Efficient Schemes for Adaptive Frequency Tracking and their Relevance for EEG and ECG

Amplitude and frequency are the two primary features of one-dimensional signals, and thus both are widely utilized to analysis data in numerous fields. While amplitude can be examined directly, frequency requires more elaborate approaches, except in the simplest cases. Consequently, a large number of techniques have been proposed over the years to retrieve information about frequency. The most famous method is probably power spectral density estimation. However, this approach is limited to stationary signals since the temporal information is lost. Time-frequency approaches were developed to tackle the problem of frequency estimation in non-stationary data. Although they can estimate the power of a signal in a given time interval and in a given frequency band, these tools have two drawbacks that make them less valuable in certain situations. First, due to their interdependent time and frequency resolutions, improving the accuracy in one domain means decreasing it in the other one. Second, it is difficult to use this kind of approach to estimate the instantaneous frequency of a specific oscillatory component. A solution to these two limitations is provided by adaptive frequency tracking algorithms. Typically, these algorithms use a time-varying filter (a band-pass or notch filter in most cases) to extract an oscillation, and an adaptive mechanism to estimate its instantaneous frequency. The main objective of the first part of the present thesis is to develop such a scheme for adaptive frequency tracking, the single frequency tracker. This algorithm compares favorably with existing methods for frequency tracking in terms of bias, variance and convergence speed. The most distinguishing feature of this adaptive algorithm is that it maximizes the oscillatory behavior at its output. Furthermore, due to its specific time-varying band-pass filter, it does not introduce any distortion in the extracted component. This scheme is also extended to tackle certain situations, namely the presence of several oscillations in a single signal, the related issue of harmonic components, and the availability of more than one signal with the oscillation of interest. The first extension is aimed at tracking several components simultaneously. The basic idea is to use one tracker to estimate the instantaneous frequency of each oscillation. The second extension uses the additional information contained in several signals to achieve better overall performance. Specifically, it computes separately instantaneous frequency estimates for all available signals which are then combined with weights minimizing the estimation variance. The third extension, which is based on an idea similar to the first one and uses the same weighting procedure as the second one, takes into account the harmonic structure of a signal to improve the estimation performance. A non-causal iterative method for offline processing is also developed in order to enhance an initial frequency trajectory by using future information in addition to past information. Like the single frequency tracker, this method aims at maximizing the oscillatory behavior at the output. Any approach can be used to obtain the initial trajectory. In the second part of this dissertation, the schemes for adaptive frequency tracking developed in the first part are applied to electroencephalographic and electrcardiographic data. In a first study, the single frequency tracker is used to analyze interactions between neuronal oscillations in different frequency bands, known as cross-frequency couplings, during a visual evoked potential experiment with illusory contour stimuli. With this adaptive approach ensuring that meaningful phase information is extracted, the differences in coupling strength between stimuli with and without illusory contours are more clearly highlighted than with traditional methods based on predefined filter-banks. In addition, the adaptive scheme leads to the detection of differences in instantaneous frequency. In a second study, two organization measures are derived from the harmonic extension. They are based on the power repartition in the frequency domain for the first one and on the phase relation between harmonic components for the second one. These measures, computed from the surface electrocardiogram, are shown to help predicting the outcome of catheter ablation of persistent atrial fibrillation. The proposed adaptive frequency tracking schemes are also applied to signals recorded in the field of sport sciences in order to illustrate their potential uses. To summarize, the present thesis introduces several algorithms for adaptive frequency tracking. These algorithms are presented in full detail and they are then applied to practical situations. In particular, they are shown to improve the detection of coupling mechanisms in brain activity and to provide relevant organization measures for atrial fibrillation

Embedded Deep Learning for Sleep Staging

The rapidly-advancing technology of deep learning (DL) into the world of the Internet of Things (IoT) has not fully entered in the fields of m-Health yet. Among the main reasons are the high computational demands of DL algorithms and the inherent resource-limitation of wearable devices. In this paper, we present initial results for two deep learning architectures used to diagnose and analyze sleep patterns, and we compare them with a previously presented hand-crafted algorithm. The algorithms are designed to be reliable for consumer healthcare applications and to be integrated into low-power wearables with limited computational resources

ECG periodic components as a promising tool for complexity assessment during stepwise ablation of atrial fibrillation

Introduction: Stepwise radiofrequency catheter ablation (step-CA) has become a treatment of choice for the restoration of sinus rhythm (SR) in patients (pts) with long-standing persistent atrial fibrillation (pers-AF). Its success rate appears limited as the amount of ablation to achieve long term SR is unknown. Recently, intracardiac organization indices (OI) of AF have been used to track the efficiency of step-CA, with limited success. Our study is aimed at developing new OIs based on the relationships between harmonic components of atrial activity from the surface ECG as a global assessment of AF complexity and organization during step-CA. Methods: 3 pts with pers-AF (age 62, AF duration 17 months) underwent a step-CA. An adaptive tracking algorithm was developed for estimating the instantaneous frequency of atrial activity on chest lead V1 (after QRST subtraction) and for extracting its fundamental and harmonic components. An adaptive organization index (AOI) was computed as the ratio between the power of the extracted components and the total power of the signal to evaluate the temporal evolution of AF oscillations. The variance of the phase difference (PD) between the fundamental and harmonic components was used for measuring AF regularity. Results: Step-CA terminated 2/3 pers-AF into flutter. Importantly, in the 2 terminated pts, the AOI did not show any significant change during the step-CA (from pre-ablation to CFAE), while the PD showed a gradual reduction suggestive of increased coupling between the fundamental and the 1st harmonic. See figure. Conclusions: The PD and the AOI as measurements of complexity from the surface ECG appear as promising methods for tracking the effect of step-CA on global AF organization. This, however, needs to be validated on a larger population

A New Method for ECG Tracking of Persistent Atrial Fibrillation Termination during Stepwise Ablation

Stepwise radiofrequency catheter ablation (step-CA) has become the treatment of choice for the restoration of sinus rhythm (SR) in patients with long-standing persistent atrial fibrillation (pers-AF). Its success rate appears limited as the amount of ablation to achieve long term SR is unknown. Multiple organization indexes (OIs) have been previously developed to track the organization of AF during step-CA, however, with limited success. We report an adaptive method for tracking AF termination (AF-term) based on OIs characterizing the relationship between harmonic components of atrial activity from the surface ECG of AF activity. By computing their relative evolution during the last two steps preceding AF-term, we found that the performance of our OIs was superior to classical indices to track the efficiency of step-CA “en route” to AF-term. Our preliminary results suggest that the gradual synchronization between the fundamental and its first harmonic of AF activity appears as a promising parameter for predicting AF-term during step-CA

Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation

This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG). Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N 20) or long-standing AF (N 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9 were obtained, with a predictive value for long-standing AF of 93.1. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG. Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings

Harmonic frequency tracking algorithm for predicting the success of pharmacological cardioversion of atrial fibrillation

Purpose: Non-invasively predicting the success of pharmacological cardioversion for patients with atrial fibrillation (AF) would be of great interest in clinical settings. In this study, an adaptive algorithm for tracking fundamental and harmonic components of atrial activity is proposed for extracting waveform features from surface ECG and discriminate patients with successful or failed cardioversion. Methods: 49 patients diagnosed with AF for whom pharmacological cardioversion was a success (15) or a failure (34) were studied. An adaptive tracking algorithm was applied to precordial ECG leads (V1-V6) for estimating the instantaneous frequency as well as extracting fundamental and first harmonic components with time-varying band-pass filters. Joint tracking on pairs of leads improved robustness. The phase difference between fundamental and harmonic signals was used as a measure of AF organization. Successful and failed cardioversions were classified with quadratic discriminant analysis based on the mean and variance of instantaneous AF frequency, and on the mean and variance of the phase difference slope. Results: The best selection of features for classifying successful and failed cardioversions achieved a correct rate of 81.6% with balanced sensitivity and specificity, using the mean instantaneous frequency and the mean and variance of phase difference slope estimated from leads V1 and V4. In this case, the negative predictive value was 90.3%, meaning that cardioversion failure could be predicted with high reliability. Conclusions: Adaptive tracking of AF harmonic components could potentially be used as a diagnostic tool for the assessment of future cardioversion efficacy. Indeed, an accurate prediction of future cardioversion failure could help tailoring treatments to appropriate options