EEG-based deep learning model for the automatic detection of clinical depression
- PDF / 1,581,404 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 12 Downloads / 169 Views
SCIENTIFIC PAPER
EEG‑based deep learning model for the automatic detection of clinical depression Pristy Paul Thoduparambil1 · Anna Dominic1 · Surekha Mariam Varghese1 Received: 13 May 2020 / Accepted: 10 October 2020 © Australasian College of Physical Scientists and Engineers in Medicine 2020
Abstract Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the EEG of a depressed individual. There are several strategies for automated depression diagnosis, but they all have flaws, which make the diagnostic task inaccurate. In this paper, a deep model is designed in which an integration of Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) is implemented for the detection of depression. CNN and LSTM are used to learn the local characteristics and the EEG signal sequence, respectively. In the deep learning model, filters in the convolution layer are convolved with the input signal to generate feature maps. All the extracted features are given to the LSTM for it to learn the different patterns in the signal, after which the classification is performed using fully connected layers. LSTM has memory cells to remember the essential features for a long time. It also has different functions to update the weights during training. Testing of the model was done by random splitting technique and obtained 99.07% and 98.84% accuracies for the right and left hemispheres EEG signals, respectively. Keywords CD · EEG · CNN · LSTM
Introduction Clinical depression (CD) or simply depression, is one of the most common psychological conditions that has an adverse impact on many lives [1]. Feeling of sadness, guilt, loss of interest, difficulty in concentrating, trouble in making decisions, sleep disturbances are some of the main symptoms of clinical depression. Such conditions may also become chronic, which in turn might hinder a person from carrying out everyday responsibilities. This can lead to various disturbing thoughts and self-harm attempts in the worst cases [2]. It can affect any individual, regardless of age and social position [3]. The three levels of depression are mild, medium, and extreme, depending upon the severity of the symptoms [2]. Depression is attributed primarily to the inter-hemisphere imbalance, a hyper-active Right Hemisphere (RH), and a * Pristy Paul Thoduparambil [email protected] 1
Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India
comparatively hypoactive Left Hemisphere (LH). Negative thinking and depression are related to more prominent activation within the frontal area of the RH. The intensity of depression is completely associated with the hyperactivity of RH [4]. Studies indicate that women are the foremost victims of depression [5]. To help depressed individuals return to a healthy life, early identification of depression is
Data Loading...
