Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients

Farahmand, Sina; Sobayo, Tiwalade; Mogul, David J.

doi:10.1109/tnsre.2018.2881606

Cited by 21 publications

(13 citation statements)

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“…The EMD and instantaneous frequency analysis are a promising tool for neuroscience and electrophysiology (12). The EMD has successfully been applied to electrophysiology data in a range of contexts including (but not limited to) rodent hippocampal theta oscillations (20), epileptic activity in human patients (21), TMS evoked EEG responses (22) and for assessing instantaneous phase-synchrony in intracranial EEG (23). We build on this prior work to introduce phase-aligned instantaneous frequency as a general measure of oscillatory waveform shape.…”

Section: : Introductionmentioning

confidence: 99%

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

Quinn

Lopes‐dos‐Santos

Huang

et al. 2021

Journal of Neurophysiology

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Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. 'Normalised shapes' can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

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Section: : Introductionmentioning

confidence: 99%

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

Quinn

Lopes‐dos‐Santos

Huang

et al. 2021

Journal of Neurophysiology

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“…Computational modeling was described in our previous study to quantify and compare meanλ 1:60% value among three epileptic networks with different phase-synchrony level [27]. Simulation results demonstrated that as the phase-synchrony level increases between neuronal oscillators from its minimum to its maximum, the meanλ 1:60% value decreases accordingly.…”

Section: Dataset and Methodsmentioning

confidence: 99%

“…Furthermore, the normalized meanλ 1:60% values were measured to quantify phase-synchrony levels as seizures evolve in order to magnify the small relative changes of the meanλ 1:60% over time. It should be noted that phase-synchrony was observed to increase from mid-ictal towards seizure end and achieved its maximum level at seizure termination in all of the analyzed epilepsy patients [27].…”

Section: Dataset and Methodsmentioning

confidence: 99%

“…Therefore, it is critical to utilize non-linear methods to measure instantaneous phasesynchrony. In our previous study, an analytical methodology based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) was utilized to quantitatively evaluate the phase-synchrony dynamics in patients with temporal and frontal lobe epilepsy [27]. In this study, a reduction in the phase-synchrony was observed around seizure onset.…”

Section: Introductionmentioning

confidence: 99%

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Noise-Assisted MEMD-Based Phase-Connectivity Analysis to Personalize Closed-Loop DBS Therapy in Epilepsy Patients

Farahmand

Sobayo

Mogul

2019

Preprint

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Deep brain stimulation (DBS) is a treatment that has been explored for controlling seizures in patients with intractable epilepsy. Many clinical and pre-clinical studies using DBS therapy determine stimulation parameters through trial and error. The same stimulation parameters are often applied to the whole cohort, which consequently produces mixed results of responders and non-responders. In this paper, an adaptive non-linear analytical methodology is proposed to extract stimulation frequency and location(s) from endogenous brain dynamics of epilepsy patients, using phase-synchrony and phase-connectivity analysis, as seizures evolve. The proposed analytical method was applied to seizures recorded using depth electrodes implanted in hippocampus and amygdala in three patients. A reduction in phase-synchrony was observed in all patients around seizure onset. However, phase-synchrony started to gradually increase from mid-ictal and achieved its maximum level at seizure termination. This result suggests that hyper-synchronization of the epileptic network may be a crucial mechanism by which the brain naturally terminates seizure. Stimulation frequency and locations that matched the network phase-synchrony at seizure termination were extracted using phase-connectivity analysis. One patient with temporal lobe epilepsy (TLE) had a stimulation frequency ~15 Hz with the stimulation locations confined to the hippocampus. The other two patients with extra-temporal lobe epilepsy (ETE) had stimulation frequency ~90 Hz with at least one stimulation location outside of hippocampus. These results suggest that DBS parameters should vary based on the patient's underlying pathology. The proposed methodology provides an algorithm for tuning DBS parameters for individual patients in an effort to increase the clinical efficacy of the therapy.

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“…However, its de-noising ability depends on the selection of wavelet base function and the determination of threshold. Empirical Mode Decomposition (EMD) based de-noising method [11]- [13] applies EMD to decompose time series to multiple Intrinsic Mode Functions (IMFs) and a Residue (R). As the noise is concentrated in the high-frequency IMFs, only the noise reduction of the high-frequency IMFs is required.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting

et al. 2019

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Multivariate time series forecasting recently has received extensive attention with its wide application in finance, transportation, environment, and so on. However, few of the currently developed models have considered the impact of noise on prediction. Since multivariate time series contains multiple subsequences with strong nonlinear fluctuations, it is also difficult to obtain satisfactory prediction results. In this paper, aiming at improving prediction performance, we have proposed a novel ensemble threephase model called adaptive noise reducer-stacked auto-encoder-validating-AdaBoost-based long shortterm memory (ANR-SAE-VALSTM). We start with an introduction of a novel ANR for time series noise elimination. The SAEs are then used to extract features from the de-noised multivariate time series. Finally, we feed the de-noised features into the VALSTM to train an ensemble over-fitting prevention predictor. The proposed model is employed on the Beijing PM2.5 dataset and GEFCom2014 Electricity Price dataset. Compared with other popular models, the proposed model has achieved the best prediction performance in all prediction horizons. In addition, a careful ablation study is conducted to demonstrate the efficiency of our model design. INDEX TERMS Multivariate time series forecasting, adaptive noise reducer, stacked auto-encoders, long short-term memory, validating AdaBoost algorithm.

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Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients

Cited by 21 publications

References 44 publications

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

Noise-Assisted MEMD-Based Phase-Connectivity Analysis to Personalize Closed-Loop DBS Therapy in Epilepsy Patients

An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting

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