Baygin Mehmet, Barua Prabal Datta, Dogan Sengul, Tuncer Turker, Hong Tan Jen, March Sonja, Tan Ru-San, Molinari Filippo, Acharya U Rajendra
Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, Turkey.
School of Business (Information System), University of Southern Queensland, Australia.
Comput Methods Programs Biomed. 2024 Apr;247:108076. doi: 10.1016/j.cmpb.2024.108076. Epub 2024 Feb 10.
Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals.
We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm.
Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction.
The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.
焦虑症很常见；早期诊断对治疗至关重要。焦虑会诱发大脑和心脏的生理变化。我们旨在开发一种高效且准确的手工特征工程模型，用于利用心电图（ECG）信号自动检测焦虑症。
我们研究了19名受试者通过可穿戴传感器收集的公开可用心电图数据，这些受试者在观看可能诱发焦虑的视频时被记录。使用汉密尔顿焦虑量表，将受试者分为正常、轻度焦虑、中度焦虑和重度焦虑组。心电图被分为不重叠的4秒（案例1）、5秒（案例2）和6秒（案例3）片段进行分析。我们提出了一种基于自组织动态模式的特征提取函数——概率二进制模式（PBP），其中函数内的模式由输入信号相关值的概率决定。这与可调q因子小波变换相结合，以促进在空间和频率域中多级生成特征向量。使用邻域成分分析和卡方函数来选择特征并降低数据维度。使用浅层k近邻和支持向量机分类器为每个输入信号计算四个（=2×2）按分类器划分的结果。从后者中，应用新颖的自组织组合多数投票来计算另外五个投票结果。使用贪婪算法从九个（按分类器划分和投票）结果中选择最佳最终模型结果。
我们的模型在所有三种情况下的分类准确率均超过98.5%。消融研究证实了基于PBP的特征工程相对于传统局部二进制模式特征提取的准确性提升。
结果证明了我们基于PBP的特征工程模型用于利用ECG信号进行焦虑症分类的可行性和准确性。
