自动检测注意力缺陷多动症使用基于短时间里埃变换和离散等位数变换的机器学习算法
Manjusha Deshmukh1, Mahi Khemchandani2
1Computer Engineering Department, Saraswati College of Engineering, Navi Mumbai, India.
Applied neuropsychology. Child
|March 11, 2025
概括
使用STFT的脑电图 (EEG) 分解显示出在未成年人中自动检测ADHD的前景. 机器学习算法达到高达91%的准确性,有助于早期诊断.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 注意缺陷多动症 (ADHD) 是儿童和青少年常见的一种神经发育障碍.
- 重叠的症状使传统的ADHD诊断变得复杂.
- 脑电图 (EEG) 提供了一种非侵入性方法来评估大脑活动和潜在的自动诊断.
研究的目的:
- 研究电脑电图 (EEG) 分解技术,以改善ADHD检测.
- 评估机器学习算法在区分ADHD患者与健康个人的有效性,使用处理的EEG数据.
主要方法:
- 使用独立组件分析 (ICA) 来处理EEG信号,以消除噪音和人工物.
- 脑电图信号被用短时间里埃变换 (STFT) 和离散等边变换 (DCT) 分解成子频段.
- 分解子带和原始EEG信号作为机器学习分类器 (XGBoost,物流回归,随机森林) 的输入.
主要成果:
- 与DCT相比,STFT分解方法显示出更高的性能.
- 在STFT-XGBoost模型中,通过6个电极放置 (Fp1Fp2F3F4P3C3) 实现了91%的准确性.
- 其他电极组合和分类器 (逻辑回归,随机森林) 也显示出高精度,在88%至89%之间.
结论:
- 使用STFT分解和机器学习的自动EEG分析显示了ADHD诊断的巨大潜力.
- 这项技术可以提高早期检测和个性化治疗策略的ADHD.
- 拟议的方法为未来在ADHD诊断中临床应用提供了一种新的非侵入性工具.
相关概念视频
01:30Attention-Deficit/Hyperactivity Disorder
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Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
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01:26Discrete-time Fourier transform
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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
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01:15Discrete Fourier Transform
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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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01:20Discrete-Time Fourier Series
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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
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01:10Fast Fourier Transform
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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
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