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

Single channel blind source separation based local mean decomposition for Biomedical applications

Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more sources and fewer sensors) Blind Source Separation (BSS) problem. In this paper, we propose a novel technique using Local Mean Decomposition (LMD) and Independent Component Analysis (ICA) combined with single channel BSS (LMD-ICA). First, the LMD was used to decompose the single channel source into a series of data sequences, which are called as Product Functions (PF), then, ICA algorithm was used to process PFs to get similar independent components and extract the original signals. A comparison was made between LMD-ICA and previously proposed single channel ICA method (EEMD-ICA). The real time experimental results demonstrated the advantage of the proposed single channel source separation method for artifact removal and in biomedical source separation applications. © 2013 IEEE

Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches

Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606

A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD limits the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.Fil: Dai, Yangyang. Nankai University; ChinaFil: Duan, Feng. Nankai University; ChinaFil: Feng, Fan. Nankai University; ChinaFil: Sun, Zhe. RIKEN; JapónFil: Zhang, Yu. Lehigh University Bethlehem; Estados UnidosFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Marti Puig, Pere. Central University of Catalonia; EspañaFil: Solé Casals, Jordi. Central University of Catalonia; Españ

Extracting ECG-based cardiac information from the upper arm

Cardiovascular disease (CVD) is the global number one cause of death. Therefore, there is an acute need for constantly monitoring cardiac conditions and/or cardiac monitoring for extended periods. The current clinical Electrocardiogram (ECG) recording systems require precise placement of electrodes on the patient’s body, often performed by trained medical professionals. These systems also have long wires that require repeated disinfection and can be easily tangled and interfered with clothing and garment. These limitations have severely restricted the possible application scenarios of ECG systems. To overcome these limitations, there is a need for wearable ECG devices with minimal wires to detect possible cardiac abnormalities with minimal intervention from healthcare professionals. Previous research on this topic has focused on extracting cardiac information from the body surface by investigating various electrode placements and developing ECG processing algorithms. Building on these studies, it is possible to develop devices and algorithms that can extract ECG-related information without the need for precise electrode placements on the body's surface. The present thesis aims to extract ECG-based cardiac information using signals recorded from the upper arm. Far-field ECG is prone to contamination by artifacts such as Electromyogram (EMG), which greatly reduces its clinical value. The current study examines how various state-of-the-art heartbeat detection algorithms perform in four levels of simulated EMG artifacts. The simulated EMG was added to Lead II from two different datasets: the MIT-BIH arrhythmia dataset (Dataset 1) and data we collected from 20 healthy participants (Dataset 2). Results show that Stationary Wavelet Transform (SWT) provided the most robust features against EMG intensity level increment among various algorithms. The next step involved recording bio-potential signals using a high-density bio-potential amplification system attached to the upper arm. The system used three high-density electrodes, each with 64 channels, in addition to the standard Lead II. Twenty participants, reported healthy, were asked to perform two tasks: Rest and Elbow Flexion (EF): holding three weights (C1: 1.2 kg, C2: 2.2 kg, and C3: 3.6 kg). The tasks were repeated 2 and 3 times, respectively. Firstly, I identified optimal electrode locations on the upper arm for each task. I then generated a synthesized ECG using the selected electrodes with generalized weights over subjects and trials. Considering the robustness of SWT to EMG intensity level increment, I next focused on optimizing SWT by addressing two of its drawbacks: introducing phase shift and the requirement of a pre-defined mother wavelet. Regarding the first drawback, zero-phase wavelet (Zephlet) was implemented to replace SWT filters with zero-phase filters for the matter of feature extraction from the synthesized ECG. Next, I incorporated the synchronized extracted features with a Multiagent Detection Scheme (MDS) for the means of heartbeat detection. The F1-score for the heartbeat detection was 0.94 ± 0.16, 0.86 ± 0.22, 0.79 ± 0.26, and 0.67 ± 0.31 for Rest and EF with three different levels of muscle contraction (C1 to C3), respectively. Changing the acceptable distance between the detected and actual heartbeats from 50 ms to 20 ms, the F1-score changed to 0.81 ± 0.20, 0.66 ± 0.26, 0.57 ± 0.26, and 0.44 ± 0.26 for Rest and C1 to C3, respectively. Regarding the second drawback, Lattice parametrization was used to optimize the mother wavelet for the means of PQRST delineation. The mother wavelet was generalized over subjects, trials, and tasks. The Pearson’s Correlation Coefficient (CC) between the averaged delineated PQRST from analyzing feature and the averaged PQRST from Lead II using this generalized mother wavelet was 0.88 ± 0.05, 0.85 ± 0.08, 0.83± 0.11, and 0.81 ± 0.12 for Rest and C1-C3, respectively. This thesis makes several contributions to the current literature. It introduces locations on the upper arm that can be used to place sensors in a wearable to capture cardiac activity with robustness across intra-subject, inter-subject and inter-contraction variabilities. It also identifies a robust method against noise increment for heartbeat detection. Zephlet was implemented for the first time that can replace SWT in many applications in which there is a need for synchrony with respect to the original signal or among components. And finally, this thesis introduces a generalized mother wavelet that can be used to extract PQRST and enhance SNR in many applications, such as ECG waveform extraction, arrhythmia detection, and denoising

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods