Segmentation of depth-EEG seizure signals: Method based on a physiological parameter and comparative study

Application of singular spectrum-based change-point analysis to EMG-onset detection

While many approaches have been proposed to identify the signal onset in EMG recordings, there is no standardized method for performing this task. Here, we propose to use a change-point detection procedure based on singular spectrum analysis to determine the onset of EMG signals. This method is suitable for automated real-time implementation, can be applied directly to the raw signal, and does not require any prior knowledge of the EMG signal's properties. The algorithm proposed by Moskvina and Zhigljavsky (2003) was applied to EMG segments recorded from wrist and trunk muscles. Wrist EMG data was collected from 9 Parkinson's disease patients with and without tremor, while trunk EMG data was collected from 13 healthy able-bodied individuals. Along with the change-point detection analysis, two threshold-based onset detection methods were applied, as well as visual estimates of the EMG onset by trained practitioners. In the case of wrist EMG data without tremor, the change-point analysis showed comparable or superior frequency and quality of detection results, as compared to other automatic detection methods. In the case of wrist EMG data with tremor and trunk EMG data, performance suffered because other changes occurring in these signals caused larger changes in the detection statistic than the changes caused by the initial muscle activation, suggesting that additional criteria are needed to identify the onset from the detection statistic other than its magnitude alone. Once this issue is resolved, change-point detection should provide an effective EMG-onset detection method suitable for automated real-time implementation.

Evaluation of real-time QRS detection algorithms in variable contexts

A method is presented to evaluate the detection performance of real-time QRS detection algorithms to propose a strategy for the adaptive selection of ORS detectors, in variable signal contexts. Signal contexts are defined as different combinations of QRS morphologies and clinical noise. Four QRS detectors are compared in these contexts by means of a multivariate analysis. This evaluation strategy is general and can be easily extended to a larger number of detectors. A set of morphology contexts, corresponding to eight QRS morphologies (normal, PVC, premature atrial beat, paced beat, LBBB, fusion, RBBB, junctional premature beat), was extracted from 17 standard ECG records. For each morphology context, the set of extracted beats, ranging from 30 to 23000, was resampled to generate 50 realisations of 20 concatenated beats. These realisations were then used as input to the QRS detectors, without noise, and with three different types of additive clinical noise (electrode motion artifact, muscle artifact, baseline wander) at three signal-to-noise ratios (5 dB, -5 dB, -15 dB). Performance was assessed by the number of errors, which reflected both false alarms and missed beats. The results show that the evaluated detectors are indeed complementary. For example, the Pan-Tompkins detector is the best in most contexts but the Okada detector generates fewer errors in the presence of electrode motion artifact. These results will be particularly useful to the development of a real-time system that will be able to choose the best ORS detector according to the current context.

Tracking rhythmicity in nonstationary quasi-periodic biomedical signals using adaptive time-varying covariance

A time-varying covariance method for detecting and quantifying the evolution of rhythmicity (frequency) in persistently varying quasi-periodic nonstationary signals is presented. The basic method, evaluated using chirp signals, utilizes a shifting window of fixed length. A substantial reduction in estimation bias and variability are obtained by utilizing an adaptive window whose length is dependent on past frequency estimates. The adaptive window yields estimates that are comparable in accuracy to those obtained using high-resolution time-frequency representation but with lower computation requirements and the potential for on-line application. Finally, an example of the application of the method for analyzing a neural recording is also illustrated.

A method to quantify invariant information in depth-recorded epileptic seizures

In the field of epilepsy, the analysis of stereoelectroencephalographic (SEEG) signals recorded with depth electrodes provides major information on interactions between brain structures during seizures. A methodology of comparing SEEG seizure recordings is applied in 4 patients suffering from temporal lobe epilepsy. It proceeds in 3 steps: (i) segmentation of SEEG signals, (ii) characterization and labeling of segments and (iii) comparison of observations coded as sequences of symbol vectors. The third step is based on a vectorial extension of Wagner and Fischer's algorithm to first, quantify similarities between observations and second, extract invariant information, referred to as spatio-temporal signatures. These are automatically extracted by the algorithm without the need to make a priori assumptions on the 'patterns' to be searched for. Theoretical results show that two observations of non-equal duration can be matched by deforming the first one (using insertion/deletion operations on vectors) to optimally fit the second, under a minimal cost constraint. Clinical results show that the study brings objective results on reproducible mechanisms occurring during seizures: for a given patient, quantified descriptions of seizure periods are compared and similar ictal patterns, or signatures, are extracted from SEEG signals. Some of these signatures (particularly those containing spikes, spike-and-waves, slow waves and rapid discharges) are relevant: they seem to reflect reproducible propagation schemes whose analysis may help in the understanding of epileptogenic networks.