Key result
FFNN achieves 100% accuracy for EEG-based depression classification, outperforming PNN.
Why the study?
Does a feedforward neural network improve classification accuracy of EEG signals in depression patients compared to a probabilistic neural network?
Does a feedforward neural network improve classification accuracy of EEG signals in depression patients compared to a probabilistic neural network?
Feedforward neural networks demonstrate superior accuracy over probabilistic neural networks in classifying EEG signals for depression.
Answers are grounded in this paper and cite their sources.
May support ANN-wavelet EEG classification for depression detection; leaves open prospective validation before clinical use.
A detailed understanding of key signal characteristics has enabled the use of artificial neural networks (ANN) for feature detection and classification of EEG signals in clinical research. The present study is performed to classify EEG signals of normal and depression patients with wavelet parameters as key input features. The characteristics of depression cannot be made out by visual inspection of EEG records unlike epilepsy which is well characterized by sudden recurrent and transient waveforms. In this study, a comparison is made between the performance of feedforward neural network (FFNN) and probabilistic neural network (PNN) while classifying the EEG signals of normal and depression patients. Classification capabilities of both the methods are validated with the EEG recordings from 30 normal controls and 30 depression patients. One-way ANOVA provided a statistical significant difference between the two classes of EEG signals recorded. Preprocessing for feature extraction is done using discrete wavelet transform (DWT). The time domain and relative wavelet energy (RWE) features calculated from the sub-bands are given as a set of input to the neural network. Another set of feature used independently for training the network is the wavelet entropy (WE). The FFNN achieved a classification accuracy of 100% and PNN gave an accuracy of 58.75% with time domain and wavelet energy as the input features. With wavelet entropy as the input feature, FFNN further showed 98.75% classification accuracy while PNN gave an accuracy of only 46.5%. The results indicate that FFNN with the given input features is more suitable for the classification of EEG signals with mood changing depressive disorders.
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Puthankattil et al. (2016) studied Depression (n=60). Feedforward neural network (FFNN) vs. Probabilistic neural network (PNN) was evaluated on Classification accuracy using time domain and wavelet energy features. Feedforward neural network (FFNN) achieved 100% classification accuracy for depression using time domain and wavelet energy features, outperforming probabilistic neural network (PNN) at 58.75%.
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