Seconds Of Signal Research Articles

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events.

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation.

Ventricular fibrillation (VF) signals are characterized by highly volatile and erratic electrical impulses, the analysis of which is difficult given the complex behavior of the heart rhythms in the left (LV) and right ventricles (RV), as sometimes shown in intracardiac recorded Electrograms (EGM). However, there are few studies that analyze VF in humans according to the simultaneous behavior of heart signals in the two ventricles. The objective of this work was to perform a spectral and a non-linear analysis of the recordings of 22 patients with Congestive Heart Failure (CHF) and clinical indication for a cardiac resynchronization device, simultaneously obtained in LV and RV during induced VF in patients with a Biventricular Implantable Cardioverter Defibrillator (BICD) Contak Renewal IVTM (Boston Sci.). The Fourier Transform was used to identify the spectral content of the first six seconds of signals recorded in the RV and LV simultaneously. In addition, measurements that were based on Information Theory were scrutinized, including Entropy and Mutual Information. The results showed that in most patients the spectral envelopes of the EGM sources of RV and LV were complex, different, and with several frequency peaks. In addition, the Dominant Frequency (DF) in the LV was higher than in the RV, while the Organization Index (OI) had the opposite trend. The entropy measurements were more regular in the RV than in the LV, thus supporting the spectral findings. We can conclude that basic stochastic processing techniques should be scrutinized with caution and from basic to elaborated techniques, but they can provide us with useful information on the biosignals from both ventricles during VF.

Fast magnitude determination using a single seismological station record implementing machine learning techniques

In this work a Support Vector Machine Regression (SVMR) algorithm is used to calculate local magnitude (Ml) using only five seconds of signal after the P wave onset of one three component seismic station. This algorithm was trained with 863 records of historical earthquakes, where the input regression parameters were an exponential function of the waveform envelope estimated by least squares and the maximum value of the observed waveform for each component in a single station. Ten-fold cross validation was applied for a normalized polynomial kernel obtaining the mean absolute error for different exponents and complexity parameters. The local magnitude (Ml) could be estimated with 0.19 units of mean absolute error. The proposed algorithm is easy to implement in hardware and may be used directly after the field seismological sensor to generate fast decisions at seismological control centers, increasing the possibility of having an effective reaction.

Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station

<p>Seismic event characterization is often accomplished using algorithms based only on information received at seismological stations located closest to the particular event, while ignoring historical data received at those stations. These historical data are stored and unseen at this stage. This characterization process can delay the emergency response, costing valuable time in the mitigation of the adverse effects on the affected population. Seismological stations have recorded data during many events that have been characterized by classical methods, and these data can be used as previous “knowledge” to train such stations to recognize patterns. This knowledge can be used to make faster characterizations using only one three-component broadband station by applying bio-inspired algorithms or recently developed stochastic methods, such as kernel methods. We trained a Support Vector Machine (SVM) algorithm with seismograph data recorded by INGEOMINAS’s National Seismological Network at a three-component station located near Bogota, Colombia. As input model descriptors, we used the following: (1) the integral of the Fourier transform/power spectrum for each component, divided into 7 windows of 2 seconds and beginning at the P onset time, and (2) the ratio between the calculated logarithm of magnitude (Mb) and epicentral distance. We used 986 events with magnitudes greater than 3 recorded from late 2003 to 2008.<br />The algorithm classifies events with magnitude-distance ratios (a measure of the severity of possible damage caused by an earthquake) greater than a background value. This value can be used to estimate the magnitude based on a known epicentral distance, which is calculated from the difference between P and S onset times. This rapid (< 20 seconds) magnitude estimate can be used for rapid response strategies.<br />The results obtained in this work confirm that many hypocentral parameters and a rapid location of a seismic event can be obtained using a few seconds of signal registered at a single station. A cascade scheme of SVMs or other appropriate algorithms can be used to completely classify an event in a very short time with acceptable accuracy using data from only one station.</p><p> </p><p><strong>Resumen</strong></p><p>Los algoritmos de determinación de parámetros hipocentrales empleados en la actualidad, se basan específicamente en la información recibida en las estaciones de monitoreo mas cercanas al epicentro y no tienen en cuenta la valiosa información histórica registrada a lo largo del tiempo en dichas estaciones. Es por esto que los procesos de caracterización toman varios minutos, tiempo precioso que podría ser de gran utilidad en la generación de alertas tempranas que permitan una oportuna reacción ante el evento. El registro de información, a lo largo el tiempo, de sismos ocurridos en los alrededores de la estación, puede ser empleada para dotarla de algún grado de experiencia que le permita, mediante detección y clasificación de patrones, realizar una caracterización previa mucho mas rápida, mediante el empleo de técnicas modernas las cuales pueden ser algoritmos bio-inspirados o métodos estocásticos mas recientes conocidos como métodos Kernel. En el presente trabajo se emplea un método conocido como Maquinas de Soporte Vectorial (MSV), entrenando dicho algoritmo con información de la relación del área bajo la curva de la potencia de la transformada de Fourier de las componentes N-S, E-W y Vertical, calculada para 5 ventanas de 2 segundos, desde la onda p, de 123 sismos de magnitud superior a 3, desde 2004 hasta 2008, alrededor de la estación El Rosal, de la Red Sismológica Nacional de Ingeominas. El Algoritmo clasifica sismos que superen un umbral predeterminado de la relación entre el Logaritmo de la magnitud y la distancia, que refleja, de alguna manera, la intensidad del sismo. Con la obtención de este parámetro será posible conocer la magnitud del evento, debido a que la distancia puede ser calculada, con base en picado de la onda S, y de esta manera establecer una aproximación rápida de la magnitud en un tiempo aproximado de 20 segundos después del evento. Los resultados obtenidos permiten confirmar que con poco tiempo se señal en una sola estación sismológica es posible obtener información confiable para ser empleada en alertas tempranas.</p>

The Potential for Earthquake Early Warning in Italy Using ElarmS

Abstract The new Italian National Seismic Network (INSN) is a dense network of broadband stations deployed for monitoring Italian seismicity. The network consists of 250 stations with a typical station spacing of ∼40 km. Earthquake early warning is the rapid detection of an event in progress, assessment of the hazard it poses, and transmission of a warning ahead of any significant ground motion. We explore the potential for using the INSN real-time network for the purpose of earthquake early warning. We run the ElarmS early warning methodology off-line using a data set of more than 200 events with magnitudes between 2.5 and 6.0. A scaling relation for magnitude determination from the dominant period of the first seconds of signal following the P onset is developed from the data set. The standard deviation in the magnitude estimates using this approach is 0.4 magnitude units, and all event magnitude estimates are within ±0.75 magnitude units of the true magnitude. Given the existing distribution of seismic stations it takes an average of 10 sec after event initiation before the P wave has been detected at four stations. If we require a detection at four stations before issuing the first alert, then the blind zone, within which no warning would be available, has a radius of ∼37 km. The ElarmS methodology can provide a warning earlier than this but with a greater uncertainty. An assessment of past damaging earthquakes across Italy shows that applying ElarmS with the existing seismic network could provide warning to population centers in repeats of past events. For example, in a repeat of the 1980 Irpinia earthquake Naples could receive an ∼15-sec warning. The variations in the size of the blind zone and warning times for different regions can be used as a guide to selecting strategic locations for future station deployments.

Earthquake magnitude estimation from peak amplitudes of very early seismic signals on strong motion records

We show that the low‐pass filtered, peak amplitudes of initial P‐ and S‐wave seismic signals recorded in the vicinity of an occurring earthquake source correlates with the earthquake magnitude and may be used for real‐time estimation of the event size in seismic early warning applications. The earthquake size can be therefore estimated using only a couple of seconds of signal from the P‐ or S‐wave onsets, i.e. while the rupture itself is still propagating and rupture dimension is far from complete. We argue that dynamic stress release and/or slip duration on the fault in the very early stage of seismic fracture, scales both with the observed peak amplitude and with the elastic energy available for fracture propagation. The probability that a fracture grows to a larger size should scale with the energy initially available.