Sleep Spindles Detection: a Mixed Method using STFT and WMSD

Arnaldo Manuel Guimarães Batista; Manuel Duarte Ortigueira; DEE Group Author

Sleep Spindles Detection: a Mixed Method using STFT and WMSD

Arnaldo Manuel Guimarães Batista, Manuel Duarte Ortigueira, DEE Group Author

Research output: Contribution to journal › Article › peer-review

Abstract

Sleep spindles are a hallmark of stage 2 sleep and are promising indicators of neurodegenerative disorders such as schizophrenia and dementia. In this paper two sleep spindle detectors are presented. The first is based on the Short Time Fourier Transform (STFT), the second is a novel algorithm and is based in the wave morphology of sleep spindles. Finally, a combination of the previous is proposed in a novel mixed algorithm. Performance results are presented applying the algorithms to a signal scored by two human experts. It is showed in that the combination of two algorithms, which separately provided seasonable results (around 91% sensibility), improves when they are mixed using the approach proposed (93%sensibility).

Original language	Unknown
Pages (from-to)	229-233
Journal	International Journal of Bioelectromagnetism
Volume	14
Issue number	4
Publication status	Published - 1 Jan 2012

Cite this

@article{70d39a44203140d8b7aee15a1cdcb6ce,

title = "Sleep Spindles Detection: a Mixed Method using STFT and WMSD",

abstract = "Sleep spindles are a hallmark of stage 2 sleep and are promising indicators of neurodegenerative disorders such as schizophrenia and dementia. In this paper two sleep spindle detectors are presented. The first is based on the Short Time Fourier Transform (STFT), the second is a novel algorithm and is based in the wave morphology of sleep spindles. Finally, a combination of the previous is proposed in a novel mixed algorithm. Performance results are presented applying the algorithms to a signal scored by two human experts. It is showed in that the combination of two algorithms, which separately provided seasonable results (around 91% sensibility), improves when they are mixed using the approach proposed (93%sensibility).",

keywords = "Sleep Spindles, Wavelet Transform., Short Time Fourier Transform, EEG, Wave Morphology for Spindle Detection",

author = "Batista, {Arnaldo Manuel Guimar{\~a}es} and Ortigueira, {Manuel Duarte} and {DEE Group Author}",

year = "2012",

month = jan,

day = "1",

language = "Unknown",

volume = "14",

pages = "229--233",

journal = "International Journal of Bioelectromagnetism",

issn = "1456-7857",

publisher = "International Society of Bioelectromagnetism (ISBEM)",

number = "4",

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TY - JOUR

T1 - Sleep Spindles Detection: a Mixed Method using STFT and WMSD

AU - Batista, Arnaldo Manuel Guimarães

AU - Ortigueira, Manuel Duarte

AU - DEE Group Author

PY - 2012/1/1

Y1 - 2012/1/1

N2 - Sleep spindles are a hallmark of stage 2 sleep and are promising indicators of neurodegenerative disorders such as schizophrenia and dementia. In this paper two sleep spindle detectors are presented. The first is based on the Short Time Fourier Transform (STFT), the second is a novel algorithm and is based in the wave morphology of sleep spindles. Finally, a combination of the previous is proposed in a novel mixed algorithm. Performance results are presented applying the algorithms to a signal scored by two human experts. It is showed in that the combination of two algorithms, which separately provided seasonable results (around 91% sensibility), improves when they are mixed using the approach proposed (93%sensibility).

AB - Sleep spindles are a hallmark of stage 2 sleep and are promising indicators of neurodegenerative disorders such as schizophrenia and dementia. In this paper two sleep spindle detectors are presented. The first is based on the Short Time Fourier Transform (STFT), the second is a novel algorithm and is based in the wave morphology of sleep spindles. Finally, a combination of the previous is proposed in a novel mixed algorithm. Performance results are presented applying the algorithms to a signal scored by two human experts. It is showed in that the combination of two algorithms, which separately provided seasonable results (around 91% sensibility), improves when they are mixed using the approach proposed (93%sensibility).

KW - Sleep Spindles

KW - Wavelet Transform.

KW - Short Time Fourier Transform

KW - EEG

KW - Wave Morphology for Spindle Detection

M3 - Article

SN - 1456-7857

VL - 14

SP - 229

EP - 233

JO - International Journal of Bioelectromagnetism

JF - International Journal of Bioelectromagnetism

IS - 4

ER -