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Publications using catch22
Ben Fulcher edited this page Mar 5, 2024
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This is a list of publications using catch22. Please get in touch by email if we've missed a paper of yours!
catch22 features have formed the basis of new algorithms:
- As a basis for developing semi-synthetic time series to understand algorithm performance π arXiv (2023).
- A new method to reduce the complexity of feature-based explanations. π Information Fusion (2023).
- The canonical interval time-series classifier. π IEEE International Conference on Big Data (2020).
catch22 features have been used in applications to:
- Classify hyperkinetic, tonic, and tonic-clonic seizures using unsupervised clustering of video signals. π Frontiers in Neurology (2023).
- Estimate pain intensity from physiological sensors. π arXiv (2023).
- Classify human exercises using wearable sensors and video data π Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2023).
- Classify advertising engagement using affect and physiological signals of heart rate, electrodermal activity, pupil dilation, and skin temperature π Sensors (2023).
- Video-based approach for human exercise classification π arXiv (2023).
- Determine the type of breathing using wireless sensors with a motion capture system π Algorithms (2023).
- Distinguishing types of human breathing from motion-capture data π Algorithms (2023).
- Automate General Movements Assessment for Cerebral Palsy from smartphone videos. π medRxiv (2023).
- Detect stress levels in real time from multiple physiological signals (heart rate, blood pressure, electrodermal activity, and respiration). π IEEE Access (2023).
- Comparison to ShapAAL method for sensor time-series classification. π PLoS ONE (2022).
- Extract markers of cardiometabolic disease risk from wearable device recordings. π Journal of Medical Internet Research (2022).
- Predict behavioral change from physiological signals. π Sensors (2022).
- Estimate objective pain intensity using catch22 features computed from physiological sensors, paving the way for developing a wearable pain measurement device. π PLoS ONE (2021).
- Predict the incidence trends of infectious diseases. BMC Bioinformatics (2024).
- Distinguish chemical stimuli from C. elegans chemosensory system recordings. π bioRxiv (2024).
- Predict when an individual patient can switch from intravenous to oral antibiotic treatment from routinely collected clinical parameters from over 10,000 intensive care unit stays. π Nature Communications (2024).
- Track Drosophila in real time for high-throughput behavioral phenotyping. π eLife (2023).
- Evaluate similarity of synthetically generated peripheral nerve signals π 10th International IEEE/EMBS Conference on Neural Engineering (NER) (2021).
- Demonstrate that oxygen consumption rate time-series measurements are highly predictive of cardiomyocytes differentiation outcome from human-induced pluripotent stem cells π Biotechnology and Bioengineering (2023).
- Detect dynamic electrical signatures of human breast cancer cells from voltage imaging. π Communcations Biology (2022).
- Screen for COVID-19 using holographic microscopy reconstructed red blood cells. π Biomedical Optics Express (2022).
- Understand influences on the dynamics of tree motion. π Biogeosciences (2021).
- Component identification for intelligent devices. π 2023 IEEE International Conference on Data Mining Workshops (ICDMW).
- Detect edge flag faults in wind turbines. π Wind Energ. Sci. Discuss. (2023).
- Compare the influence of imputation strategies for classfying household devices from electricity usage. π 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS) (2024).
- Active trailing edge flap system fault detection in wind turbines π Wind Energ. Sci. Discuss. (2023).
- Detect chemical analytes from chemiresistive hardware sensor arrays π arXiv (2023).
- Detect fraud from smart meters π Journal of Internet Services and Applications (2023).
- Appliance detection from very low-frequency smart meter time series π ResearchGate (2023).
- Track on-board diagnostics for monitoring engine operations. π Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS (2022).
- Detect anomalies in cloud services, where using catch22 resulted in the highest performance. π IoT (2022).
- Predict anode effects in aluminium production at least 1 min in advance from TRIMET Aluminium SE Essen (TAE) time-series data. π Applied Sciences (2021).
- EEG Signal Classification using Genetic Programming-based AutoML π Journal of Information and Data Management (2024).
- EEG classification using AutoML π Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) (2023).
- Infer rules underlying experience of subjective liking of artwork stimuli from EEG π PLoS ONE (2023).
- Identify methamphetamine users from EEG data π ResearchGate (2023).
- Generate long-term air temperature forecasts as part of explainable AI models. π Atmospheric Research (2023).
- Detect earthquakes. π arXiv (2022).
- Classify exoplanets using catch22 features extracted from their light curves. π Student dissertation (2019).
- As part of an algorithm for metalearning to predict movement of financial markets. π Journal of Information and Data Management (2024).
- Representations for time series in database management systems, such as Apache IoTDB, InfluxDB, OpenTSDB. π The VLDB Journal (2024).
- As part of a metalearning strategy for predicting market price movement π Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) (2023).
- Analyze US market price data. π arXiv (2023).
- Capture meaningful properties of financial time series (performance is lower than using domain features). π Working Papers REM.
- As a new algorithm fusing catch22 and the Matrix Profile, showing that the resulting the proposed C22MP is a state-of-the-art anomaly detector. π 2023 IEEE International Conference on Data Mining (ICDM) (2023).
- To compare performance on robust learning of noisy time-series collections. π arXiv (2024).
- As part of an improved deep forest model for time-series classification. π _ Neural Process Lett_ (2024).
- To understand dataset differences in evaluating foundation models for probabilistic time-series forecasting π arXiv (2023).
- Predict performance of time-series forecasting algorithms. π Expert Systems with Applications (2023)..
- Compare and cluster long time series π IEEE International Conference on Knowledge Graph (ICKG) (2022).
- Cluster long time series. π arXiv (2022).
- Track optimization trajectories for combinatorial optimization problems. π arXiv (2022).
- Extend shapelets for time-series classification. π Applied Sciences (2022).
- Perform meta-learning for time-series forecasting using 390 time-series features (including catch22). π IEEE Access (2021).
- Using catch22 features in sktime.