Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications

Identification of audio evoked response potentials in ambulatory EEG data

Electroencephalography (EEG) is commonly used for observing brain function over a period of time. It employs a set of invasive electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers and clinicians to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of undesirable signals, which are known as artefacts, contaminate the EEG signal. EEG and ERP signals are very small (in the order of microvolts); they are often obscured by artefacts with much larger amplitudes in the order of millivolts. This greatly increases the difficulty of interpreting EEG and ERP signals.Typically, ERPs are observed by averaging EEG measurements made with many repetitions of the stimulus. The average may require many tens of repetitions before the ERP signal can be observed with any confidence. This greatly limits the study and useof ERPs. This project explores more sophisticated methods of ERP estimation from measured EEGs. An Optimal Weighted Mean (OWM) method is developed that forms a weighted average to maximise the signal to noise ratio in the mean. This is developedfurther into a Bayesian Optimal Combining (BOC) method where the information in repetitions of ERP measures is combined to provide a sequence of ERP estimations with monotonically decreasing uncertainty. A Principal Component Analysis (PCA) isperformed to identify the basis of signals that explains the greatest amount of ERP variation. Projecting measured EEG signals onto this basis greatly reduces the noise in measured ERPs. The PCA filtering can be followed by OWM or BOC. Finally, crosschannel information can be used. The ERP signal is measured on many electrodes simultaneously and an improved estimate can be formed by combining electrode measurements. A MAP estimate, phrased in terms of Kalman Filtering, is developed using all electrode measurements.The methods developed in this project have been evaluated using both synthetic and measured EEG data. A synthetic, multi-channel ERP simulator has been developed specifically for this project.Numerical experiments on synthetic ERP data showed that Bayesian Optimal Combining of trial data filtered using a combination of PCA projection and Kalman Filtering, yielded the best estimates of the underlying ERP signal. This method has been applied to subsets of real Ambulatory Electroencephalography (AEEG) data, recorded while participants performed a range of activities in different environments. From this analysis, the number of trials that need to be collected to observe the P300 amplitude and delay has been calculated for a range of scenarios

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program

The activities are reported of the NASA Biomedical Applications Team at Southwest Research Institute between 25 August, 1972 and 15 November, 1973. The program background and methodology are discussed along with the technology applications, and biomedical community impacts

Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock

Purpose of Review: Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, inflammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may offer new options for increasing cancer treatment effectiveness and would encompass the prevention, diagnosis, and treatment of this disease. Recent Findings: In recent years, there has been a significant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very first time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications. Summary: This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the state-of-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer researchThis work was supported in part by CLARIFY project, within European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 875160, Instituto de Fomento de la Región de Murcia (INFO) and the European Regional Development Fund (FEDER

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock

Purpose of Review: Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, inflammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may offer new options for increasing cancer treatment effectiveness and would encompass the prevention, diagnosis, and treatment of this disease. Recent Findings: In recent years, there has been a significant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very first time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications. Summary: This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the state-of-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer research.publishersversionpublishe

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock.

Purpose of Review Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, infammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may ofer new options for increasing cancer treatment efectiveness and would encompass the prevention, diagnosis, and treatment of this disease. Recent Findings In recent years, there has been a signifcant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very frst time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications. Summary This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the stateof-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer research.post-print1077 K

System Design And Motion Artifact Removal Algorithm Implementation For Ambulatory Women Ecg Measurement System:e-Bra System

Cardio Vascular Disease (CVD) leads to sudden cardiac death due to irregular phenomenon of the cardiac signal by the abnormal case of blood vessel and cardiac structure. For last three decades, there is an enhanced interest in research for cardiac diseases.. As a result, the death rate by cardiac disease in men has been falling gradually compared with relatively increasing the death rate for women due to CVD. The main reason for this phenomenon is due to the lack of seriousness to female CVD and different symptoms of female CVD compared with the symptoms of male CVD. Usually, because the CVDs for women accompany with ordinary symptoms not attributable to the heart abnormality signal such as unusual fatigue, sleep disturbances, shortness of breath, anxiety, chest discomfort, and indigestion dyspepsia, most women CVD patients do not realize that these symptoms are actually related to the CVD symptoms. Therefore, periodic ECG signal observation is required not only for women who have been diagnosed with heart disease but also for persons who want to examine their heart activity. Electrocardiogram (ECG) is used to diagnose abnormality of heart. Among the medical checkup methods for CVDs, it is very an effective method for the diagnosis of cardiac disease and the early detection of heart abnormality to monitor ECG periodically. This dissertation proposes an effective ECG monitoring system for woman by attaching the system on woman\u27s brassiere by using augmented chest lead attachment method. The suggested system called E-Bra system in this dissertation consists of an ECG transmission system and a computer installed program called E-Bra pro in order to display and analyze the ECG transmitted from the transmission module. The ECG transmission module consists of three parts such as ECG physical signal detection part with 3 stage amplifier and two electrodes, data acquisition with AD converter, and data transmission part with GPRS (General Packet Radio Service) communication, and it has very compact size that is attachable at the bottom layer of a brassiere for women. However, the ECG signal measured from the transmission module includes not only pure ECG components information; P waves QRS complex, and T wave, but also a motion artifact component (MA) due to subject movements. The MA component is one of the reasons for misdiagnosis. Therefore, the main purpose of the E-Bra system is to provide a reliable ECG data set identical to the quality of an ECG data set collected in hospital. Unfortunately, removing MA is a big challenge because the frequency range of the MA is duplicated on the frequency range of the pure ECG components, P-QRS-T. In this dissertation, two motion artifact removal algorithms (MARAs) with adaptive filter structure and independent component analysis concept are suggested, and the performance of the two MARAs will be evaluated by correlation values and signal noise ratio (SNR) values