ELM结合LBEM：提升ADHD患者分类的局部特征提取方法

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本文档标题"ELM-Based Classification of ADHD Patients Using a Novel Local Feature Extraction Method"是一篇发表在2016年IEEE International Conference on Bioinformatics and Biomedicine (BIBM)上的研究论文。该论文关注于利用新型局部特征提取方法改进注意力缺陷多动障碍（ADHD）患者与正常儿童之间的区分精度。 ADHD是一种常见的儿童神经发育障碍，其特征在于功能交互的异常动态。为了提高诊断准确性，研究者们一直在寻找有效的方法从原始功能性磁共振成像（fMRI）数据中挖掘更多有价值的信息。作者Yang Li、Zhichao Lian、Min Li、Zhonggeng Liu、Liang Xiao和Zhihui Wei来自南京科技大学计算机科学与工程学院，他们提出了名为Local Binary Encoding Method (LBEM) 的创新性局部特征提取策略。LBEM的主要目标是有效地刻画功能交互模式（FIPs），这是一种在ADHD诊断中的关键指标。传统上，ADHD的诊断依赖于对脑部连接性的分析，而LBEM则能够更好地捕捉到这些功能连接的细微变化和异常。论文的核心贡献在于，LBEM通过一种新颖的二进制编码技术，能够将复杂的功能网络数据转化为易于处理的特征表示。这种方法能够更精细地揭示 ADHD 孩子与正常孩子之间在大脑活动协调上的差异，从而增强分类模型的识别能力。由于其高效性和对异常特征的敏感性，LBEM有望成为ADHD诊断领域的一个有力工具，为提高ADHD早期识别和干预提供技术支持。这篇论文不仅探讨了神经影像学数据分析的新方法，而且展示了它在实际应用中的潜力，对于理解和改善ADHD的诊断准确度具有重要意义。未来的研究可能围绕着进一步优化LBEM算法、扩展到更大样本量的数据集以及与其他生物标志物结合，以实现更精准的个体化诊断。

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ELM-Based Classification of ADHD Patients Using a

Novel Local Feature Extraction Method

Yang Li, Zhichao Lian, Min Li, Zhonggeng Liu, Liang Xiao, Zhihui Wei

School of Computer Science and Engineering , Nanjing University of Science and Technology

Nanjing,China

Abstract—Recently, it has been an increasing interest in

modeling abnormal temporal dynamics of functional interactions

in psychiatric disorders. However, the accuracy of differentiating

attention-deficit/hyperactivity disorder (ADHD) children form

normal children has still much space for improvement. To further

improve the accuracy, the key issue is to extract more effective

features from original fMRI data. In this paper, we propose a

novel local feature extraction method named Local Binary

Encoding Method (LBEM) that can effectively characterize

functional interaction patterns (FIPs). In particular, we show that

the proposed method can well discriminate the functional

interaction abnormalities, which is composed of a Bayesian

connectivity change point model, a local feature extraction

method and a kernel Extreme Learning Machine (ELM) -based

classifier. The experiment on a real dataset of 23 ADHD children

and 45 normal control (NC) children has shown that our method

achieved better classification performance compared to the

existing methods.

Keywords—local feature extraction method, functional

interaction patterns, Bayesian connectivity change point model,

ELM, ADHD

I. INTRODUCTION

Modeling functional connectivity between anatomically

distinct regions of interest (ROIs) in the brain has emerged as

a powerful tool for investigating functional brain interactions

and their abnormalities in psychiatric conditions in recent years

[1]. In this work, we have applied a model based on novel

Bayesian connectivity change point model (BCCPM) [2] that

contains a local feature extraction method to analyze the

abnormal temporal dynamics of functional interactions

especially attention-deficit/hyperactivity disorder (ADHD).

Increasing the accuracy of differentiating ADHD children form

normal children on the basis of [1] is our pursuit.

Recent studies suggested that the functional

activity/connectivity of any cortical area is subject to top-down

influences of attention, expectation, and perceptual tasks [3].

Dynamic interactions between connections from higher to

lower-order cortical areas and intrinsic cortical circuits involve

moment-by-moment functional changes in brain [3]. It has

been reported that functional interactions are still undergoing

sizable dynamic changes at different time scales even in resting

state [4]. Along this study direction, there have been several

studies analyzing brain functional dynamics. In [5], M.A.

Lindquist et al. have developed Hierarchical Exponentially

Weighted Moving Average (HEWMA) method on fMRI

signals to detect BOLD signal state change in response to

stimulus. In [6], C. Chang et al. use sliding time windows to

capture the functional brain dynamics.

In this work, a novel classification framework composed of

an effective Bayesian connectivity change point model for

modeling functional brain interactions, a local binary encoding

method for extracting features and a kernel ELM based

classifier for classification is proposed for classifying ADHD

patients. The main novelty of the paper is a new local feature

extraction method named Local Binary Encoding. The

procedure includes three stages (see Fig. 1). First, the BCCPM

is introduced to split sample set for obtaining a better

understanding of the functional brain dynamics without any

prior knowledge. Second, a local binary encoding method is

exploited on each sample to extract more effective and

discriminable local features for robust classification. Finally, a

KELM classifier is applied for the processed brain data to

compute the final classification results.

Input split Extraction KELM Output

Fig. 1. The flowchart of the proposed computational framework.

II. METHOD

A. Bayesian Connectivity Change Point Model

As mentioned before, brain functional interaction

/connectivity is under dynamic changes. Traditional analyses

which extract features directly from the whole time series do

not consider the dynamics of functional connectivity, and thus

the classification results are not accurate. For the purpose of

detecting the dynamics of functional interaction patterns, we

need to segment the fMRI time series into blocks where the

functional interaction patterns are considered as static. In this

paper, we applied the Bayesian connectivity change point

model (BCCPM) proposed by Lian et al. [2] to detect the

boundaries segmenting time series into blocks representing

different functional interaction patterns. Here, we just briefly

introduced the BCCPM model and the details can be referred

to [2].

Given a data matrix Y that consists of m of variables (i.e.,

ROIs) and T observations in temporal order, the BCCPM can

detect the location of change points which segment the time

series into blocks with different joint probabilities (defining

functional interactions) among the m variables.

First, the BCCPM defines a block indicator vector

 

, ,..,

C C C C

where

C 

if the time t is a change point (defined as the

starting point of a temporal block), and

C 

otherwise. The

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