application of dynamic stopping algorithms to compensate for poorer SNR. Dynamic stopping improved dry system performance; however, further signal processing efforts are likely necessary for full mitigation.

Methods for artifact detection and removal from scalp EEG: A review

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

Minimum Noise Estimate filter: a Novel Automated Artifacts Removal method for Field Potentials

In this paper a novel automated and unsupervised method for removing artifacts from multichannel field potential signals is introduced which can be used in brain computer interface (BCI) applications. The method, which is called minimum noise estimate (MNE) filter is based on an iterative thresholding followed by Rayleigh quotient which tries to find an estimate of the noise and to minimize it over the original signal. MNE filter is capable to operate without any prior information about field potential signals. The performance of the proposed method is evaluated by its application on two different type of signals namely electrocorticogram (ECoG) and electroencephalogram (EEG) datasets through a decoding procedure. The results indicate that the proposed method outperforms well-known artifacts removal techniques such as common average referencing (CAR), Laplacian method, independent component analysis (ICA) and wavelet denoising approach.

Automatic detection of noisy channels in fNIRS signal based on correlation analysis

BACKGROUND

fNIRS signals can be contaminated by distinct sources of noise. While most of the noise can be corrected using digital filters, optimized experimental paradigms or pre-processing methods, few approaches focus on the automatic detection of noisy channels.

METHODS

In the present study, we propose a new method that detect automatically noisy fNIRS channels by combining the global correlations of the signal obtained from sliding windows (Cui et al., 2010) with correlation coefficients extracted experimental conditions defined by triggers.

RESULTS

The validity of the method was evaluated on test data from 17 participants, for a total of 16 NIRS channels per subject, positioned over frontal, dorsolateral prefrontal, parietal and occipital areas. Additionally, the detection of noisy channels was tested in the context of different levels of cognitive requirement in a working memory N-back paradigm.

COMPARISON WITH EXISTING METHOD(S)

Bad channels detection accuracy, defined as the proportion of bad NIRS channels correctly detected among the total number of channels examined, was close to 91%. Under different cognitive conditions the area under the Receiver Operating Curve (AUC) increased from 60.5% (global correlations) to 91.2% (local correlations).

CONCLUSIONS

Our results show that global correlations are insufficient for detecting potentially noisy channels when the whole data signal is included in the analysis. In contrast, adding specific local information inherent to the experimental paradigm (e.g., cognitive conditions in a block or event-related design), improved detection performance for noisy channels. Also, we show that automated fNIRS channel detection can be achieved with high accuracy at low computational cost.

The impact of head movements on EEG and contact impedance: an adaptive filtering solution for motion artifact reduction

Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements.

Motion Artifact Suppression in Impedance Pneumography Signal for Portable Monitoring of Respiration: An Adaptive Approach

The focus of this paper is motion artifact (MA) reduction from the impedance pneumography (IP) signal, which is widely used to monitor respiration. The amplitude of the MA that contaminates the IP signal is often much larger than the amplitude of the respiratory component of the signal. Moreover, the morphology and frequency composition of the artifacts may be very similar to that of the respiration, making it difficult to remove these artifacts. The proposed filter uses a regularization term to ensure that the pattern of the filtered signal is similar to that of respiration. It also ensures that the amplitude of the filter output is within the expected range of the IP signal by imposing an ε-tube on the filtered signal. The adaptive ε-tube filter is 100 times faster than the previously proposed nonadaptive version and achieves higher accuracies. Moreover, the experimental results, using several different performance measures, suggest that the proposed method outperforms popular MA reduction methods such as normalized least mean squares (NLMS) and recursive least squares (RLS) as well as independent component analysis (ICA). When used to extract the respiratory rate, the adaptive ε-tube achieves a mean error of 1.27 breaths per minute (BPM) compared to 4.72 and 4.63 BPM for the NLMS and RLS filters, respectively. When compared to the ICA algorithm, the proposed filter has an error of 1.06 BPM compared to 3.47 BPM for ICA. The statistical analyses indicate that all of the reported performance improvements are significant.

Iterative Covariance-Based Removal of Time-Synchronous Artifacts: Application to Gastrointestinal Electrical Recordings

OBJECTIVE

The aim of this study was to develop, validate, and apply a fully automated method for reducing large temporally synchronous artifacts present in electrical recordings made from the gastrointestinal (GI) serosa, which are problematic for properly assessing slow wave dynamics. Such artifacts routinely arise in experimental and clinical settings from motion, switching behavior of medical instruments, or electrode array manipulation.

METHODS

A novel iterative Covariance-Based Reduction of Artifacts (COBRA) algorithm sequentially reduced artifact waveforms using an updating across-channel median as a noise template, scaled and subtracted from each channel based on their covariance.

RESULTS

Application of COBRA substantially increased the signal-to-artifact ratio (12.8 ± 2.5 dB), while minimally attenuating the energy of the underlying source signal by 7.9% on average ( -11.1 ± 3.9 dB).

CONCLUSION

COBRA was shown to be highly effective for aiding recovery and accurate marking of slow wave events (sensitivity = 0.90 ± 0.04; positive-predictive value = 0.74 ± 0.08) from large segments of in vivo porcine GI electrical mapping data that would otherwise be lost due to a broad range of contaminating artifact waveforms.

SIGNIFICANCE

Strongly reducing artifacts with COBRA ultimately allowed for rapid production of accurate isochronal activation maps detailing the dynamics of slow wave propagation in the porcine intestine. Such mapping studies can help characterize differences between normal and dysrhythmic events, which have been associated with GI abnormalities, such as intestinal ischemia and gastroparesis. The COBRA method may be generally applicable for removing temporally synchronous artifacts in other biosignal processing domains.

An Algorithm for Real-Time Pulse Waveform Segmentation and Artifact Detection in Photoplethysmograms

Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts' annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.

Negligible Motion Artifacts in Scalp Electroencephalography (EEG) During Treadmill Walking

Recent mobile brain/body imaging (MoBI) techniques based on active electrode scalp electroencephalogram (EEG) allow the acquisition and real-time analysis of brain dynamics during active unrestrained motor behavior involving whole body movements such as treadmill walking, over-ground walking and other locomotive and non-locomotive tasks. Unfortunately, MoBI protocols are prone to physiological and non-physiological artifacts, including motion artifacts that may contaminate the EEG recordings. A few attempts have been made to quantify these artifacts during locomotion tasks but with inconclusive results due in part to methodological pitfalls. In this paper, we investigate the potential contributions of motion artifacts in scalp EEG during treadmill walking at three different speeds (1.5, 3.0, and 4.5 km/h) using a wireless 64 channel active EEG system and a wireless inertial sensor attached to the subject’s head. The experimental setup was designed according to good measurement practices using state-of-the-art commercially available instruments, and the measurements were analyzed using Fourier analysis and wavelet coherence approaches. Contrary to prior claims, the subjects’ motion did not significantly affect their EEG during treadmill walking although precaution should be taken when gait speeds approach 4.5 km/h. Overall, these findings suggest how MoBI methods may be safely deployed in neural, cognitive, and rehabilitation engineering applications.

A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts

In this work, we describe a methodology to probabilistically estimate the R-peak locations of an electrocardiogram (ECG) signal using a particle filter. This is useful for heart rate estimation, which is an important metric for medical diagnostics. Some scenarios require constant in-home monitoring using a wearable device. This poses a particularly challenging environment for heart rate detection, due to the susceptibility of ECG signals to motion artifacts. In this work, we show how the particle filter can effectively track the true R-peak locations amidst the motion artifacts, given appropriate heart rate and R-peak observation models. A particle filter based framework has several advantages due to its freedom from strict assumptions on signal and noise models, as well as its ability to simultaneously track multiple possible heart rate hypotheses. Moreover, the proposed framework is not exclusive to ECG signals and could easily be leveraged for tracking other physiological parameters. We describe the implementation of the particle filter and validate our approach on real ECG data affected by motion artifacts from the MIT-BIH noise stress test database. The average heart rate estimation error is about 5 beats per minute for signal streams contaminated with noisy segments with SNR as low as -6 dB.

Sensor fusion methods for reducing false alarms in heart rate monitoring

Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.

UnoViS: the MedIT public unobtrusive vital signs database

While PhysioNet is a large database for standard clinical vital signs measurements, such a database does not exist for unobtrusively measured signals. This inhibits progress in the vital area of signal processing for unobtrusive medical monitoring as not everybody owns the specific measurement systems to acquire signals. Furthermore, if no common database exists, a comparison between different signal processing approaches is not possible. This gap will be closed by our UnoViS database. It contains different recordings in various scenarios ranging from a clinical study to measurements obtained while driving a car. Currently, 145 records with a total of 16.2 h of measurement data is available, which are provided as MATLAB files or in the PhysioNet WFDB file format. In its initial state, only (multichannel) capacitive ECG and unobtrusive PPG signals are, together with a reference ECG, included. All ECG signals contain annotations by a peak detector and by a medical expert. A dataset from a clinical study contains further clinical annotations. Additionally, supplementary functions are provided, which simplify the usage of the database and thus the development and evaluation of new algorithms. The development of urgently needed methods for very robust parameter extraction or robust signal fusion in view of frequent severe motion artifacts in unobtrusive monitoring is now possible with the database.

EEG artifact removal-state-of-the-art and guidelines

This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time

Blood pressure monitoring based on pulse transit or arrival time has been the focus of much research in order to design ambulatory blood pressure monitors. The accuracy of these monitors is limited by several challenges, such as acquisition and processing of physiological signals as well as changes in vascular tone and the pre-ejection period. In this work, a literature survey covering recent developments is presented in order to identify gaps in the literature. The findings of the literature are classified according to three aspects. These are the calibration of pulse transit/arrival times to blood pressure, acquisition and processing of physiological signals and finally, the design of fully integrated blood pressure measurement systems. Alternative technologies as well as locations for the measurement of the pulse wave signal should be investigated in order to improve the accuracy during calibration. Furthermore, the integration and validation of monitoring systems needs to be improved in current ambulatory blood pressure monitors.

MS-QI: A Modulation Spectrum-Based ECG Quality Index for Telehealth Applications

As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.

Heart monitoring systems--a review

To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.

Photoplethysmograph signal reconstruction based on a novel hybrid motion artifact detection-reduction approach. Part I: Motion and noise artifact detection

Motion and noise artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA detection method which can provide a clean vs. corrupted decision on each successive PPG segment. For motion artifact detection, we compute four time-domain parameters: (1) standard deviation of peak-to-peak intervals (2) standard deviation of peak-to-peak amplitudes (3) standard deviation of systolic and diastolic interval ratios, and (4) mean standard deviation of pulse shape. We have adopted a support vector machine (SVM) which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them. We apply several distinct features of the PPG data to enhance classification performance. The algorithm we developed was verified on PPG data segments recorded by simulation, laboratory-controlled and walking/stair-climbing experiments, respectively, and we compared several well-established MNA detection methods to our proposed algorithm. All compared detection algorithms were evaluated in terms of motion artifact detection accuracy, heart rate (HR) error, and oxygen saturation (SpO2) error. For laboratory controlled finger, forehead recorded PPG data and daily-activity movement data, our proposed algorithm gives 94.4, 93.4, and 93.7% accuracies, respectively. Significant reductions in HR and SpO2 errors (2.3 bpm and 2.7%) were noted when the artifacts that were identified by SVM-MNA were removed from the original signal than without (17.3 bpm and 5.4%). The accuracy and error values of our proposed method were significantly higher and lower, respectively, than all other detection methods. Another advantage of our method is its ability to provide highly accurate onset and offset detection times of MNAs. This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed, which is the subject of the companion paper to this article. Finally, our MNA detection algorithm is real-time realizable as the computational speed on the 7-s PPG data segment was found to be only 7 ms with a Matlab code.

Employing ensemble empirical mode decomposition for artifact removal: extracting accurate respiration rates from ECG data during ambulatory activity

Observation of a patient's respiration signal can provide a clinician with the required information necessary to analyse a subject's wellbeing. Due to an increase in population number and the aging population demographic there is an increasing stress being placed on current healthcare systems. There is therefore a requirement for more of the rudimentary patient testing to be performed outside of the hospital environment. However due to the ambulatory nature of these recordings there is also a desire for a reduction in the number of sensors required to perform the required recording in order to be unobtrusive to the wearer, and also to use textile based systems for comfort. The extraction of a proxy for the respiration signal from a recorded electrocardiogram (ECG) signal has therefore received considerable interest from previous researchers. To allow for accurate measurements, currently employed methods rely on the availability of a clean artifact free ECG signal from which to extract the desired respiration signal. However, ambulatory recordings, made outside of the hospital-centric environment, are often corrupted with contaminating artifacts, the most degrading of which are due to subject motion. This paper presents the use of the ensemble empirical mode decomposition (EEMD) algorithm to aid in the extraction of the desired respiration signal. Two separate techniques are examined; 1) Extraction of the respiration signal directly from the noisy ECG 2) Removal of the artifact components relating to the subject movement allowing for the use of currently available respiration signal detection techniques. Results presented illustrate that the two proposed techniques provide significant improvements in the accuracy of the breaths per minute (BPM) metric when compared to the available true respiration signal. The error reduced from ± 5.9 BPM prior to the use of the two techniques to ± 2.9 and ± 3.3 BPM post processing using the EEMD algorithm techniques.

Photoplethysmograph signal reconstruction based on a novel motion artifact detection-reduction approach. Part II: Motion and noise artifact removal

We introduce a new method to reconstruct motion and noise artifact (MNA) contaminated photoplethysmogram (PPG) data. A method to detect MNA corrupted data is provided in a companion paper. Our reconstruction algorithm is based on an iterative motion artifact removal (IMAR) approach, which utilizes the singular spectral analysis algorithm to remove MNA artifacts so that the most accurate estimates of uncorrupted heart rates (HRs) and arterial oxygen saturation (SpO2) values recorded by a pulse oximeter can be derived. Using both computer simulations and three different experimental data sets, we show that the proposed IMAR approach can reliably reconstruct MNA corrupted data segments, as the estimated HR and SpO2 values do not significantly deviate from the uncorrupted reference measurements. Comparison of the accuracy of reconstruction of the MNA corrupted data segments between our IMAR approach and the time-domain independent component analysis (TD-ICA) is made for all data sets as the latter method has been shown to provide good performance. For simulated data, there were no significant differences in the reconstructed HR and SpO2 values starting from 10 dB down to -15 dB for both white and colored noise contaminated PPG data using IMAR; for TD-ICA, significant differences were observed starting at 10 dB. Two experimental PPG data sets were created with contrived MNA by having subjects perform random forehead and rapid side-to-side finger movements show that; the performance of the IMAR approach on these data sets was quite accurate as non-significant differences in the reconstructed HR and SpO2 were found compared to non-contaminated reference values, in most subjects. In comparison, the accuracy of the TD-ICA was poor as there were significant differences in reconstructed HR and SpO2 values in most subjects. For non-contrived MNA corrupted PPG data, which were collected with subjects performing walking and stair climbing tasks, the IMAR significantly outperformed TD-ICA as the former method provided HR and SpO2 values that were non-significantly different than MNA free reference values.

Motion artifact reduction in PPG signals from face: face tracking & stochastic state space modeling approach

The Photoplethymography(PPG) is generally measured on a finger or an ear using contact sensors. The recent several studies using non-contact sensor such as CCD camera and web-cam to measure PPG have been introduced. However the motion artifact issue is also emerging in non-contact camera sensing similar to contact-type one because it is sensitive to artifacts generated by subject's head and body motion. In this paper, the two sequential approaches for a motion artifact reduction algorithm are presented; the one is a face tracking method that detects and tracks the skin region of face which is containing PPG signals, the other is the reduction method of motion artifact due to various head & face movement such as roll, yaw, pitch, translation and scale. Results of the proposed KF are compared to these of the FIR band pass filter(BPF).

Automatic detection and classification of artifacts in single-channel EEG

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Improved signal quality indication for photoplethysmographic signals incorporating motion artifact detection

Wearable monitoring systems have gained tremendous popularity in the health-care industry, opening new possibilities in diagnostic routines and medical treatments. Numerous hardware systems have been presented since, which allow for continuous acquisition of various biosignals like the ECG, PPG, EMG or EEG and which are suited for ambulatory settings. Unfortunately, these flexible systems are liable to motion artifacts and especially photoplethysmographic signals are seriously distorted when the patient is not at rest. A lot of work has been done to reduce artifacts and noise, ranging from simple filtering methods to very complex statistical approaches. With regard to the PPG, certain quality indices have been proposed to evaluate the signal conditions. As movements are the primary source of signal disturbances, the relation between the output of a signal quality estimator and acceleration data captured directly on the PPG sensor is focused in this work. It will be shown that typical motions can be detected on-line, thereby providing additional information which will significantly improve signal quality assessments.

A methodology for validating artifact removal techniques for physiological signals

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a "ground truth" signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this "ground truth," together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.

Multiscale entropy analysis of biological signals

Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy (MSE) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and pathologic conditions. The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia (atrial fibrillation), and with a life-threatening syndrome (congestive heart failure). Further, these different conditions have distinct MSE curve profiles, suggesting diagnostic uses. The results support a general "complexity-loss" theory of aging and disease. We also apply the method to the analysis of coding and noncoding DNA sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called "junk DNA" sequences contain important biological information.