Biomedical signal quality analysis

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We are currently experiencing an explosive growth in data. This growth includes biomedical data (e.g., electrocardiogram (ECG), electromyogram (EMG), pulse oximetry, blood pressure) which provide valuable information regarding the status and function of the body and are useful in a variety of applications (e.g., health/wellness, biometrics, gaming, and sports/fitness). There exists a large and continually growing body of knowledge regarding the acquisition of biomedical signals, as well as signal processing methods to extract useful information. Biomedical signals, however, can be contaminated due to noise, artifacts, and measurement setup errors; this is particularly true in unsupervised setups (e.g., telehealth), where highly trained operators are not present. Contaminants in the recordings can lead to misinterpretations, inaccuracies, and errors, including misdiagnoses. Despite advances in biomedical instrumentation, contaminants are frequently present in recordings. Currently, biomedical signal quality analysis relies on human experts. This is time-consuming, costly, and prone to human error. In addition, the increases in pervasive, continuous and/or multi-channel monitoring are making manual or semi-automated biomedical signal quality analysis methods impractical due to the amount of data.**The objective of this research is to develop novel automated biomedical signal quality analysis methods to detect, identify, quantify, and mitigate contaminants. The proposed research is organized into three main research themes: 1) Multi-scale analysis, 2) Multi-variate analysis, and 3) Pattern recognition. Multi-scale approaches are well-suited to signals that arise from complex interconnected systems, such as biological systems. Recent research indicates strong potential in this approach, compared to conventional approaches that are either time or frequency based. Multi-variate approaches take advantage of redundant and complementary information within multi-channel recordings (i.e., multiple leads for the same signal type) and/or multi-modal recordings (i.e., recordings of different signal types). Pattern recognition methods can be employed to discover and leverage trends within the data; this can be used to detect and identify contaminants in biomedical signals, as well as classify the quality of data (e.g., excellent, good, poor, unacceptable). Methods will be evaluated in terms of performance (e.g., correctly detecting and identifying contaminations) and generalizability (e.g., methods work for various contaminants and combinations of contaminants).**The exponential growth in biomedical data is associated with various challenges (e.g., acquisition, transferring, storage, and visualization). This research tackles a key, under-researched, area of quality analysis. There is utility within the large datasets being developed, but the capacity to discern which data has adequate quality, and avoid overly contaminated data, is essential. Outcomes of the proposed research will provide engineering contributions in signal processing and data quality analysis, and in the long-term be applied in the context of biomedical signal instrumentation and measurement. For example, automatic biomedical signal quality analysis methods will enable acquisition setups to be validated, alerting operators of issues and directing them on how to resolve these issues. It will also improve the performance of signal processing methods that extract information from these signals (e.g., increase accuracy of clinical decision support systems, reduction of false alarms). While this research is focused on biomedical data, the concepts and frameworks developed in this research are applicable to other data types.