Tag Archives: Change Detection

Modelling ECGs with sums of gaussians and estimating them through switching Kalman Filters and the likelihood of each mode

Oster, J.; Behar, J.; Sayadi, O.; Nemati, S.; Johnson, A.E.W.; Clifford, G.D., Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters, in Biomedical Engineering, IEEE Transactions on , vol.62, no.9, pp.2125-2134, Sept. 2015, DOI: 10.1109/TBME.2015.2402236.

Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue to increase in prevalence, driven partly by the ease of remote monitoring technology usage, the need to automate ECG analysis continues to grow. In previous studies, a model-based ECG filtering approach to ECG data from healthy subjects has been applied to facilitate accurate online filtering and analysis of physiological signals. We propose an extension of this approach, which models not only normal and ventricular heartbeats, but also morphologies not previously encountered. A switching Kalman filter approach is introduced to enable the automatic selection of the most likely mode (beat type), while simultaneously filtering the signal using appropriate prior knowledge. Novelty detection is also made possible by incorporating a third mode for the detection of unknown (not previously observed) morphologies, and denoted as X-factor. This new approach is compared to state-of-the-art techniques for the ventricular heartbeat classification in the MIT-BIH arrhythmia and Incart databases. F1 scores of 98.3% and 99.5% were found on each database, respectively, which are superior to other published algorithms’ results reported on the same databases. Only 3% of all the beats were discarded as X-factor, and the majority of these beats contained high levels of noise. The proposed technique demonstrates accurate beat classification in the presence of previously unseen (and unlearned) morphologies and noise, and provides an automated method for morphological analysis of arbitrary (unknown) ECG leads.

On quickest change detection when the process is modelled with HMMs

Cheng-Der Fuh; Yajun Mei, Quickest Change Detection and Kullback-Leibler Divergence for Two-State Hidden Markov Models, in Signal Processing, IEEE Transactions on , vol.63, no.18, pp.4866-4878, Sept.15, 2015 DOI: 10.1109/TSP.2015.2447506

In this paper, the quickest change detection problem is studied in two-state hidden Markov models (HMM), where the vector parameter θ of the HMM changes from θ0 to θ1 at some unknown time, and one wants to detect the true change as quickly as possible while controlling the false alarm rate. It turns out that the generalized likelihood ratio (GLR) scheme, while theoretically straightforward, is generally computationally infeasible for the HMM. To develop efficient but computationally simple schemes for the HMM, we first discuss a subtlety in the recursive form of the generalized likelihood ratio (GLR) scheme for the HMM. Then we show that the recursive CUSUM scheme proposed in Fuh (Ann. Statist., 2003) can be regarded as a quasi-GLR scheme for pseudo post-change hypotheses with certain dependence structure between pre- and postchange observations. Next, we extend the quasi-GLR idea to propose recursive score schemes in the scenario when the postchange parameter θ1 of the HMM involves a real-valued nuisance parameter. Finally, the Kullback-Leibler (KL) divergence plays an essential role in the quickest change detection problem and many other fields, however it is rather challenging to numerically compute it in HMMs. Here we develop a non-Monte Carlo method that computes the KL divergence of two-state HMMs via the underlying invariant probability measure, which is characterized by the Fredholm integral equation. Numerical study demonstrates an unusual property of the KL divergence for HMM that implies the severe effects of misspecifying the postchange parameter for the HMM.

The problem of monitoring events that can only be predicted stochastically, applied to mobile sensors for monitoring

Jingjin Yu; Karaman, S.; Rus, D., Persistent Monitoring of Events With Stochastic Arrivals at Multiple Stations, Robotics, IEEE Transactions on , vol.31, no.3, pp.521,535, June 2015, DOI: 10.1109/TRO.2015.2409453.

This paper introduces a new mobile sensor scheduling problem involving a single robot tasked to monitor several events of interest that are occurring at different locations (stations). Of particular interest is the monitoring of transient events of a stochastic nature, with applications ranging from natural phenomena (e.g., monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities (e.g., monitoring early formations of traffic congestion using an aerial robot). Motivated by examples like these, this paper focuses on problems in which the precise occurrence times of the events are unknown apriori, but statistics for their interarrival times are available. In monitoring such events, the robot seeks to: (1) maximize the number of events observed and (2) minimize the delay between two consecutive observations of events occurring at the same location. This paper considers the case when a robot is tasked with optimizing the event observations in a balanced manner, following a cyclic patrolling route. To tackle this problem, first, assuming that the cyclic ordering of stations is known, we prove the existence and uniqueness of the optimal solution and show that the solution has desirable convergence rate and robustness. Our constructive proof also yields an efficient algorithm for computing the unique optimal solution with O(n) time complexity, in which n is the number of stations, with O(log n) time complexity for incrementally adding or removing stations. Except for the algorithm, our analysis remains valid when the cyclic order is unknown. We then provide a polynomial-time approximation scheme that computes for any ε > 0 a (1 + ε)-optimal solution for this more general, NP-hard problem.

Estimating the bandwidth of a communication channel for adjusting the bitrate in high-definition video streaming, using Pareto and Gamma distributions (that are conjugate) in a bayesian estimation framework

Javadtalab, A.; Semsarzadeh, M.; Khanchi, A.; Shirmohammadi, S.; Yassine, A., Continuous One-Way Detection of Available Bandwidth Changes for Video Streaming Over Best-Effort Networks, Instrumentation and Measurement, IEEE Transactions on , vol.64, no.1, pp.190,203, Jan. 2015. DOI: 10.1109/TIM.2014.2331423

Video streaming over best-effort networks, such as the Internet, is now a significant application used by most Internet users. However, best-effort networks are characterized by dynamic and unpredictable changes in the available bandwidth, which adversely affect the quality of video. As such, it is important to have real-time detection mechanisms of bandwidth changes to ensure that video is adapted to the available bandwidth and transmitted at the highest quality. In this paper, we propose a Bayesian instantaneous end-to-end bandwidth change prediction model and method to detect and predict one-way bandwidth changes at the receiver. Unlike existing congestion detection mechanisms, which use network parameters such as packet loss probability, round trip time (RTT), or jitter, our approach uses weighted interarrival time of video packets at the receiver side. Furthermore, our approach is continuous, since it measures available bandwidth changes with each incoming video packet, and therefore detects congestion occurrence in <200 ms, on average, which is significantly faster than existing approaches. In addition, it is a one-way scheme, since it only takes into account the characteristics of the incoming path and not the outgoing path, as opposed to other approaches, which use RTT and are hence less accurate. In this paper, we provide extensive experimental simulations and real-world network implementation. Our results indicate that the proposed detection method is superior to existing solutions.