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1. Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification

   https://doi.org/10.1109/bhi50953.2021.9508527  

   Le, Minh Duc; Singh Rathour, Vidhiwar; Truong, Quang Sang; Mai, Quan; Brijesh, Patel; Le, Ngan (July 2021, 2021 IEEE International Conference on Biomedical and Health Informatics (BHI))

   The automatic classification of electrocardiogram (ECG) signals has played an important role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs), particularly Convolutional Neural Networks (CNNs), have excelled in a variety of intelligent tasks including biomedical and health informatics. Most the existing approaches either partition the ECG time series into a set of segments and apply 1D-CNNs or divide the ECG signal into a set of spectrogram images and apply 2D-CNNs. These studies, however, suffer from the limitation that temporal dependencies between 1D segments or 2D spectrograms are not considered during network construction. Furthermore, meta-data including gender and age has not been well studied in these researches. To address those limitations, we propose a multi-module Recurrent Convolutional Neural Networks (RCNNs) consisting of both CNNs to learn spatial representation and Recurrent Neural Networks (RNNs) to model the temporal relationship. Our multi-module RCNNs architecture is designed as an end-to-end deep framework with four modules: (i) timeseries module by 1D RCNNs which extracts spatio-temporal information of ECG time series; (ii) spectrogram module by 2D RCNNs which learns visual-temporal representation of ECG spectrogram ; (iii) metadata module which vectorizes age and gender information; (iv) fusion module which semantically fuses the information from three above modules by a transformer encoder. Ten-fold cross validation was used to evaluate the approach on the MIT-BIH arrhythmia database (MIT-BIH) under different network configurations. The experimental results have proved that our proposed multi-module RCNNs with transformer encoder achieves the state-of-the-art with 99.14% F1 score and 98.29% accuracy. 

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2. Glitch subtraction with adaptive spline -- Part 1

   https://doi.org/10.5281/zenodo.7592442  

   Mohanty, Soumya D (January 2023, Zenodo)

   This dataset contains the data used in the paper (arXiv:2301.02398) on the estimation and subtraction of glitches in gravitational wave data using an adaptive spline fitting method called SHAPES . Each .zip file corresponds to one of the glitches considered in the paper. The name of the class to which the glitch belongs (e.g., "Blip") is included in the name of the corresponding .zip file (e.g., BLIP_SHAPESRun_20221229T125928.zip). When uncompressed, each .zip file expands to a folder containing the following. An HDF5 file containing the Whitened gravitational wave (GW) strain data in which the glitch appeared. The data has been whitened using a proprietary code. The original (unwhitened) strain data file is available from gwosc.org. The name of the original data file is the part preceding the token '__dtrndWhtnBndpss' in the name of the file.A JSON file containing information pertinent to the glitch that was analyzed (e.g., start and stop indices in the whitened data time series).A set of .mat  files containing segmented estimates of the glitch as described in the paper.  A MATLAB script, plotglitch.m, has been provided that plots, for a given glitch folder name, the data segment that was analyzed in the paper. Another script, plotshapesestimate.m, plots the estimated glitch. These scripts require the JSONLab package. 

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3. Inferring the astrophysical population of gravitational wave sources in the presence of noise transients

   https://doi.org/10.1093/mnras/stad1823  

   Heinzel, Jack; Talbot, Colm; Ashton, Gregory; Vitale, Salvatore (June 2023, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT The global network of interferometric gravitational wave (GW) observatories (LIGO, Virgo, KAGRA) has detected and characterized nearly 100 mergers of binary compact objects. However, many more real GWs are lurking sub-threshold, which need to be sifted from terrestrial-origin noise triggers (known as glitches). Because glitches are not due to astrophysical phenomena, inference on the glitch under the assumption it has an astrophysical source (e.g. binary black hole coalescence) results in source parameters that are inconsistent with what is known about the astrophysical population. In this work, we show how one can extract unbiased population constraints from a catalogue of both real GW events and glitch contaminants by performing Bayesian inference on their source populations simultaneously. In this paper, we assume glitches come from a specific class with a well-characterized effective population (blip glitches). We also calculate posteriors on the probability of each event in the catalogue belonging to the astrophysical or glitch class, and obtain posteriors on the number of astrophysical events in the catalogue, finding it to be consistent with the actual number of events included. 

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4. Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

   https://doi.org/10.5281/zenodo.5649211  

   Coughlin, Scott; Zevin, Michael; Bahaadini, Sara; Rohani, Neda; Allen, Sara; Berry, Christopher; Crowston, Kevin; Harandi, Mabi; Jackson, Corey; Kalogera, Vicky; et al (January 2021, Zenodo)

   {"Abstract":["This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (O1, O2 and O3, where O3 is split into O3a and O3b). Gravity Spy classified all noise events identified by the Omicron trigger pipeline in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made Omega scans of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.<\/p>\n\nThere are 22 classes used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are two additional classes used to classify O3 data.<\/p>\n\nFor O1 and O2, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle<\/p>\n\nFor O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Blip_Low_Frequency<\/strong>, Chirp, Extremely_Loud, Fast_Scattering<\/strong>, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle<\/p>\n\nIf you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool GWpy. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command<\/p>\n\n```conda create --name gravityspy-py38 -c conda-forge python=3.8 gwpy pandas psycopg2 sqlalchemy```<\/p>\n\nAfter downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.<\/p>\n\n```<\/p>\n\nfrom gwpy.table import GravitySpyTable<\/p>\n\nH1_O2 = GravitySpyTable.read('H1_O2.csv')<\/p>\n\nH1_O2[(H1_O2["ml_label"] == "Blip") & (H1_O2["ml_confidence"] > 0.9)]<\/p>\n\nH1_O2[0:4].download(nproc=1)<\/p>\n\n```<\/p>\n\nEach of the columns in the CSV files are taken from various different inputs: <\/p>\n\n[\u2018event_time\u2019, \u2018ifo\u2019, \u2018peak_time\u2019, \u2018peak_time_ns\u2019, \u2018start_time\u2019, \u2018start_time_ns\u2019, \u2018duration\u2019, \u2018peak_frequency\u2019, \u2018central_freq\u2019, \u2018bandwidth\u2019, \u2018channel\u2019, \u2018amplitude\u2019, \u2018snr\u2019, \u2018q_value\u2019] contain metadata about the signal from the Omicron pipeline. <\/p>\n\n[\u2018gravityspy_id\u2019] is the unique identifier for each glitch in the dataset. <\/p>\n\n[\u20181400Ripples\u2019, \u20181080Lines\u2019, \u2018Air_Compressor\u2019, \u2018Blip\u2019, \u2018Chirp\u2019, \u2018Extremely_Loud\u2019, \u2018Helix\u2019, \u2018Koi_Fish\u2019, \u2018Light_Modulation\u2019, \u2018Low_Frequency_Burst\u2019, \u2018Low_Frequency_Lines\u2019, \u2018No_Glitch\u2019, \u2018None_of_the_Above\u2019, \u2018Paired_Doves\u2019, \u2018Power_Line\u2019, \u2018Repeating_Blips\u2019, \u2018Scattered_Light\u2019, \u2018Scratchy\u2019, \u2018Tomte\u2019, \u2018Violin_Mode\u2019, \u2018Wandering_Line\u2019, \u2018Whistle\u2019] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity). <\/p>\n\n[\u2018ml_label\u2019, \u2018ml_confidence\u2019] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification. <\/p>\n\n[\u2018url1\u2019, \u2018url2\u2019, \u2018url3\u2019, \u2018url4\u2019] are the links to the publicly-available Omega scans for each glitch. \u2018url1\u2019 shows the glitch for a duration of 0.5 seconds, \u2018url2\u2019 for 1 seconds, \u2018url3\u2019 for 2 seconds, and \u2018url4\u2019 for 4 seconds.<\/p>\n\n```<\/p>\n\nFor the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.<\/p>\n\nFor detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. <\/p>"]} 

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5. A generalized deep learning model to detect and classify volcano seismicity

   https://doi.org/10.30909/vol/rjss1878  

   Fee, David; Tan, Darren; Lyons, John; Sciotto, Mariangela; Cannata, Andrea; Hotovec-Ellis, Alicia; Girona, Társilo; Wech, Aaron; Roman, Diana; Haney, Matthew; et al (January 2025, Volcanica)

   Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] (10.1029/2024JB029194) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network (VOISS-Net) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings. 

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