Brainwave detection model for panic attacks based on event-related potential

Abstract EN

Panic attacks could adversely affect a patient’s daily life and can pose risks to others.

The symptoms of panic attacks can be timely observed by detecting the brainwave.

This research presents a model that can evaluate the level of panic attack symptoms using the brainwaves detection during (or before) the symptom occurs.

It helps monitor the patient’s brainwave based on Event-related potential (ERP).

The model is derived from the simulation with horror pictures and frightening sound on the experimental group of 30 people.

The survey related to symptoms which has been used regarding to the criteria of the Beck Anxiety Inventory (BAI).

The results showed that there is a consistent change of Electroencephalography (EEG) in each change of brainwaves where its quantitative analysis found that the changes of Beta, Gamma, and Alpha directly affect the model of Brainwaves Panic Attacks Measurement (BPAM) which is associated with panic attacks.

1 out of 30 cases scored higher than the average of the BPAM at 220 The Model BPAM can detect the risk to be Panic Attack compared to the use of tests Beck Anxiety Inventory (BAI) were found to be consistent.

The test value BAI Score 19- 63 was BPAM Score 401-1000.

In addition, the results found that at P300 the brainwave pattern of EEG in meditation had decreased significantly whereas the brainwave related to attention had increased considerably for which human brain can potentially respond to stimulated external events.