Yuanyi Ding, Yipeng Zhang, Chenda Duan, Atsuro Daida, Yun Zhang, Sotaro Kanai, Mingjian Lu, Shaun A Hussain, Richard J Staba, Hiroki Nariai, Vwani Roychowdhury
{"title":"PyHFO 2.0:基于深度学习的临床高频振荡分析的开源平台。","authors":"Yuanyi Ding, Yipeng Zhang, Chenda Duan, Atsuro Daida, Yun Zhang, Sotaro Kanai, Mingjian Lu, Shaun A Hussain, Richard J Staba, Hiroki Nariai, Vwani Roychowdhury","doi":"10.1088/1741-2552/ae10e0","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Approach: </strong>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.</p><p><strong>Main results: </strong>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.</p><p><strong>Significance: </strong>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.
.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PyHFO 2.0: an open-source platform for deep learning-based clinical high-frequency oscillations analysis.\",\"authors\":\"Yuanyi Ding, Yipeng Zhang, Chenda Duan, Atsuro Daida, Yun Zhang, Sotaro Kanai, Mingjian Lu, Shaun A Hussain, Richard J Staba, Hiroki Nariai, Vwani Roychowdhury\",\"doi\":\"10.1088/1741-2552/ae10e0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Approach: </strong>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.</p><p><strong>Main results: </strong>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.</p><p><strong>Significance: </strong>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.
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PyHFO 2.0: an open-source platform for deep learning-based clinical high-frequency oscillations analysis.
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.
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