PyHFO 2.0: an open-source platform for deep learning-based clinical high-frequency oscillations analysis.

IF 3.8
Yuanyi Ding, Yipeng Zhang, Chenda Duan, Atsuro Daida, Yun Zhang, Sotaro Kanai, Mingjian Lu, Shaun A Hussain, Richard J Staba, Hiroki Nariai, Vwani Roychowdhury
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引用次数: 0

Abstract

Objective: Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly important for identifying epileptogenic zones in patients with drug-resistant epilepsy. However, few open-source platforms offer both state-of-the-art computational methods and user-friendly interfaces to support practical clinical use.

Approach: We present PyHFO 2.0, an enhanced open-source, Python-based platform that extends previous work by incorporating a more comprehensive set of detection methods and deep learning tools for HFO analysis. The platform now supports three commonly used detectors: Short-Term Energy (STE), Montreal Neurological Institute (MNI), and a newly integrated Hilbert transform-based detector. For HFO classification, PyHFO 2.0 includes deep learning models for artifact rejection, spike high-frequency oscillation (spkHFO) detection, and identification of epileptogenic HFOs (eHFOs). These models are integrated with the Hugging Face ecosystem for automatic loading and can be replaced with custom-trained alternatives. An interactive annotation module enables clinicians and researchers to inspect, verify, and reclassify events.

Main results: All detection and classification modules were evaluated using clinical EEG datasets, supporting the applicability of the platform in both research and translational settings. Validation across multiple datasets demonstrated close alignment with expert-labeled annotations and standard tools such as RIPPLELAB.

Significance: PyHFO 2.0 aims to simplify the use of computational neuroscience tools in both research and clinical environments by combining methodological rigor with a user-friendly graphical interface. Its scalable architecture and model integration capabilities support a range of applications in biomarker discovery, epilepsy diagnostics, and clinical decision support, bridging advanced computation and practical usability. .

PyHFO 2.0:基于深度学习的临床高频振荡分析的开源平台。
目的:脑电图(EEG)记录中高频振荡(HFOs)的准确检测和分类对于识别耐药癫痫患者的致痫区越来越重要。然而,很少有开源平台同时提供最先进的计算方法和用户友好的界面来支持实际的临床应用。方法:我们提出了PyHFO 2.0,这是一个增强的开源,基于python的平台,通过合并一套更全面的检测方法和用于HFO分析的深度学习工具,扩展了以前的工作。该平台现在支持三种常用的探测器:短期能量(STE)、蒙特利尔神经学研究所(MNI)和一种新集成的基于希尔伯特变换的探测器。对于HFO分类,PyHFO 2.0包括用于伪迹抑制、峰值高频振荡(spkHFO)检测和癫痫性HFO (ehfo)识别的深度学习模型。这些模型与hug Face生态系统集成,可自动加载,并可替换为定制训练的替代品。交互式注释模块使临床医生和研究人员能够检查,验证和重新分类事件。主要结果:所有检测和分类模块均使用临床脑电图数据集进行评估,支持该平台在研究和转化环境中的适用性。跨多个数据集的验证证明了与专家标记注释和标准工具(如RIPPLELAB)的紧密一致性。意义:PyHFO 2.0旨在通过将严谨的方法论与友好的图形界面相结合,简化计算神经科学工具在研究和临床环境中的使用。其可扩展的架构和模型集成功能支持生物标志物发现、癫痫诊断和临床决策支持等一系列应用,将先进的计算和实际可用性连接起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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