![](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F6529056%2Ff49f6ba667db1e9e5d928400a8c5098c%2FScreenshot%202024-02-16%20175851.png?generation=1708093806400116&alt=media\")
I would like to introduce a paper that extensively compares multiple state-of-the-art models and signal feature extractors. The Figure 2 of the paper illustrates various methods of feature extraction. I hope this article proves useful to you. 🙂
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Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
\nElectroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for ondevice applications. In this work, for the first time, we extensively compare multiple state-ofthe-art models and signal feature extractors in a real time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models.
\n", "messages": [ { "id": 2654907, "postDate": "2024-02-16T14:47:36.797Z", "content": "I would like to introduce a paper that extensively compares multiple state-of-the-art models and signal feature extractors. The Figure 2 of the paper illustrates various methods of feature extraction. I hope this article proves useful to you. 🙂
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Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
\nElectroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for ondevice applications. In this work, for the first time, we extensively compare multiple state-ofthe-art models and signal feature extractors in a real time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models.
\n", "rawMarkdown": "I would like to introduce a paper that extensively compares **multiple state-of-the-art models and signal feature extractors**. The Figure 2 of the paper illustrates various methods of feature extraction. I hope this article proves useful to you. 🙂\n\n\n\n## Title\nReal-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting\n\n## Abstract\nElectroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for ondevice applications. In this work, for the first time, we extensively compare multiple state-ofthe-art models and signal feature extractors in a real time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models.\n\n[Link to the paper on arXiv](https://arxiv.org/pdf/2201.08780.pdf)", "votes": 10 } ], "comments": [], "raw_markdown_by_id": { "2654907": "I would like to introduce a paper that extensively compares **multiple state-of-the-art models and signal feature extractors**. The Figure 2 of the paper illustrates various methods of feature extraction. I hope this article proves useful to you. 🙂\n\n\n\n## Title\nReal-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting\n\n## Abstract\nElectroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for ondevice applications. In this work, for the first time, we extensively compare multiple state-ofthe-art models and signal feature extractors in a real time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models.\n\n[Link to the paper on arXiv](https://arxiv.org/pdf/2201.08780.pdf)" } }