Detection of Chronic Stress using Bio-Signals
نویسندگان
چکیده
منابع مشابه
Robot control system using SMR signals detection
One of the important issues in designing a brain-computer interface system is to select the type of mental activity to be imagined. In some of these systems, mental activity varies with user intent and action that must be controlled by the brain-computer system, and in a number of other signals, the received signals contain the same activity-related mental activity that should be performed by t...
متن کاملClassification of Bio Optical signals using K- Means Clustering for Detection of Skin Pathology
Early diagnosis of precancerous and malignant lesions is critical for the improving of the current poor survival rate of patients with a variety of tumors. The development of new high-specificity and high-sensitivity imaging technologies can play an important role in the early diagnosis, accurate staging, and treatment of cancer. Bio-optical signals are the result of the optical functions of th...
متن کاملDetection of schizophrenia patients using convolutional neural networks from brain effective connectivity maps of electroencephalogram signals
Background: Schizophrenia is a mental disorder that severely affects the perception and relations of individuals. Nowadays, this disease is diagnosed by psychiatrists based on psychiatric tests, which is highly dependent on their experience and knowledge. This study aimed to design a fully automated framework for the diagnosis of schizophrenia from electroencephalogram signals using advanced de...
متن کاملDetection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods
Introduction: Emotions play an important role in health, communication, and interaction between humans. The ability to recognize the emotional status of people is an important indicator of health and natural relationships. In DEAP database, electroencephalogram (EEG) signals as well as environmental physiological signals related to 32 volunteers are registered. The participants in each video we...
متن کاملAutomatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers
Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Indian Journal of Science and Technology
سال: 2016
ISSN: 0974-5645,0974-6846
DOI: 10.17485/ijst/2016/v9i46/107866