Absence Seizure Detection Algorithm for Portable EEG Devices

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

Pressure pulsation investigation in an electrical submersible pump based on Morlet continuous wavelet transform

Electrical Submersible Pumps (ESP) are one of the most reliable and efficient ways to lift oil or water from the ground or deep-sea to the surface. How to reduce the pressure pulsation and increase reliability is a challenging issue in the ESP design processes. In this study, a typical three-stage ESP model was selected as the research object. Based on numerical calculations and validation tests, the flow-field distribution mechanism within the dynamic and static interference zones of multi-stage ESP was investigated. Meanwhile, the inter-stage variability of pressure pulsation characteristics within the main hydraulic components was explored by Morlet continuous wavelet transform. The results showed that the numerical predicted performance has an excellent agreement with the experimental results, which confirms the accuracy of the numerical calculations. The time-domain characteristics of pressure pulsation at each monitoring location within the ESP showed high disorder due to the inter-stage propagation and coupling of the pressure pulsations. The low-frequency signal in the pressure pulsation signal had not only a cascading superposition of intensity, but also a significant phase difference. It was found that the main form of propagation between pulsating signal levels is the low-frequency signal. This work may facilitate the reduction or control of the pressure pulsations and thus improve the operation stability of ESP.

MULTISCALE ENVIRONMENTAL MONITORING IN URBAN TOPOCLIMATE

Climate is a complex system subject to much natural and anthropogenic interference. Urban microclimates and topoclimates need effective methodologies to localize coexisting phenomena, including multiscale approaches that demonstrate their relational character. Air temperature, air relative humidity, and wind are microclimatic variables that can be evaluated with respect to their qualitative energy flow characteristics. The technical performance of this monitoring depends on a good estimate of the statistics of the digital signal assessed, which in turn represents greater care in the management and implementation of structural change indicators derived from frequency-domain modeling. Therefore, this study aimed to analyze the power spectra of the air temperature, air relative humidity and wind data series using the traditional Morlet Continuous Wavelet Transform (CWT) and the Cross Wavelet Transform (XWT), with Wavelet Coherence (WC) analysis. The daily, monthly and annual scales were studied, and the results demonstrated significant interrelationships between the variables. The WC values found were validated by the Monte Carlo method, scalograms and spectrograms, representing seasonal and daily cycle frequencies. The urban fractions presented behavior in accordance with the seasonality of tropical climates of the Cerrado, as well as of interference of the urban roughness. The CWT, XWT and WC tools are adequate for urban monitoring and planning analyses, qualitatively reflecting the urban dynamics of microenvironments

Chirp analyzer for estimating amplitude and latency of steady-state auditory envelope following responses

The envelope following response (EFR) is a scalp-recorded evoked potential elicited by carrier tones or noise, modulated in amplitude with a continuous sweep of modulation frequencies. This non-stationary response reflects the phase-locked neural activity of the auditory pathway to the temporal envelope of sounds and has been commonly assessed by fixed-frequency methods based on the discrete Fourier transform, such as the Fourier Analyzer (FA). In this work, we study the estimation of the EFR with the use of explicit time-frequency methods, which offer more information about the energy distribution of the recorded signal, such as the Short-Term Fourier Transform (STFT) and the Morlet Continuous Wavelet Transform (CWT). We further introduce the Chirp Analyzer (CA), which is similar to FA, but using as basis function the same linear chirp that amplitude-modulates the carrier stimulus. In a direct comparison using controlled simulated responses, the CA showed to be able to estimate the correct EFR amplitudes, without the typical bias offered by the estimation using STFT (equivalent to FA) and more robust to noise than the CWT method, although with higher sensitivity to the presence of a delay in the response with respect to the stimulus. For addressing the latter issue, we also propose here a novel methodology for estimating the apparent latency of the response. This method proved to be reliable when using the STFT and the CA methods, as assessed using simulated responses. The estimation of the EFR amplitude with any of the methods, but especially with CA, should be corrected by using the estimated delay when possible. An illustrative application of these methods to small datasets of a rat and a human newborn, suggested that all time-frequency methods can be used to study the EFR amplitudes in a wide range of modulation frequencies, but they should be interpreted in the light of the limitations shown in the simulation studies.

Relative acoustic impedance from wavelet transform

Most deconvolution algorithms try to transform the seismic wavelet into spikes by designing inverse filters that remove an estimated seismic wavelet from seismic data. We assume that seismic trace subtle discontinuities are associated with acoustic impedance contrasts and can be characterized by wavelet transform spectral ridges, also called modulus maxima lines (WTMML), allowing us to improve seismic resolution by using the wavelet transform. Specifically, we apply the complex Morlet continuous wavelet transform (CWT) to each seismic trace and compute the WTMMLs. Then, we reconstruct the seismic trace with the inverse continuous wavelet transform from the computed WTMMLs with a broader band complex Morlet wavelet than that used in the forward CWT. Because the reconstruction process preserves amplitude and phase along different scales, or frequencies, the result looks like a deconvolution method. Considering this high-resolution seismic representation as a reflectivity approximation, we estimate the relative acoustic impedance (RAI) by filtering and trace integrating it. Conventional deconvolution algorithms assume the seismic wavelet to be stochastic, while the CWT is implicitly time varying such that it can be applied to both depth and time-domain data. Using synthetic and real seismic data, we evaluated the effectiveness of the methodology on detecting seismic events associated with acoustic impedance changes. In the real data examples, time and in-depth RAI results, show good correlation with real P-impedance band-pass data computed using more rigorous commercial inversion software packages that require well logs and low-frequency velocity model information.