Detection of sleep apnea using sub-frame based temporal variation of energy in beta band in EEG
Autor: | Suvasish Saha, Projna Paromita, Shaikh Anowarul Fattah, Samee Azad, Farhin Ahmed, Arnab Bhattacharjee |
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Rok vydání: | 2016 |
Předmět: |
Sleep disorder
medicine.diagnostic_test Computer science Speech recognition 020208 electrical & electronic engineering Feature extraction Sleep apnea Spectral density Apnea 02 engineering and technology Electroencephalography medicine.disease Radio spectrum respiratory tract diseases Beta band 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing medicine.symptom |
Zdroj: | 2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). |
DOI: | 10.1109/wiecon-ece.2016.8009131 |
Popis: | Sleep apnea is a sleep disorder that affects one's breathing during sleep. A large number of people all over the world are suffering from this disease. Electroencephalogram (EEG) provides electrical activity of the brain signal that enables physicians to diagnose and monitor sleep apnea events. In this paper, an efficient scheme for classifying apnea and non-apnea events of an apnea patient is proposed based on temporal variation of Beta band energy in a frame of EEG data. Unlike conventional approaches, instead of extracting features from the whole frame at a time, a given test frame of EEG signal is divided into overlapping sub-frames and spectral characteristics are extracted from each pre-processed sub-frame. By investigating the spectro-temporal characteristics of all the traditional frequency bands of EEG signal, it is found that the temporal variation of spectral energy in Beta band plays the dominant role in classifying apnea and non-apnea events. Statistical features are extracted from the temporal pattern of Beta band energy and are used in K nearest neighborhood classifier. Extensive experimentation is carried out on several apnea patients with various apnea indices and a very satisfactory apnea detection performance is achieved in comparison to that obtained by some existing methods. |
Databáze: | OpenAIRE |
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