A Novel Methodology for Intracranial Pressure Subpeak Identification Enabling Morphological Feature Analysis.

IF 4.4 2区医学 Q2 ENGINEERING, BIOMEDICAL

IEEE Transactions on Biomedical Engineering Pub Date : 2024-11-11 DOI:10.1109/TBME.2024.3495542

Varun Vinayak Kalaiarasan, Marcella Miller, Xu Han, Brandon Foreman, Xiaodong Jia

{"title":"A Novel Methodology for Intracranial Pressure Subpeak Identification Enabling Morphological Feature Analysis.","authors":"Varun Vinayak Kalaiarasan, Marcella Miller, Xu Han, Brandon Foreman, Xiaodong Jia","doi":"10.1109/TBME.2024.3495542","DOIUrl":null,"url":null,"abstract":"Objective: The objective of this study is to propose a novel methodology for intracranial pressure (ICP) waveform subpeak identification by incorporating arterial blood pressure (ABP) and electrocardiogram (ECG) signals from patients who have undergone traumatic brain injury (TBI).Methods: This approach consisted of 1) multimodal signal pre-processing and initial manual ICP waveform morphology labeling, 2) semi-supervised training of a support vector machine (SVM) ICP waveform morphological classifier, and 3) a dynamic time warping barycenter averaging (DBA) based ICP waveform template generation and derivative dynamic time warping (DDTW)-driven ICP waveform subpeak mapping from template to incoming processed waveforms.Results: This proposed framework was evaluated on 30,000 ICP waveforms and resulted in an overall subpeak identification accuracy score of 98.2%.Conclusion: The results showcased an improvement over existing methodologies and showed resilience to variations in ICP waveform morphologies from patient to patient due to the incorporation of a subject matter expert (SME) to accommodate new and unseen ICP morphologies.Significance: The robustness of this comprehensive approach enabled the analysis of ICP morphological features over time to provide clinicians with crucial insights regarding the development of secondary pathologies in patients and facilitate monitoring their physiological state.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/TBME.2024.3495542","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Objective: The objective of this study is to propose a novel methodology for intracranial pressure (ICP) waveform subpeak identification by incorporating arterial blood pressure (ABP) and electrocardiogram (ECG) signals from patients who have undergone traumatic brain injury (TBI).

Methods: This approach consisted of 1) multimodal signal pre-processing and initial manual ICP waveform morphology labeling, 2) semi-supervised training of a support vector machine (SVM) ICP waveform morphological classifier, and 3) a dynamic time warping barycenter averaging (DBA) based ICP waveform template generation and derivative dynamic time warping (DDTW)-driven ICP waveform subpeak mapping from template to incoming processed waveforms.

Results: This proposed framework was evaluated on 30,000 ICP waveforms and resulted in an overall subpeak identification accuracy score of 98.2%.

Conclusion: The results showcased an improvement over existing methodologies and showed resilience to variations in ICP waveform morphologies from patient to patient due to the incorporation of a subject matter expert (SME) to accommodate new and unseen ICP morphologies.

Significance: The robustness of this comprehensive approach enabled the analysis of ICP morphological features over time to provide clinicians with crucial insights regarding the development of secondary pathologies in patients and facilitate monitoring their physiological state.

查看原文本刊更多论文

颅内压子峰值识别的新方法，支持形态特征分析。

研究目的本研究的目的是提出一种新方法，结合脑外伤（TBI）患者的动脉血压（ABP）和心电图（ECG）信号进行颅内压（ICP）波形亚峰值识别：该方法包括：1）多模态信号预处理和初始人工ICP波形形态标注；2）支持向量机（SVM）ICP波形形态分类器的半监督训练；3）基于动态时间扭曲原点平均（DBA）的ICP波形模板生成和导数动态时间扭曲（DDTW）驱动的ICP波形子峰映射（从模板到输入处理的波形）：对 30,000 个 ICP 波形进行了评估，结果显示子峰识别的总体准确率为 98.2%：结果表明，与现有方法相比，该方法有所改进，并且由于加入了主题专家（SME）以适应新的和未见过的 ICP 形态，该方法对不同患者的 ICP 波形形态变化具有适应性：这种综合方法的稳健性使其能够分析随时间变化的 ICP 形态特征，为临床医生提供有关患者继发性病变发展的重要见解，并有助于监测患者的生理状态。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Biomedical Engineering 工程技术-工程：生物医学

CiteScore

9.40

自引率

4.30%

发文量

880

审稿时长

2.5 months

期刊介绍： IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.