A Fast Depression Detection Method Based on AKRCC-KNN Model

In order to proceed the fast detection of depression with EEG (electroencephalogram) signal, this study proposed a so-called AKRCC-KNN model for automatic and accurate diagnosis. Based on the multi-channel EEG signal with pre-processing, there is a novel approach focusing on the feature extraction,...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics Vol. 29; no. 6; pp. 1329 - 1341
Main Authors: Kan, Jing, Tong, Wei, Wu, Bichen, Ma, Yongchun, Chen, Kewei
Format: Journal Article
Language:English
Published: Tokyo Fuji Technology Press Co. Ltd 20.11.2025
Subjects:
Accuracy
Classification
Convergence
Correlation coefficients
Electroencephalography
Feature extraction
Feature selection
K-nearest neighbors algorithm
Mental depression
Performance enhancement
Phase lag
ISSN:1343-0130, 1883-8014
Online Access:Get full text
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Summary:In order to proceed the fast detection of depression with EEG (electroencephalogram) signal, this study proposed a so-called AKRCC-KNN model for automatic and accurate diagnosis. Based on the multi-channel EEG signal with pre-processing, there is a novel approach focusing on the feature extraction, in which the PLI (phase lag index) of EEG signals is calculated as the feature; moreover, the feature selection algorithm (so-called AKRCC) is innovatively integrated with AKRC (altered Kendall’s rank correlation coefficient) method for feature re-arrangement and convergence determination for feature selection, in order to improve the selective feature’s accuracy with limited computation expense. Hence the entire process of detection of depression with enhanced performance is listed as follows. Firstly, the PLI of EEG signals is computed to obtain their functional connectivity networks. AKRCC algorithm is then applied to rank PLI matrix elements by their discriminative power and determine optimal feature dimensionality through classification accuracy convergence monitoring. Finally, the selected multidimensional features are input into a KNN classifier for automatic classification. Extensive experiments on the MODMA dataset (24 major depression disorder patients, 29 healthy controls) demonstrate the model’s superior performance. With 1-second full-band EEG features, the AKRCC-KNN model achieves a state-of-the-art identification accuracy of 97.65% (specificity: 96.95%, sensitivity: 98.54%), surpassing existing methods. This indicates that the proposed depression detection model in this paper can achieve intelligent and rapid depression detection, providing an efficient, accurate, and diverse solution for clinical depression detection.
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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p1329