Retracted: A Deep Convolution Neural Network Framework for Detecting Depression

• Published in: International Conference on Intelligent Computing and Control Systems (Online) pp. 1061 - 1068
• Main Authors: Mohanraj, S., Balasubramaniyam, S., Kannan, V., Jeeva, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.05.2022
• ISSN: 2768-5330
• DOI: 10.1109/ICICCS53718.2022.9788445

Despair is common primary mental disorder reason to increase many suicide cases. To reduce the effects of depression, correct diagnosis are needed. An ECG is a device used to detect then collect the heart rate variability (HRV) parameters of electrical activity using electrodes. That report can be make use to produce the exact des pair level of a person. Existing system with the usage of ECG electrical data and deep learning (DL). So here the proposed system based on DL Neural Network (NN) namely DesNN for detection of des pair level using ECG data of different subjects. The proposed process done by two stages, likely the recorded data based and the hardware device with electrode based. The hardware ECG device output signals are stored in the online web based cloud storage and the data retrieval by when the detection is required. The outcome of the project DesNN comes with a high accurate level and detection level under the both stages recorded data based and hardware electrode based. The simulation and prototype model outcome performance with the DesNN is state-of-the-art and highly improved with existing models.