Title :
Automatic sleep spindle detection in raw EEG signal of newborn babies
Author :
Bhattacharyya, Sourya ; Ghoshal, Subhabrata ; Biswas, Arunava ; Mukhopadhyay, Jayanta ; Majumdar, Arun Kumar ; Majumdar, Bandana ; Mukherjee, Suchandra ; Singh, Arun Kumar
Author_Institution :
Indian Inst. of Technol., Kharagpur, India
Abstract :
In this study, a novel method for automatically detecting sleep spindles from a given raw EEG (Electroencephalogram) data is proposed. We do not use any feature extraction and learning technique. Rather, we model the visual perception of identifying rhythmic peaks within frequency range 11.5-15 Hz. To achieve the performance close to visual detection, we first use a Gaussian window for smoothening of the signal. Then peak detection method is applied for identifying visually distinguishable peaks. If the frequency of peaks lies within frequency range 11.5-15 Hz, then we declare existence of a sleep spindle. Validity of our process is determined by visual scoring of sleep spindles and comparing it with the automatic scoring. We get a specificity range of 89%-98% for a sensitivity range of 87%-96% which is better that any other automatic detection process.
Keywords :
electroencephalography; learning (artificial intelligence); medical signal processing; Gaussian window; automatic scoring; automatic sleep spindle detection; electroencephalogram; feature extraction; learning technique; newborn babies; peak detection; raw EEG signal; visual perception; Electroencephalography; Kernel; Noise; Pediatrics; Sleep; Smoothing methods; Visualization; Artifacts; Electroencephalogram(EEG); Gaussian Smoothing; Peak detection; Sleep Spindles;
Conference_Titel :
Electronics Computer Technology (ICECT), 2011 3rd International Conference on
Conference_Location :
Kanyakumari
Print_ISBN :
978-1-4244-8678-6
Electronic_ISBN :
978-1-4244-8679-3
DOI :
10.1109/ICECTECH.2011.5941563