Novel Low Complexity Biomedical Signal Processing Techniques for Online Applications

Biomedical signal processing has become a very active domain of research nowadays. With the advent of portable monitoring devices, from accelerometer-enabled bracelets and smart-phones to more advanced vital sign tracking body area networks, this field has been receiving unprecedented attention. Indeed, portable health monitoring can help uncover the underlying dynamics of human health in a way that has not been possible before. Several challenges have emerged however, as these devices present key differences in terms of signal acquisition and processing in comparison with conventional methods. Hardware constraints such as processing power and limited battery capacity make most established techniques unsuitable and therefore, the need for low-complexity yet robust signal processing methods has appeared. Another issue that needs to be addressed is the quality of the signals captured by these devices. Unlike in clinical scenarios, in portable health monitoring subjects are constantly performing their daily activities. Moreover, signals maybe captured from unconventional locations and subsequently, be prone to perturbations. In order to obtain reliable measures from these monitoring devices, one needs to acquire dependable signal quality measures, to avoid false alarms. Indeed, hardware limitations and low-quality signals can greatly influence the performance of portable monitoring devices. Nevertheless, most devices offer simultaneous acquisition of multiple physiological parameters, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Through multi-modal signal processing the overall performance can be improved, for instance by deriving parameters such as heart rate estimation from the most reliable and uncontaminated source. This thesis is therefore, dedicated to propose novel low-complexity biomedical processing techniques for real-time/online applications. Throughout this dissertation, several bio-signals such as the ECG, PPG, and electroencephalogram (EEG) are investigated. %There is an emphasis on ECG processing techniques, as most of the bio-signals recorded today reflect information about the heart. The main contribution of this dissertation consists in two signal processing techniques: 1) a novel ECG QRS-complex detection and delineation technique, and 2) a short-term event extraction technique for biomedical signals. The former is based on a processing technique called mathematical morphology (MM), and adaptively uses subject QRS-complex amplitude- and morphological attributes for a robust detection and delineation. This method is generalized to intra-cardiac electrograms for atrial activation detection during atrial fibrillation. The second method, called the Relative-Energy algorithm, uses short- and long-term signal energies to highlight events of interest and discard unwanted activities. Collectively, the results obtained by these methods suggest that while presenting low-computational costs, they can efficiently and robustly extract biomedical events of interest. Using the relative energy algorithm, a continuous non-binary ECG signal quality index is presented. The ECG quality is determined by creating a cleaned-up version of the input ECG and calculating the correlation coefficient between the cleaned-up and the original ECG. The proposed quality index is fast and can be implemented online, making it suitable for portable monitoring scenarios

A Bayesian approach for seismic recurrence parameters estimation

Recurrence models apply historical seismicity information to seismic hazard analysis. These models that play an important role in the obtained hazard curve are determined by their parameters. Recurrence parameters estimation has some features that lie in missing-data problems category. Thus, the observed data cannot be used directly to estimate model parameters. Furthermore discussion about results reliability and probable conservatism is impossible. The present study aims at offering an approach for Gutenberg-Richter parameters (a and b-values) estimation and determine their variation. Applying the proposed method to analyses of the heterogeneous data sets of seismic catalog, one would calculate valid estimates for recurrence parameters. This method has the capability to reflect all known sources of variability. The results of the case study clearly demonstrate applicability and efficiency of the proposed method, which can easily be implemented not only in advanced but also in practical seismic hazard analyses

Applying b-value variation to seismic hazard analysis using closed-form joint probability distribution

Reliable use of b-value in log[N(M)]=a-bM is critical in seismicity comparisons, seismic hazard analysis, prediction and comparative mechanism studies. Since earthquakes and the b-value are considered as stochastic processes and random variable, respectively, applying the probability distribution of b-value is necessary in its temporal and spatial variations assessment. In this paper, we propose a novel method that employs the b-value uncertainty in probabilistic seismic hazard analysis using normal-exponential joint distribution function. To this end, we calculate b-value statistics based on bootstrap sampling of the seismic catalog. Our analytical and experimental evaluations show that the proposed joint distribution results in a more precise closed-form relation for the probabilistic seismic hazard analysis accurately reducing the hazard in comparison to conventional methods. The benefit of the proposed approach here is improving the ability of assessing the effectiveness of various seismic risk mitigation strategies and so, allocates the available resources more efficiently

A Bayesian approach for seismic recurrence parameters estimation

Recurrence models apply historical seismicity information to seismic hazard analysis. These models that play an important role in the obtained hazard curve are determined by their parameters. Recurrence parameters estimation has some features that lie in missing-data problems category. Thus, the observed data cannot be used directly to estimate model parameters. Furthermore discussion about results reliability and probable conservatism is impossible. The present study aims at offering an approach for Gutenberg-Richter parameters (a and b-values) estimation and determine their variation. Applying the proposed method to analyses of the heterogeneous data sets of seismic catalog, one would calculate valid estimates for recurrence parameters. This method has the capability to reflect all known sources of variability. The results of the case study clearly demonstrate applicability and efficiency of the proposed method, which can easily be implemented not only in advanced but also in practical seismic hazard analyses

AltitudeOmics: Baroreflex Sensitivity During Acclimatization to 5,260 m.

<b>Introduction:</b> Baroreflex sensitivity (BRS) is essential to ensure rapid adjustment to variations in blood pressure (BP). Little is known concerning the adaptive responses of BRS during acclimatization to high altitude at rest and during exercise. <b>Methods:</b> Twenty-one healthy sea-level residents were tested near sea level (SL, 130 m), the 1st (ALT1) and 16th day (ALT16) at 5,260 m using radial artery catheterization. BRS was calculated using the sequence method (direct interpretation of causal link between BP and heartrate). At rest, subjects breathed a hyperoxic mixture (250 mmHg O <sub>2</sub> , end tidal) to isolate the preponderance of CO <sub>2</sub> chemoreceptors. End-tidal CO <sub>2</sub> varied from 20 to 50 mmHg to assess peripheral chemoreflex. Rebreathing provoked incremental increase in CO <sub>2</sub> , increasing BP to assess baroreflex. During incremental cycling exercise to exhaustion, subjects breathed room air. <b>Results:</b> Resting BRS decreased in ALT1 which was exacerbated in ALT16. This decrease in ALT1 was reversible upon additional inspired CO <sub>2</sub> , but not in ALT16. BRS decrease during exercise was greater and occurred at lower workloads in ALT1 compared to SL. At ALT16, this decrease returned toward SL values. <b>Discussion/Conclusion:</b> This study is the first to report attenuated BRS in acute hypoxia, exacerbated in chronic hypoxia. In ALT1, hypocapnia triggered BRS reduction whilst in ALT16 resetting of chemoreceptor triggered BRS reduction. The exercise BRS resetting was impaired in ALT1 but normalized in ALT16. These BRS decreases indicate decreased control of BP and may explain deteriorations of cardiovascular status during exposure to high altitude

AltitudeOmics: Spontaneous Baroreflex Sensitivity During Acclimatization to 5,260 m: A Comparison of Methods

Baroreflex sensitivity (BRS) is essential to ensure rapid adjustment to variations in blood pressure (BP). Spontaneous baroreflex function can be assessed using continuous recordings of blood pressure. The goal of this study was to compare four methods for BRS quantification [the sequence, Bernardi's (BER), frequency and transfer function methods] to identify the most consistent method across an extreme range of conditions: rest and exercise, in normoxia, hypoxia, hypocapnia, and hypercapnia. Using intra-radial artery BP in young healthy participants, BRS was calculated and compared using the four methods in normoxia, acute and chronic hypoxia (terrestrial altitude of 5,260 m) in hypocapnia (hyperventilation), hypercapnia (rebreathing) and during ramp exercise to exhaustion. The sequence and BER methods for BRS estimation showed good agreement during the resting and exercise protocols, whilst the ultra- and very-low frequency bands of the frequency and transfer function methods were more discrepant. Removing respiratory frequency from the blood pressure traces affected primarily the sequence and BER methods and occasionally the frequency and transfer function methods. The sequence and BER methods contained more respiratory related information than the frequency and transfer function methods, indicating that the former two methods predominantly rely on respiratory effects of BRS. BER method is recommended because it is the easiest to compute and even though it tends to overestimate BRS compared to the sequence method, it is consistent with the other methods, whilst its interquartile range is the smallest

Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology

QRS complex detection in the electrocardiogram (ECG) has been extensively investigated over the last two decades. Still, some issues remain pending due to the diversity of QRS complex shapes and various perturbations, notably baseline drift. This is especially true for ECG signals acquired using wearable devices. Our study aims at extracting QRS complexes and their fiducial points using Mathematical Morphology (MM) with an adaptive structuring element, on a beat-to-beat basis. The structuring element is updated based on the characteristics of the previously detected QRS complexes for a more robust and precise detection. The MIT-BIH arrhythmia and Physionet QT databases were respectively used for assessing the detection performance of R-waves and other fiducial points. Furthermore, the proposed method was evaluated on a wearable-device dataset of ECGs during vigorous exercises. Results show comparable or better performance than the state-of-the-art with a 99.87% sensitivity and 0.22% detection error rate for the MIT-BIH arrhythmia database. Efficient extraction of QRS fiducial points was achieved against the Physionet QT database. On the wearable-device dataset, an improvement of more than 10% in QRS complex detection rate compared to classic approaches was obtained

Adaptive Mathematical Morphology for QRS Fiducial Points Detection in the ECG

Fixed structure Mathematical Morphology (MM) operators have been used to detect QRS complexes in the ECG. These schemes are limited by the arbitrary setting of threshold values. Our study aims at extracting QRS complex fiducial points using MM with an adaptive structuring element, on a beat-to-beat basis. The structuring element is updated based on the characteristics of the previously detected QRS complexes. The MIT-BIH arrhythmia and Physionet QT databases were respectively used for assessing the performance of R-waves and other fiducial points detection. Results show comparable or better performance than the state-of-the-art and an efficient extraction of Qand S-waves as well as onset and offset points of the QRS comple

A Novel Preprocessing Tool to Enhance ECG R-wave Extraction

Various approaches have been proposed to detect the R-waves in the ECG. From the derivative-based to more complicated wavelet transform methods, the main goal of these approaches is to extract the R-waves from the perturbations present in the ECG. Our study aims at proposing a simple preprocessing tool that suppresses perturbations and enhances the R-waves in the ECG. Using sliding windows, short- and long-term signal energies are calculated for each sample in the ECG. A coefficient signal is then created as the ratio between the corresponding short- and long-term energies. The enhanced ECG is then calculated by multiplying the coefficient signal and the original ECG. The MIT-BIH database was used for evaluation and the proposed method was tested against synthetic white and EMG noises. Using the proposed method as a preprocessing tool to the classic Pan-Tompkins approach lead to a significant decrease over the number of false positive and false negative QRS complexes, when synthetic noise is added to ECG