Authors : R. Kalpana and I. Gnanambal
Abstract: There is a need to analyze the patients who are suffering from the common disorder in the brain called depression is the most common and disabling mental health disorder which affects not only on the person who is suffering but also on their entire families, friends and the overall society. Current clinical diagnosis relies almost maximum on patient self-report and clinical opinion, leading to a number of subjective biases. Our aim is to develop an objective that supports clinicians in their diagnosis and monitoring of depression. In this study, the analysis is carried over with depression patients who are suffering with cognitive disabilities. By using graph-theory approach and statistical analysis we can analyze EEG and is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing methods. This study attempts to classify the EEG signals of normal and depression patients using well-established signal processing techniques involving graph-theory concept The use of multichannel system is emphasized using brain connectivity analysis.
R. Kalpana and I. Gnanambal, 2016. The Analysis of Nonlinear Invariants of Multi-Channel EEG Signal Using Graph-Theory Connectivity Approach in Patient with Depression. Asian Journal of Information Technology, 15: 4106-4112.