Likeness-Based Detection of Sleep Slow Oscillations in Normal and Altered Sleep Conditions:...

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 57, NO. 2, FEBRUARY 2010 363

Likeness-Based Detection of Sleep Slow Oscillationsin Normal and Altered Sleep Conditions: Application

on Low-Density EEG RecordingsAndrea Piarulli, Danilo Menicucci, Angelo Gemignani, Umberto Olcese, Paola d’Ascanio, Alessandro Pingitore,

Remo Bedini, and Alberto Landi∗, Member, IEEE

Abstract—Sleep slow oscillation (SSO) is a common EEG pat-tern of spontaneous activity during nonrapid eye movement sleep.A new method for detecting SSOs is presented and compared toprevious canonical methods. The main result of this research isthat for the first time an extensive SSO analysis is applied to clin-ical EEG montages, based on low-density EEG recordings. Theproposed method gives positive indications about its effectivenessalso for altered sleep, extending the SSO analysis to extreme cases,and thus, opening a new front for investigating pathophysiologicalcorrelates of sleep disorders.

Index Terms—Automatic data processing, EEG, signal detection,sleep, slow oscillations.

I. INTRODUCTION

S LEEP is classically investigated by measuring electrical po-tentials on the scalp: the synchronous synaptic activity of

the neurons located underneath is detected through surface elec-trodes evenly spaced. Surface electrical potentials are studiedby means of the EEG, in order to obtain recordings of the con-tinuous time-varying potentials generated by the neural activity.In healthy humans, brain potentials oscillate with amplitudestypically ranging from 5 to 150 µV and with frequency range of0.1–140 Hz. In particular, the stages of hypersynchronous sleepor slow-wave sleep (SWS, nonrapid eye movement (NREM)stages III and IV, according to Rechtschaffen and Kales cri-teria for non-REM sleep [1]) are characterized by oscillationsgreater than 50 µV in the 0.3–4 Hz frequency range (slow-waveactivity). A novel hallmark in SWS characterization is the sleepslow oscillation (SSO): a specific EEG oscillatory pattern with afrequency range of 0.3–1 Hz, and amplitudes up to 300 µV, hav-ing a widespread synchronization over the cortex and occurring

Manuscript received November 4, 2008; revised April 21, 2009, July 2, 2009,and August 4, 2009. First published September 18, 2009; current version pub-lished January 20, 2010. Asterisk indicates corresponding author.

A. Piarulli is with the EXTREME Centre, Scuola Superiore Sant’Anna, 56127Pisa, Italy (e-mail: [email protected]).

D. Menicucci, A. Pingitore, and R. Bedini are with the Institute of ClinicalPhysiology, National Research Council (CNR), 56124 Pisa, Italy, and are alsowith the EXTREME Centre, Scuola Superiore Sant’Anna, 56127 Pisa, Italy(e-mail: [email protected]).

A. Gemignani and P. d’Ascanio are with the EXTREME Centre, ScuolaSuperiore Sant’Anna, 56127 Pisa, Italy, and are also with the Departmentof Physiological Sciences, University of Pisa, 56127 Pisa, Italy (e-mail:[email protected]; [email protected]).

U. Olcese is with the PERCRO Laboratory, Scuola Superiore Sant’Anna,56127 Pisa, Italy (e-mail: [email protected]).

∗A. Landi is with the EXTREME Centre, Scuola Superiore Sant’Anna, 56127Pisa, Italy, and also with the Dipartimento di Sistemi Elettrici e Automazione,University of Pisa, 56123 Pisa, Italy (e-mail: [email protected]).

Digital Object Identifier 10.1109/TBME.2009.2031983

hundreds of times in a night of natural sleep. This phenomenon,first detected in human sleep by means of an EEG in [2], is ofnotable relevance since it represents the macroscopic correlateof a specific neural behavior: intracellular recordings in animalshave revealed that during SSOs, the membrane potential of corti-cal neurons oscillates between a state of deep hyperpolarization(downstate, about 700 ms) and a state of wake-like depolariza-tion (upstate, about 700 ms) [3]–[5]. Furthermore, this rhythmicactivity seems to originate in the cortex, reverberate in subcor-tical structures [6], [7] and propagate through the cortex as atraveling wave [8]. Each propagating SSO (SSO instance) hasa definite site of origin, usually located in the anterior corti-cal regions, and predominantly spreads towards posterior cor-tical areas [8]. In addition, a relationship has been establishedbetween SSOs and neural plasticity, i.e., memory consolida-tion of specific tasks [9], [10]. Despite the importance of thisphenomenon, a widely accepted methodological approach forthe detection and the characterization of the SSO instances isstill lacking. Reported methods recognize SSOs through vari-ous morphological constraints [8], [11], [12], whose thresholdvalues are mostly derived from years of experience in visualinspection of EEG sleep recordings. Even if no general guide-lines for setting SSO visual scoring have been established inthe literature, several attempts have been made to define anautomatic recognition. As examples, two milestone studies inSSO neurophysiology deal with this issue adopting differentperspectives. While Massimini et al. [8] based the identifica-tion of SSOs on three criteria, two concerning the amplitudeand one the temporal width of the waves, Riedner et al. [12]dropped the amplitude criteria, maintaining only the temporalconstraint. The strength of Massimini’s method is its reliability(i.e., the very low number of false detections) since it is based ona strict application of three morphological criteria. On the otherhand, by fixing rigid thresholds, many SSO detections can belost; this problem is emphasized by the well-established inter-subject variability of slow-wave activity amplitude, due to age,sex differences, and quality of sleep [13]–[17]. As for Riedner etal. [12], while on the one hand their approach allows detectionof almost all the SSOs in an EEG recording; however, droppingany amplitude constraint on oscillations may lead to the detec-tion of SSO instances composed of only very low amplitudewaves (less than 50 µV), noncorresponding to the paradigmaticSSO given in [1]. Taking the cue from all these considerations,the aim of this study was to develop an automatic method capa-ble of detecting a large number of SSOs while yielding, at the

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364 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 57, NO. 2, FEBRUARY 2010

same time, a low number of false detections. In addition, ourmethod is able to work both on high-density EEG configuration(128 channels) and on low-density ones (32 channels or less).Starting from visually staged sleep EEGs [1], we focused onSSOs during NREM stages from 2 to 4, using a likeness-basedautomatic detection method, hereafter referred to as the likenessmethod (LM). LM was tested following a comparison with thecanonical detection method (CDM) described in [8] and evalu-ating the stability of the results, by varying the number of EEGelectrodes from high-density configurations down to clinicallow-density EEG montages. The behavior of the LM was testedin a proof-of-concept study exploring two sleep classes: a nor-mal sleep recording, showing normal patterns of delta activity ata visual inspection and an altered sleep recording, characterizedby a mingled sleep macrostructure, without a clear separationbetween stages and generally with low-amplitude delta waves.

II. EXPERIMENTAL SETUP

The proposed LM was tested on sleep recordings from twosubjects. Both subjects were right-handed, 30-year-old males.Inclusion criteria were: drug-free for at least 1 year, no personaland family history for sleep disorders and no comorbidity withother medical, neurological, and psychiatric disorders. Informedconsent was signed in accordance with the guidelines of the localEthical Committee. After a night of adaptation in the laboratory,the volunteers were allowed to sleep at their usual bedtime andEEG recordings were carried out during the first sleep cycle ofthe night. Subject 1 spent wakefulness time before sleep mainlyattending classes and reading scientific books; thus, he had aprevalent daily mental activity, sustained by frontal lobe func-tions, such as working memory and logical and mathematicalreasoning. Subject 2 was recorded after an all-day ultratriathlonperformance (participation in an Ironman competition, swim-ming for 3.8 km, followed by cycling for 180 km, and runningfor 42 km) and showed high plasma levels of stress-relatedsubstances such as cortisol and proinflammatory cytokines [in-terleukin 2 (IL2) and tumor necrosis factor-alpha (TNF-α)],which typically characterize stress-related diseases (depressionand posttraumatic stress disorders) or hormonal disorders [18].EEG signals were obtained at 500 Hz with a 128-channel ac-quisition system (EGI Geodesics) and referred to the potentialin the scalp vertex (Cz). EEG raw data were postprocessed by anotch filter at the line frequency (50 Hz) and then rereferencedto the average of the signals recorded from the mastoids (A1and A2)

Vir = Vi −VA1 + VA2

2(1)

where Vi is the potential on the electrode i, VA1 and VA2 aremastoid potentials, and Vir is the rereferenced potential.

Sleep stages were visually scored according to Rechtschaffenand Kales [1] on the EEG referenced to the mastoids (C3-A2;C4-A1). Movement artifacts were detected by analyzing all theEEG channels and finding concurrent increases in signal am-plitudes. EEG epochs with amplitudes exceeding a thresholdcorresponding to the 95th percentile of the amplitude distri-bution of recorded signals were classified as movements, or

muscular artifacts. After visual inspection, most of these EEGepochs were discarded. Transitional decline in signal quality,mainly due to instability or loss of contact of the electrodeswith the skull during the recording, was checked on the basisof signal statistics EEGLAB Toolbox [19] and the detected badchannels were discarded. To this aim, in line with the EEGLABtutorial on rejecting artifacts in continuous data by using thechannel statistics function of the toolbox, data channels wereempirically rejected if channels had distributions of potentialvalues that were further away from a Gaussian distribution thanother scalp channels. EEG recordings were segmented in 5-min epochs; for each epoch, channel statistics were estimated,discarding highly non-Gaussian channels. The analyzed record-ings showed more than 90% of artifact-free epochs. The retainedEEG signals were bandpass-filtered between 0.1 and 4.4 Hz us-ing a Chebyshev ( ) II filter (MATLAB, The MathWorks, Inc., Natick, MA), with parameters chosen so as to ob-tain minimal amplitude attenuation and phase distortion. Thefilter was applied to signals in both forward and reverse direc-tions to avoid time biases. In line with Riedner et al. [12], weset low-frequency stop = 0.1 Hz, low-frequency pass = 0.5Hz, high-frequency pass = 4 Hz, and high-frequency stop = 10Hz. The filter procedure produced no attenuation at passband,−60 dB at the low stopband and −80 dB at the high stopband.

III. LIKENESS METHOD

The LM in SWS analysis is based on the detection of SSOinstances and relies on two fundamental threads:

1) location of temporal occurrence of the SSO instances;2) for each instance occurrence, selection of the EEG chan-

nels showing an SSO pattern.Each thread is discussed in detail in the next two sections.

Fig. 1 summarizes the main steps of the LM.With regard to the first thread, seeds of SSO instances are

identified by applying the CDM (canonical criteria) on eachsingle electrode recording. Each identified SSO, named canon-ical SSO, constitutes a seed and concurrent seeds detected ondifferent channels are clustered into the same SSO instancesince they represent different detections of the same propagat-ing SSO. In Fig. 1(a), black continuous lines identify the seeds,namely, the waves that satisfy the CDM. As far as the sec-ond thread is concerned, for each SSO instance a likeness rulecapable of detecting concurrent waves strongly similar to thecanonical ones was applied to channels not showing a canonicalSSO. In Fig. 1(a), the dashed gray lines identify an example ofthis kind of wave. In fact, direct use of the CDM on the singlechannel EEG signal only ensures detection of well-expressedinstances but does not guarantee a priori the complete detectionof SSOs belonging to instances in case of waxing and waning-like behaviors. In order to deal with this limitation, the likenessrule ensures, for each instance, the detection of underexpressedSSOs (i.e., noncanonical SSOs), thus providing a more detaileddescription and characterization of the propagating instance. Asa result of the application of the LM on EEG sleep record-ings, propagating waves were identified as SSO instances, ifcomposed of at least one canonical SSO. Each identified SSO

PIARULLI et al.: LIKENESS-BASED DETECTION OF SLEEP SLOW OSCILLATIONS IN SLEEP CONDITIONS 365

Fig. 1. (a) Five bandpass-filtered EEG traces are shown. Black solid linesidentify waves complying with the canonical criteria and the thickest one isthe PI. The gray dashed line identifies a concurrent subthreshold wave; thetwo gray solid lines are not recognized by LM as SSOs. (b) With the samegraphical code, the phases of the signals are depicted. It is worth noting that inthe time-phase plane canonical and subthreshold waves overlap. (c) To measurethe overlap degree, the cross-correlation functions between the PI and the othertraces are plotted. By the canonical-likeness constraint, the subthreshold waveswere separated from the EEG traces not showing SSOs. (d) Few examples ofEEG data indicating the waves detected by the LM. For both subjects, SSOs thatsatisfy the canonical criteria described in Section III-A are presented in thicksolid lines and in thick dashed lines subthreshold waves belonging to the sameinstance. The thickest line identifies the PI, as defined in the Section III-A. Foreach SSO instance, the asterisk symbol indicates the wave of origin.

instance was then enriched with noncanonical SSOs, detectedby the application of the likeness rule on channels not exhibitingcanonical SSOs in an appropriate temporal neighborhood of theinstance itself.

A. Detection of SSO Instances

The temporal occurrence of SSO instances was identified byscanning each EEG channel to detect canonical SSOs, namely,two zero crossings separated by 0.3–1.0 s, the first one havinga negative slope; a nadir between the two zero crossings with avoltage of less than −80 µV; a nadir-to-peak (NP) amplitude ofat least 140 µV. Concurrent SSOs detected on different channelswere then grouped into instances and sorted according to theirnadir time location; in particular, within each group, the firstoccurring SSO was selected as the prototype of the instance(PI), identified in Fig. 1 by the thickest trace. On the basis ofprevious results [8], [12] and of a preliminary evaluation of ourdata, we assumed the SSOs detected on different channels to beconcurrent when the time lags between the PI and the followingones were below 200 ms. This procedure allowed clusteringof SSOs detected on different channels, belonging to the sametraveling instance; each cluster was then referred to as an SSOinstance.

B. Enrichment of SSO Instances

To select channels showing an SSO pattern belonging to eachSSO instance, we designed an algorithm to account for the con-tinuous nature of the SSO phenomena. In this perspective, the

use of the CDM to detect single SSOs appeared too restrictive.Let us consider the case of waxing and waning behavior of anSSO instance as an example. At the dawn of the instance, asa consequence of the synchronization among a small neuronalpopulation, subthreshold waves (with respect to the amplitudecriterion) may occur. Afterwards, through recruitment of largerneuronal populations, the SSO propagates and grows, reach-ing its full expression, i.e., becoming a canonical SSO beforegradually fading. Whatever the evolution of the SSO ampli-tude, however, the propagating wave tends to roughly maintainits shape since it is always determined by the same cellularmembrane-potential dynamics. Moving from this premise, inorder to detect noncanonical SSOs, for each SSO instance thelikeness rule was applied according to the following steps.

1) Considering the EEG epoch of the SSO instance, the cross-correlation function C(t) between instantaneous phases(Hilbert transform, [20]) of PI signal and each concur-rent EEG signal was estimated. Fig. 1(b) shows the esti-mated instantaneous phase of signals in Fig. 1(a), while inFig. 1(c) the cross-correlation functions are shown.Consider the signal f(t). The Hilbert transform of f(t) isgiven by

H {f(t)} = j

+∞∫

−∞

f(t − τ)πτ

δτ . (2)

The instantaneous phase of f(t) may be computed as

ϕ(t) = tan−1[H {f(t)}

f(t)

]. (3)

Let x(t) be the PI and y(t) the concurrent signal. The C(t)function is defined as

C(t) =

t∫

−t

ϕ [x∗(t)] · ϕ [y(t + τ)] δτ . (4)

The C(t) function, estimated in a symmetrical interval oflags t from –200 to +200 ms, always exhibited a cusp-like shape, with a well-recognizable maximum max[C(t)].Upon change of the time shift t, the max[C(t)] is deter-mined by the correlation of maximal overlap between thepatterns of the two phases. Its value yields an estimate ofthe amplitude-independent likeness between SSO wave-forms.

2) Considering all SSO instances extracted from an EEGrecording, the distribution of the subset of max[C(t)] val-ues corresponding to PI-canonical SSO pairs was esti-mated. The 25th percentile of this distribution was con-sidered as a reference measure for the likeness betweeneach PI and the canonical SSOs of the same SSO instance(canonical-likeness constraint).

3) In order to focus only on extremely reliable SSO instances,we employed the canonical-likeness constraint to dropthose instances composed by canonical SSOs slightly sim-ilar to their PI, i.e., with all max[C(t)] values lower thanthe canonical-likeness constraint value.


TABLE IPERCENTAGE OF FALSE DETECTIONS

4) In order to recover SSOs within channels not containingcanonical ones, for each retained SSO instance, concur-rent waves showing a strong likeness with the PI wereselected. We kept only waves that were more similar totheir PI than the canonical-likeness constraint (i.e., witha max[C(t)] value larger than the canonical-likeness con-straint), identified in Fig. 1(c) by the dashed horizontalline. Applying this procedure, instances resulted enrichedwith new channels containing SSOs, although noncanon-ical (gray dashed traces in Fig. 1).

IV. METHOD VALIDATION AND SSO CHARACTERIZATION

To verify the suitability of the proposed LM for detecting alarge number of SSOs with a low number of false detectionsand for working with various EEG configurations, we tested itby performing an analysis of two different sleep conditions: asleep recording showing normal patterns of delta waves activ-ity at visual inspection and a recording exhibiting a mingledsleep macrostructure without a clear separation among stagesand characterized by low-amplitude delta waves. With regardto SSO false detections, we dealt with this overlooked issue(none of the main neurophysiological studies on SSO activ-ity [2], [8], [11], [12] discuss the specificity of their detectionalgorithms), by considering the possible sources of artifacts. Inhealthy individuals the only possible artifacts mimicking SSOscan be slow eye movements and light head movements: bothcases are muscle-related. From an electrophysiological pointof view, muscle artifacts produce swings and high-frequency(>60 Hz) noise on EEG signals. On the contrary, the downstateof SSO is characterized by a condition of electric silence (neu-rons in the region of interest will not fire). As a first attempt torecognize false detections, for each SSO we measured the powerin the 60–200 Hz frequency band within the SSO segment fromthe first zero crossing to the nadir point. We classified as artifactsthose SSOs having an associated high-frequency power greaterthan 100 µV2 [21]. On this basis, an SSO instance was classi-fied as a false detection, if at least one of the belonging waveswas artifacted. The percentages of false detections for the CDMand the LM are reported in Table I, both in cases of 128 and32 channels. Despite the strict condition adopted for the false-detection definition, the percentage found in the normal sleeprecording was very low (about 1%) for both LM and CMD, andfor high-density and sparse electrode montages. False-detectionpercentages increased, however, moving to the altered sleepcase: in the 128-channel montage we found more than 10% of

false detections and less than 5% of false detections for bothmethods in the reduced 32-channel montage. Apparently, themingled sleep macrostructure induced noise, reasonably relatedto muscular activities, also during episodes of very deep sleep asdid SSOs. It is also interesting that the percentages of false de-tections resulted smaller for the 32-channel case compared to the128-channel one. This was related to the channel selection forthe reduction: in fact, maintaining electrodes correspondent toclassical montages, the extremely peripheral electrodes (namely,those nearer to head muscles) were discarded. In all cases, thepercentages of false detections were comparable for both meth-ods, but, as shown in the following, LM produced about fivetimes the instance detections of CDM and also a larger numberof SSOs detected per instance, thus allowing a more detaileddescription of the SSO phenomenon. As a first step, the LMwas applied to the 128 channels of the normal sleep recording;the outcomes were compared with those obtained by the CDMapplied to the same recording and compared with the data in lit-erature. The stability of the results was investigated by varyingthe density of EEG electrodes from a high-density configurationdown to typical clinical EEG montages: the configurations of64, 32, 20, and 10 electrodes were obtained by downsamplingthe original recording. For each configuration, the retained elec-trodes were selected to obtain settings corresponding to mon-tages currently used in electroencephalography. Finally, the LMwas tested in the analysis of the altered sleep recording. Theeffectiveness of the proposed method was determined by study-ing both the propagation and the morphological properties ofthe detected SSO instances. Each enriched SSO instance wascharacterized in order to describe its propagation across thescalp. In particular, we detected the location from which thepropagating SSO originates (instance origin), by selecting thechannel containing the first occurring nadir. In addition, we es-timated the instance size ratio, i.e., the number of the detectedSSOs in the instance divided by the number of electrodes in theEEG montage. Finally, an assessment of the instance speed ofpropagation was provided, based on the delays between nadirtimes of its SSOs [8]. Although this is a pointwise delay method,we chose it since these reference points are usually sharp andnot crowned by oscillations in the spindle frequency range (12–15 Hz), i.e., they are precisely detectable. To estimate the speed,we assumed a simple propagation model: SSOs propagate radi-ally from the origin location, forming circular wave fronts, likea stone thrown in a pond. On the basis of this hypothesis, wecalculated the linear regression between the distances from theorigin to each electrode location affected by a SSO and the cor-responding delays. Thus, the slope of the obtained interpolatingline yields an estimate of the instance speed. A morphologicalcharacterization of all the detected SSOs was performed (seeFig. 2). Each enriched instance was characterized by the meanvalues of some definite SSO features: the NP amplitude, thevalue of the slope between the nadir and the peak of the signal(NP slope), the absolute value of the slope of the signal betweenthe first zero crossing and the nadir (ZN slope), and the NP time.Finally, in order to evaluate the within-instance similarity of thedetected SSOs, we estimated the instance tightness as the meanvalue of max[C(t)] of SSOs belonging to the instance.


Fig. 2. SSO template with the extracted morphological features.

V. RESULTS

EEG 128-channel recordings were performed on two sub-jects during the first sleep cycle of the night (1.5–2 h). Duringthis period both subjects were able to reach the deepest stagesof NREM sleep. Fig. 1(d) shows a few examples of EEG dataindicating the waves detected by the LM. For both subjects,well-defined SSOs (satisfying the canonical criteria describedin Section III-A) are depicted in thick solid lines, while sub-threshold waves belonging to the same instance are indicatedwith thick dashed lines. For each SSO instance, the asterisksymbol indicates the wave of origin. From each recorded ses-sion, epochs corresponding to waking, NREM sleep stages 2–4and REM sleep stage were extracted and subsequently analyzed.NREM sleep stage 1 was not considered, being a transitionalcondition of drowsiness. Subject 1 had 10 min of waking be-fore sleeping, 74 min of 2 to 4 NREM sleep stages, and 5 minof stage REM. Subject 2 had 5 min of waking before sleep-ing, 34 min of 2 to 4 NREM sleep stages, and 3 min of stageREM. As expected, for low-voltage high-frequency EEG signalscharacterizing waking and REM conditions, neither CDM, northe LM detected SSOs. Subject 1, who spent his wakefulnesstime before sleep mainly studying, showed normal patterns ofdelta-wave activity during his SWS while subject 2, after an all-day ultratriathlon performance inducing high plasma levels ofstress-related substances, showed a mingled sleep macrostruc-ture without a clear separation among stages and a delta activ-ity characterized by low-amplitude waves. Although this sub-ject showed well-defined NREM stages 3 and 4, according toRechtschaffen and Kales [1], his sleep pattern was characterizedby a high number of arousals (epochs of EEG desynchroniza-tion), which induced fragmented and shallow (NREM stages 1and 2) sleep, although without awakenings.

A. Method Validation: Normal Sleep Case

As a first step, we performed analysis of the sleep recordingof subject 1 by using both the LM and the CDM. The number ofinstances detected through the application of the proposed LMmethod was 719 (detection frequency 9.7 instances/min), whilethe CDM method detected 131 instances (detection frequency1.77 instances/min). The histogram of interdetection intervals inthe case of the LM and 128-channel montage is shown in Fig. 3.This histogram, presented in log–log scale, suggests a power-

Fig. 3. For the normal sleep case, the histogram of interdetection intervals(log–log scale) in the case of the LM and 128-channel montage.

TABLE IIFOR NORMAL SLEEP CASE, DESCRIPTIVE STATISTICS OF EACH FEATURE, BOTH

FOR LM AND FOR CDM

law behavior (the number of instances seems to be proportionalto Ta , where a ≈ 2.2, and T is the interdetection interval). Thisfinding may suggest an underlying hierarchical functional or-ganization that statistically controls SSO generation [22], [23].The instances detected with the CDM were a subset of thosedetected with the LM: all CDM-detected instances were alsodetected by the LM. In addition, thanks to the enrichment pro-cedure (see Section III-B), even in the case of common instances,the instance size ratios detected with the LM were greater (seeTable II). As a relevant effect, both morphological and propaga-tion properties can be estimated with a higher accuracy for boththe single instance and the full recording. In addition, the in-creased instance size ratios did not lower the instance tightness,compared with the CDM-derived instances. As highlighted inTable III, SSOs detected by means of the LM are much moresimilar to each other compared to those detected with the CDM,that is to say, considering each instance, SSOs detected with theLM turned out to have shapes more similar to their PI than thoseobtained with the CDM. This statement holds true both consid-ering the whole sets of instances detected by each method (seeTable III, unpaired t-test, p < 0.01) and focusing on the subset ofinstances detected by both methods (see Table III, paired t-test,


TABLE IIIFOR NORMAL SLEEP CASE, DESCRIPTIVE STATISTICS OF INSTANCE

TIGHTNESS

Fig. 4. For the normal sleep case, (row a) origin and (row b) detection maps.The first column depicts results obtained through the application of the LM,while the second one results from the application of the CDM.

p < 0.01). Also, applying the detection method to the down-sampled 32-channel EEG configuration (LM: 485 detectionswith frequency of 6.5 instances/min; CDM: 110 detections withfrequency of 1.5 instances/min) similar results about tightnesswere obtained. The greater detection rate of the LM raises thequestion of whether the waves recognized through the proposedmethod can be really considered SSOs. For this purpose, weassessed whether adding instances and waves within instancesalters the topographical distribution of origins and detections ofSSOs. Frequencies regarding origins and detections of SSO in-stances were summarized on 2-D topographical representationsof the scalp (see Fig. 4). The origin maps (row a) highlight theareas of dawn of the SSO instances, while the detection maps(row b) highlight the areas more involved in SSO activity. Foran easier comparison, each map was individually normalized:white tone corresponds to the max value, black tone to zero.These maps in a normalized unit (n.u.) show a correlation be-tween the two methods, both for origin and detection locations:origin locations are mostly located in the frontal area and detec-tion locations spread throughout the whole frontocentral region.Comparing the two methods for 128 matched electrodes, theaverage difference for origin maps was 0.15 n.u. and for detec-tion location was 0.2 n.u. As confirmation of the SSO natureof the waves detected by the LM, we evaluated the grouping ofspindle activity during SSOs. By using the CDM, we confirmed,as demonstrated in [11], that a consistent grouping (i.e., a mod-ulation) of spindle activity coinciding with slow oscillationscan be generally found, and we examined whether an analo-gous grouping occurs for instances detected with LM. In detail,for each instance, the time intervals corresponding to negativeand positive half-waves of the PI were identified. For all half-waves of the SSOs belonging to the instance, the wave-triggered

TABLE IVFOR NORMAL SLEEP CASE, SIGNIFICANCE OF SSO SPINDLE GROUPING, AND

PERCENTAGES OF INSTANCES SHOWING A CLEAR SPINDLE MODULATION,BOTH FOR LM AND FOR CDM AND TWO EEG

CHANNEL CONFIGURATIONS

averages of root-mean-square (rms) activity in the spindle(12–15 Hz) range were formed (power was estimated usingthe fast Fourier transform (FFT), Hamming windowed). Foreach SSO, the modulation value was estimated as the differencebetween the upstate and the downstate values and each SSO in-stance was characterized by the average modulation of the SSOsbelonging to it. In addition, we counted the number of instancesshowing a positive modulation (i.e., in favor of the positive half-wave). The results for the normal sleep case are reported in Ta-ble IV: on average (p < 0.001), both detection methods identifySSO instances modulating spindle activity; also the percentageof instances exhibiting a spindle modulation was substantiallythe same. However, as a consequence of the enrichment proce-dure, instance averages of some features turned out to depend ondetection methods (see Table II). As expected, for LM-derivedinstances, NP amplitude was smaller and instance size ratio wasgreater than those determined by means of the CDM method.Interestingly, also NP time, instance speed, and ZN slope weredifferent, but within the ranges of SSO references [8], [12]. Anextension of the research from a proof-of-concept study to avalidation study should confirm these findings. For each fea-ture, we performed an analysis of variance (ANOVA) procedurewith two between-subject factors: detection methods (two lev-els: CDM and LM) and channel montage (two levels: 128- and32-channel). Interestingly, no channel montage effect emerged,except for the instance speed estimation: with sparser arrays ofelectrodes the speed resulted overestimated, on average, 0.7 ±0.2 m/s compared to the complete 128-channel montage. Tak-ing advantage of the fact that instances detected with the CDMare a subsample of those found by applying the LM, for eachfeature, the subsample of LM instances corresponding to theCDM ones were extracted. Thus, in Table II, there are, for eachfeature, the results of the t-test performed both on the subset ofinstance common to the two methods (paired t-test) and the t-test between all instances detected by the two methods (unpairedt-test). The major purpose of this study is the development ofa method able to detect and study SSOs in low-density EEGrecordings, so we tested the stability of the method by vary-ing the number of available active electrodes (on the recordingof subject 1, the LM was applied to 128-, 64-, 32-, 20-, and10-electrode montages). As expected, the number of detectedinstances was strictly related to the EEG configuration, namely,it decreased when the number of electrodes was downsampled.In addition, it is noteworthy to say that instances detected ata downsampled configuration were a subset of those found


Fig. 5. For the normal sleep case, (row a) origin and (row b) detection maps.Each column corresponds to a specific electrode configuration (128-, 64-, 32-,20-, and 10-channel).

Fig. 6. For the normal sleep case, paired difference distributions and box plots.(a) NP amplitude (in microvolts). (b) NP time (in milliseconds). (c) NP slope(in microvolts per millisecond). (d) ZN slope (in microvolts per millisecond).(e) Instance speed (in meters per second). (f) Instance size ratio.

analyzing the 128-channel configuration. With regard to theorigin and detection maps, from Fig. 5, we can assess a goodstability of the method when scaling from 128 down to 32 elec-trodes, while the 20-electrode configuration maps reveal somecritical differences.

In Fig. 6, the paired comparison was evaluated as follows.1) For all the downsampled configurations, for each detected

instance, the within-instance mean value of the featurewas estimated.

2) With respect to the 128-channel montage, the subset ofinstances corresponding to those detected in the down-sampled configuration was extracted. For each instance,in this subset, the within-instance mean value of the fea-ture was estimated.

3) For each pair of pair instances, we evaluated the differ-ence of the within-instance mean value between the 128-channel configuration and the downsampled one.

TABLE VFOR ALTERED SLEEP CASE DESCRIPTIVE STATISTICS OF EACH FEATURE ARE

SHOWN, BOTH FOR LM AND FOR CDM

From these comparisons (see Fig. 6), we can assert the goodstability of the LM from 128- to 32-channel configurations: infact, the distributions of paired differences have median valuesclose to zero and there are no significant differences betweenvalues obtained with the LM from 128- to 32-channel config-urations. Still, further lowering the number of electrodes, thedistribution widths tend to widen; this result is due to the scarcestatistics of lower density configuration, so feature values tendto be more scattered. In any case, the LM works effectively withthe 32-electrode configuration and some reliable results can beobtained even with a 20-electrode configuration. In particular,although the confidence interval was wider, no evidences ofbiases (no significant effects) in the estimated values resultedfor all features (ANOVA procedure with two between-subjectfactors: detection methods, i.e., CDM and LM, and channel mon-tage, i.e., 128- and 20-channel), except for the instance speedthat exhibits an increasing bias toward overestimation whenlowering the number of electrodes per montage. In fact, theANOVA procedure on instance speed with a five-level between-subject factor (channel montage, with 128-, 64-, 32-, 20- and10-channel) showed significant linear and quadratic contrasts(p < 0.01).

B. Method Validation: Altered Sleep Case

In order to test the effectiveness of the LM also in a conditionof altered sleep, a sleep EEG recording of an ultratriathlon ath-lete carried out after participation in an Ironman competition wasanalyzed and results were compared with those achieved withthe CDM. As shown in Table V, we considered both the complete128-channel configuration and the reduced case of 32-channelconfiguration. Also for the altered sleep case, the instance sizeratios (see Table V) detected with the LM were significantlygreater without lowering the instance tightness (see Table VI).In addition, as a consequence of the enrichment procedure,


TABLE VIFOR ALTERED SLEEP CASE, DESCRIPTIVE STATISTICS OF

INSTANCE TIGHTNESS

Fig. 7. For the normal sleep case, the histogram of interdetection intervals(log–log scale) in the case of the LM and 128-channel montage.

instance averages of features turned out to significantly dependon the detection method (see Table V). As expected for LM-derived instances, NP amplitude was lower compared to CDM.Also NP time, instance speed and ZN slope were different. In or-der to confirm these observations, for each feature we performedan ANOVA procedure with two between-subject factors: detec-tion methods (two levels: CDM and LM) and channel montage(two levels: 128- and 32-channel). Also, in this case, no channelmontage effect emerged, except for the instance speed estima-tion. In Table V, there are for each feature the results of thet-test performed both on the subset of instance common to thetwo methods (paired t-test) and the t-test between all instancesdetected by the two methods. For the 128-channel configura-tion, the number of instances detected through the applicationof the LM was 289 (detection frequency 8.5 instances/min),and for the CDM the number was 46 (detection frequency 1.35instances/min); while for the 32-channel configuration, the num-ber of instances detected through the application of the LM was237 (detection frequency 7 instances/min), and 42 for the CDM(detection frequency 1.2 instances/min). Thus, for both EEGconfigurations, the number and frequency of detections dropsdramatically using the CDM, while the LM still permits a largernumber of detections. In addition, the histogram of interdetec-tion intervals in the case of the LM and 128-channel montageis shown in Fig. 7. Unfortunately, also in the case of the LM,the number of detections is too poor to confirm the power-lawbehavior highlighted for the normal sleep case. Furthermore,the poor number of detections using the CDM implies the im-possibility of an effective estimation of all the features. Alsofor subject 2, instances show the same tightness of the SSOsdetected by using the CDM, as shown in Table VI. Moreover,the spindle grouping was verified also for this case, but, irre-spective of the montages and detection methods, no significantmodulation effect emerged and only about half (from 55% to

Fig. 8. For the altered sleep case, (row a) origin and (row b) detection maps.The numbers of electrodes involved in the analysis are 128 and 32.

Fig. 9. Features distributions and box plots related to the altered sleep case.For each panel, in the first column distributions derived from the applicationof the LM are depicted while in the second one there are those derived fromthe CDM. (a) NP amplitude (in microvolts). (b) NP time (in milliseconds).(c) NP slope (in microvolts per millisecond). (d) ZN slope (in microvolts permillisecond). (e) instance speed (in meters per second). (f) Instance size ratio.

57%) of the instances showed a spindle modulation. However,to the authors’ knowledge, no studies have been published onspindle modulation for these altered sleep conditions. With re-gard to SSO origins and detections (see Fig. 8), the topologicaldistributions derived from the LM are similar for 32 and 128configurations and in line with current findings in literature. Onthe contrary, maps obtained applying CDM (especially originmaps) are rather different from one another, and more impor-tantly, the origins seem to be mostly located in the central regionof the scalp. This is probably related to the very low number ofdetections, so the presence of outliers becomes extremely signif-icant. From the results in Fig. 9, the very poor statistics of datagathered from CDM is evident, while data obtained from theLM appear to be more reliable and able to give a satisfying de-scription of SSO instances, even in this extreme case. Moreover,


also for the recording of subject 2, data obtained from 128- and32-channel analysis match well, confirming positive indicationsabout the effectiveness of the LM on low-density (32-channel)recordings. This statement holds for all features, except for in-stance speed, considering the whole sets of instances detectedstarting from the two EEG montages (all unpaired t-tests werenot significant) and focusing on the subset of instances detectedfrom both montages (all paired t-tests were not significant). Onthe contrary, as previously highlighted, the instance speed esti-mation was affected by the EEG montage downsampling. Fur-ther development in the SSO speed estimation will be focusedon solving this limitation.

VI. CONCLUSION

The proposed method, following the three fundamental stepsstated shortly, presented positive indications for its effective-ness, although we report its application to only two subjects. Asa first step, we verified the applicability of the method to high-density EEG recordings. Results were compared with literaturedata and with results obtained through the application of theCDM, which still represents a gold standard in the SSO study.Our findings showed to be fully compatible with the currentliterature and the specificity of our approach was strengthenedby the evaluation of instance tightness values, which represent areliable measure of similarity between waves. In particular, in-stances detected by the LM were composed by SSOs that weremore similar to each other than those within instances detectedby the CDM. More, our method detected a greater number ofSSO instances than the CDM, allowing a more detailed de-scription of the SSO phenomenon. As a second step, promisingindications on the effectiveness of the method in the analysis oflow-density EEG recordings were obtained, also if the reducednumber of subjects tested constitutes a limitation. To this aim,we studied the stability of the method by varying the numberof active electrodes (128-, 64-, 32-, 20-, 10- channel). By theinvestigation of features, such as instance size ratio, instancespeed, and NP amplitude, we demonstrated sound reliability ofthe method with a sparse 32-channel configuration. As a laststep, we tested the applicability of the LM to an EEG recordingof altered sleep. Also, in this extreme case, we obtained reliableresults from both a high-density configuration (128-channel)and a low-density one (32-channel) while the application of theCDM partially failed in SSO characterization. It is fair to un-derline that CDM was designed to analyze high-density EEGrecordings of normal sleep, so that its partial malfunctioning inthis extreme condition was expected. In conclusion, we gener-alized the CDM [8], with its assessment of the traveling natureof SSOs, and we extended it so as to work on both alteredsleep recordings and low-density EEG configurations. Indeed,the proposed LM proved to be well suited for the study of sleepthrough low-density EEG recordings (down to 32 channels).Moreover, we obtained a first proof of its applicability also inconditions of altered sleep.

The authors are aware that this proof-of-concept study witha limited number of subjects will require careful future valida-tions with more subjects per sleep class, but we believe that the

method proposed could be an interesting and potentially signif-icant contribution to the field of SSO applications, taking intoaccount that the method seems to work not only for high-densityelectrode configurations, but also for clinical EEG montages of32 channels, and also on altered sleep EEGs. The generalizationto a sparse electrode array configuration of clinical use of theSSO instance study could facilitate investigations in clinical tri-als. These findings could grant the applicability of SSO analysiswith the LM to a wide range of neurophysiological and clinicalstudies, opening up the possibility of investigating sleep functionin normal and pathological conditions by SSO characterization.This novel approach could thus improve the understanding ofthe pathophysiology underlying sleep disorders, both in primarysleep disorders and also in mental or neurodegenerative ones:further studies are needed to cope with this challenge.

ACKNOWLEDGMENT

Authors thank Prof. P. Grigolini and Dr. P. Allegrini for theiruseful comments on the paper and Eng. M. Rossi and Dr. G.Evan for their technical assistance during EEG recordings.

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Andrea Piarulli received the M.S. degree in electronic engineering from theUniversity of Pisa, Pisa, Italy, in 2007.

He is currently with Scuola Superiore Sant’Anna, Pisa. His current researchinterests include biosignal analysis and telemedicine.

Danilo Menicucci received the M.S. degree in applied physics and the Ph.D.degree in neuroscience from the University of Pisa, Pisa, Italy.

He is currently a Postdoctoral Fellow with the National Research Council,Pisa. His current research interests include the neurobiology of sleep as well asof emotions and it focuses on stress modulation on these functions.

Angelo Gemignani received the M.S. degree in medicine and the Ph.D. degreein basic neuroscience from the University of Pisa, Pisa, Italy.

He was a Resident in psychiatry with the University of Pisa. For three yearshe was with the Laboratory of Neurobiophysics, Institut National de la Sante etde la Recherche Medicale (INSERM U438), Grenoble, France, where he wasengaged in the post-processing of fMRI signal during actual movement andmotor imagery. He is currently a Researcher with the Department of Physiolog-ical Sciences, Faculty of Medicine, Pisa. His current research interests includesleep psychophysiology, cognitive and emotional modulation of brain, cognitiveneuroscience, and experimental psychology and neurophysiology.

Umberto Olcese received the B.S. and M.S. degrees in biomedical engineeringfrom University of Pisa, Pisa, Italy. He is currently working toward the Ph.D.degree at PERCRO Laboratory, Scuola Superiore Sant’Anna, Pisa.

His current research interests include the role of sleep in memory consolida-tion and the analysis of cortical signals.

Paola d’Ascanio received the M.S. degree in biological sciences and the Ph.D.degree in neuroscience from the University of Pisa, Pisa, Italy.

She is currently an Associate Professor with the Faculty of Medicine, Univer-sity of Pisa, Pisa, Italy. She was engaged in the investigation of some mechanismsinvolved in the central control of posture and some aspects of plastic mecha-nisms in the adult brain. She collaborated with NASA for the development of theSpace Mission Neurolab STS-90. Her current research interests include studyof basic mechanisms of sleep–waking cycle and signal processing.

Alessandro Pingitore received the M.S. degree in medicine from the Universityof Pisa, Pisa, Italy, in 1991, and received the Ph.D. degree in cardiovascularphysiopathology from the University of Milan, Milan, Italy, in 1997.

Since 2002, he has been a Permanent Scientist with the National ResearchCouncil, Institute of Clinical Physiology, Pisa. He has authored or coauthoredmore than 60 published technical papers. His current research interests includehuman cardiovascular physiology of the extreme conditions and thyroid andheart relationship in normal and pathopysiological states.

Remo Bedini received the M.S. degree in electronic engineering from the Uni-versity of Pisa, Pisa, Italy, in 1970.

He is the author or coauthor of more than 200 technical papers and of six in-ternational patents. He currently leads the Medical Electronics and TelemedicineDepartment, National Research Council, Pisa. His current research inter-ests include biomedical investigations of the human physiology in extremeenvironment.

Alberto Landi (M’95) received the M.S. degree in electrical engineering fromthe University of Genova, Genova, Italy, in 1986, and the Ph.D. degree in elec-trical engineering from the University of Pisa, Pisa, Italy, in 1991.

He is currently a Full Professor of automatic control with the Departmentof Electric Systems and Automation, University of Pisa, where he is engagedin teaching courses of fundaments of automatic control, physiological controlsystems, and process control. He has authored or coauthored more than 60published technical papers. His current research interests include mathematicalmodels of physiological systems, sleep slow oscillations, and nonlinear identi-fication and control.

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