Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring

Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardiography data. They rely on discrete wavelet transform analysis using either biorthogonal 3.9 or reverse biorthogonal 3.9. The first method involves slicing chest vibrations for each cardiac activity, and then detecting the peak location, whereas the other method aims at detecting the peak directly from chest vibrations without segmentation. Performance evaluations were conducted on signals recorded from small children and adults based on missing and additional peaks. Both algorithms showed a low error rate (15.4% and 2.1% for children/infants and adults, respectively) for signals obtained in resting state. The average error for sitting and breathing tasks (adults only) was 14.4%. In summary, the first algorithm proved more promising for further exploration.

A Study of the Classification of Motor Imagery Signals using Machine Learning Tools

In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic label. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain representation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition.

Fluency-related Temporal Features and Syllable Prominence as Prosodic Proficiency Predictors for Learners of English with Different Language Backgrounds

Prosodic features are important in achieving intelligibility, comprehensibility, and fluency in a second or foreign language (L2). However, research on the assessment of prosody as part of oral proficiency remains scarce. Moreover, the acoustic analysis of L2 prosody has often focused on fluency-related temporal measures, neglecting language-dependent stress features that can be quantified in terms of syllable prominence. Introducing the evaluation of prominence-related measures can be of use in developing both teaching and assessment of L2 speaking skills. In this study we compare temporal measures and syllable prominence estimates as predictors of prosodic proficiency in non-native speakers of English with respect to the speaker’s native language (L1). The predictive power of temporal and prominence measures was evaluated for utterance-sized samples produced by language learners from four different L1 backgrounds: Czech, Slovak, Polish, and Hungarian. Firstly, the speech samples were assessed using the revised Common European Framework of Reference scale for prosodic features. The assessed speech samples were then analyzed to derive articulation rate and three fluency measures. Syllable-level prominence was estimated by a continuous wavelet transform analysis using combinations of F0, energy, and syllable duration. The results show that the temporal measures serve as reliable predictors of prosodic proficiency in the L2, with prominence measures providing a small but significant improvement to prosodic proficiency predictions. The predictive power of the individual measures varies both quantitatively and qualitatively depending on the L1 of the speaker. We conclude that the possible effects of the speaker’s L1 on the production of L2 prosody in terms of temporal features as well as syllable prominence deserve more attention in applied research and developing teaching and assessment methods for spoken L2.

Estudos de Variabilidades Interanuais e Interdecenais da Precipitação Pluvial das Regiões Hidrográficas Tocantins-Araguaia e São Francisco no Brasil

O objetivo é investigar as fases temporais das variabilidades de precipitação pluvial das Regiões Hidrográficas do Tocantins-Araguaia e São Francisco, como, também, correlacioná-las com índices de anomalias de Temperatura da Superfície do Mar (TSM) do Pacífico, na região do Niño 3.4, utilizando a análise de transformada ondaleta. A área geográfica está localizada entre os paralelos 0,5º S a 20º S e meridianos 34,8º W a 55,4º W. Foram utilizados dados mensais de precipitação observados e de reanálise (1º x 1º), no período de 1945-2016, e de TSM de 1950-2016 provenientes de órgãos governamentais nacionais e internacionais. As Ondaletas Contínuas mostraram que as variabilidades dominantes, de precipitação total anual, nas Regiões Hidrográficas do Tocantins-Araguaia e do São Francisco são nas escalas de três a cinco anos, de 11 a 12 anos e em torno de 22 anos. Para ambas as Regiões essas frequências estão em fases, pela Transformada Ondaleta Cruzada e confirmada pela Ondaleta Coerente. Nas análises de Ondaletas Cruzada e Coerente das precipitações com os índices oceânicos se verificou que houve avanço (135º) na série do Niño 3.4 em relação as das precipitações das Regiões nas escalas de três a cinco anos, mas foram verificadas diferenças de fase nas escalas decenais da precipitação das Regiões com os índices oceânicos. Concluiu-se que as variabilidades da precipitação de ambas as Regiões estão em fase e que os eventos ENOS influenciam nas precipitações das Regiões Hidrográficas do Tocantins-Araguaia e São Francisco. Studies of Interannual and Interdecennial Variabiliteis of Rainfall in the Tocantins-Araguaia and São Francisco Hydrographic Regions in Brazil ABSTRACTThe objective is to investigate the temporal phases of the variability of rainfall in the Hydrographic Regions of Tocantins-Araguaia and São Francisco, as well as to correlate them with anomalies indexes of the Sea Surface Temperature (SST) of the Pacific, in the Niño 3.4 region, using wavelet transform analysis. The geographical area is located between the parallels 0.5º S to 20º S and meridians 34.8º W to 55.4º W. We used monthly data of observed and reanalysis precipitation (1º x 1º), in the period from 1945 to 2016, and from 1950 to 2016 for SST. The data are from national and international government agencies. The continuous wavelet showed that the dominant variability of total annual precipitation, in the Hydrographic Regions of Tocantins-Araguaia and São Francisco, are in the frequencies of three to five years, 11 to 12 years and about 22 years. These frequencies are in phases by the cross wavelet transform and confirmed by the coherent wavelet. In the cross and coherent wavelet analysis of the precipitation with the oceanic indices, there was an advance (135º) in the Niño 3.4 series in relation to the precipitation of the Regions in the frequency of three to five years, but phase differences were observed in the decadal frequencies between the precipitation of the Regions and oceanic indices. We concluded that the variability of precipitation in both regions is in phase and that the ENOS events influence the rainfall in the Hydrographic Regions of Tocantins-Araguaia and São Francisco.Keywords: El Niño, hydrographic catchment, wavelet, climate variability.

Detection of geomagnetic disturbances with ionospheric calibration solutions of LOFAR astronomical observations

<p>Perturbation of an electromagnetic signal due to its passing through the Earth’s ionosphere is one of the limiting factors in obtaining high quality astronomical observations at low frequencies. Since the establishment of the Low Frequency Array (LOFAR) radio interferometer, which is operating  in the frequency range between 10  and 240 MHz, effort has been made in order to properly remove this effect during the calibration routine.</p><p>In this study we use differential TEC solutions obtained from calibration of Epoch of Reionization project’s observations and investigate their sensitivity to weak geomagnetic disturbances with wavelet transform analysis. Comparison to the different geomagnetic indices allows us to study the possible origin of medium scale ionospheric structures that have been detected.</p>