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J. Biomedical Science and Engineering, 2017, 10, 1-9

http://www.scirp.org/journal/jbise

ISSN Online: 1937-688X

ISSN Print: 1937-6871

The Application of Visual Organization

Principle in the Detection of Sleep Spindles

Xuemei Jin, Junzhong Zou, Jian Zhang

East China University of Science and Technology, Shanghai, China

Email: jinxuemei1@sina.cn

Abstract

In order to detect the sleep spindles simply and efficiently, a novel time-

domain

approach to detect sleep spindles based on the principles of visual organiz

tion is proposed. The code idea of the visual organization is to organize the

primary visual elemen

ts according to some rules of organization, and to form

a more meaningful object

of visual processing, as the input of next process.

After the collected EEG is processed with the merging algorithm based on the

principle of visual organization, it can extract the time-domain feature fr

quency and duration time better. Use these features

with a simple algorithm

to detect spindles achieving sensitivity of 92.5% and specificity of 98.1%,

which verifies the validity of this method to detect the sleep spindles.

Keywords

Sleep Spindles, Principle of Visual Organization, Time-Domain Detection

1. Introduction

Sleep spindles (SS) generated from complex interactions between thalamic, lim-

bic and cortical areas are the hallmarks of the non-REM stage 2 sleep (N2). They

are sinusoidal spindle-like waveforms which have the characteristic of progres-

sively increasing, then gradually decreasing lasting 0.5~3s with a frequency pro-

file at 11 - 16 Hz. The figure characteristic of the waveforms is obvious [1].

Apart from being the characteristic wave of the sleep stage, sleep spindles also

have great relationships with human activities. Studies [2] have founded that dur-

ing sleep, the more sleep spindles exist, noises can be more tolerated and the deep

sleep can be more easily kept. Moreover, they are known to play a fundamental

role in memory consolidation during sleep [3], as well as being related to the se-

cretion of melatonin that helps in maintaining the body’s circadian rhythms [4].

Therefore, detecting the sleep spindles rapidly and efficiently has a great value in

physiological, pathological and pharmacological studies during sleep.

How to cite this paper:

Jin, X.M., Zou, J.Z.

and

Zhang, J. (2017

) The Application of

Visual Organization Principle in the Dete

tion of Sleep Spindles.

J. Biomedical Science

and Engineering

, 1-9.

https://doi.org/10.4236/jbise.2017.105B001

Received:

January 8, 2017

Accepted:

May 3, 2017

Published:

May 10, 2017

DOI: 10.4236/jbise.2017.105B001 May 10, 2017

X. M. Jin et al.

Detecting sleep spindles by man is a time-consuming and laborious work with

many uncertainty factors and prone to human error. The study [5] has indicated

the inter-rater variability in scoring them to be around 80%.

Since the EEG was first detected, scholars have applied various signal analysis

methods to the EEG analysis. Time-domain method is one of the earliest me-

thods to study EEG, which has the irreplaceable advantages. With the conti-

nuous development of the EEG study, analysis method has turned from the

time-domain into the time of frequency-domain, time and frequency-domain

and other nonlinear method. Gorur [6] used Short time Fourier transform for

feature extraction. Both multilayer perceptron and support vector machine are

utilized in detection of the spindles achieving a sensitivity of 88.7% and 95.4%.

[7] used amplitude-frequency normal modelling and reported a sensitivity of

75.1%. [8] used bandpass filtering with thresholding, relative power and autore-

gressive modelling to achieve sensitivity and specificity values of 84.6% and

95.3%.

In this paper, the merging algorithm based on the principle of the visual or-

ganization was applied to process the rare sleep EEG and then according to the

time-domain feature of sleep spindles to detect the sleep spindles, which can be

automatically marked. The rest of paper is organized as follows: Section 2 and

section 3 introduce the detection method and result, Section 4 shows the conclu-

sion.

2. Detection Method

2.1. Definition of Increasing and Decreasing Sequences

Define the

as the

th sample point in the sequence

{}

tk k

. In

1tt

tk tk

+ ++

( 1,2,......, 1)kn= −

and

tn tn

+ ++

then

{}

tk k

is an in-

creasing sequence. If

1tn tn

+− +

1tnl tnl

++ +++

( 0,1,..., 1)lm= −

and

1tnm tnm

++ ++ +

, then

{}

tnl l

++ =

is a decreasing sequence. The local maximum

point is

and the local minimum point are

and

tnm

. A new se-

quence

is defined to mark these local minimum and maximum points, and

let

an= +

a tn

= +

a tnm

=++

2.2. Merging Algorithm Based on the Principle of the Visual

Organization

The concept of visual organization originates from cognitive psychology. The

main research contents include: Gestalt perceptual organization principles [9]

[10] [11], visual closure [12] and non-contingency principle [13]

et al.

visual or-

ganization algorithm is the method to make the basic image elements form an

overall subject according to the quantitative model of Gestalt principle in a nar-

row sense. In a broad sense, algorithms that can achieve segmentation and clus-

tering can be classified as the visual organization algorithm [14]. Visual organi-

zation method can solve the computer visual problem such as: contour extrac-

tion, image segmentation and object detection

et al.

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