An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

Lucia Billeci, Alessandro Tonacci, Gennaro Tartarisco, Antonio Narzisi, Simone Di Palma, Daniele Corda, Giovanni Baldus, Federico Cruciani, Salvatore M. Anzalone, Sara Calderoni, Giovanni Pioggia, Filippo Muratori, Silvio Bonfiglio, Cristiano Paggetti, Koushik Maharatna, Valentina Bono, Mark Donnelly, Leo Galway, Mayrose Francisa, Angele Giuliano & 6 others Fabio Apicella, Chiara Lucentini, Federico Sicca, Mohamed Chetouani, David Cohen, Jean Xavier

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.
LanguageEnglish
JournalFrontiers in Neuroscience
Volume10
Issue number276
DOIs
Publication statusPublished - 21 Jun 2016

Fingerprint

Technology
Electroencephalography
Brain
Heart Rate
Feasibility Studies
Therapeutics
Electrocardiography
Autism Spectrum Disorder

Keywords

  • Autism Spectrum Disorders (ASD)
  • quantitative EEG (QEEG)
  • electrocardiogram (ECG)
  • wearable sensors
  • monitoring
  • naturalistic
  • personalization
  • imitation

Cite this

Billeci, Lucia ; Tonacci, Alessandro ; Tartarisco, Gennaro ; Narzisi, Antonio ; Di Palma, Simone ; Corda, Daniele ; Baldus, Giovanni ; Cruciani, Federico ; Anzalone, Salvatore M. ; Calderoni, Sara ; Pioggia, Giovanni ; Muratori, Filippo ; Bonfiglio, Silvio ; Paggetti, Cristiano ; Maharatna, Koushik ; Bono, Valentina ; Donnelly, Mark ; Galway, Leo ; Francisa, Mayrose ; Giuliano, Angele ; Apicella, Fabio ; Lucentini, Chiara ; Sicca, Federico ; Chetouani, Mohamed ; Cohen, David ; Xavier, Jean. / An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies. In: Frontiers in Neuroscience. 2016 ; Vol. 10, No. 276.
@article{078102aaa6a64c9b8824c66074495d9c,
title = "An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies",
abstract = "Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.",
keywords = "Autism Spectrum Disorders (ASD), quantitative EEG (QEEG), electrocardiogram (ECG), wearable sensors, monitoring, naturalistic, personalization, imitation",
author = "Lucia Billeci and Alessandro Tonacci and Gennaro Tartarisco and Antonio Narzisi and {Di Palma}, Simone and Daniele Corda and Giovanni Baldus and Federico Cruciani and Anzalone, {Salvatore M.} and Sara Calderoni and Giovanni Pioggia and Filippo Muratori and Silvio Bonfiglio and Cristiano Paggetti and Koushik Maharatna and Valentina Bono and Mark Donnelly and Leo Galway and Mayrose Francisa and Angele Giuliano and Fabio Apicella and Chiara Lucentini and Federico Sicca and Mohamed Chetouani and David Cohen and Jean Xavier",
note = "Reference text: Althaus, M., Mulder, L.J.M., Mulder, G., Aarnoudse, C.C., Minderaa, R.B., (1999). Cardiac adaptivity to attention-demanding tasks in children with a pervasive developmental disorder not otherwise specified (PDD-NOS), Biol. Psychiatry, 46(6), 799–809. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. Balocchi, R., Menicucci, D., Santarcangelo, E., Sebastiani, L., Gemignani, A., Ghelarducci, B., et al. (2004). Deriving the respiratory sinus arrhytmia from the heartbeat time series using empirical mode decomposition. Chaos, Solitons & Fractals 20(1), 171-177. Bauer, M., Oostenveld, R., and Fries, P. (2009). Tactile stimulation accelerates behavioral responses to visual stimuli through enhancement of occipital gamma-band activity. Vision Res. 49(9), 931-942. Belmonte, M.K., Allen, G., Beckel-Mitchener, A., Boulanger, L.M., Carper, R.A., and Webb, S.J. (2004). Autism and abnormal development of brain connectivity. J Neurosci. 24(42), 9228–9231. Billeci, L., Sicca, F., Maharatna, K., Apicella, F., Narzisi, A., Campatelli, G., et al. (2013). On the application of quantitative EEG for characterizing autistic brain: a systematic review. Front Hum Neurosci. 7, 442. Billeci, L., Tartarisco, G., Brunori, E., Crifaci, G., Scardigli, S., Balocchi, R., et al. (2015). The role of wearable sensors and wireless technologies for the assessment of heart rate variability in anorexia nervosa. Eat Weight Disord. 20(1), 23-31. Billeci, L., Narzisi, A., Campatelli, G., Crifaci, G., Calderoni, S., Gagliano, A., Calzone, C., Colombi, C., Pioggia, G., Muratori, F., and ALERT group. (2016). Disentangling the initiation from the response in joint attention: an eye-tracking study in toddlers with autism spectrum disorders. Translational Psychiatry, 17:6:e808. B{\"o}lte, S., Bartl-Pokorny, K.D., Jonsson, U., Berggren, S., Zhang, D., Kostrzewa, E., et al. (2016). How can clinicians detect and treat autism early? Methodological trends of technology use in research. Acta Paediatr. 105(2), 137-144. Bono, V., Narzisi, A., Jouen, A.L., Tilmont, E., Hommel, S., Jamal, W., Xavier, J., Billeci, L., Maharatna, K., Wald, M., Chetouani, M., Cohen, D., Muratori F. and MICHELANGELO Study Group. (2016). GOLIAH: A Gaming Platform for Home-Based Intervention in Autism – Principles and Design. Frontiers in Psychiatry, 7. Burns, A., Greene, B.R., McGrath, M.J., O'Shea, T.J., Kuris, B., Ayer, S.M., et al. (2010). SHIMMERTM – A wireless sensor platform for noninvasive biomedical research. IEEE Sensors Journal 10, 1527-1534. Cantor, D.S., and Chabot, R. (2009). QEEG studies in the assessment and treatment of childhood disorders. Clin EEG Neurosci. 40(2), 113-121. Cester, I., Dunne, S., Riera, A., and Ruffini, G. (2008). ENOBIO: wearable, wireless, 4-channel electrophysiology recording system optimized for dry electrodes. In: International Workshop on Wearable Micro and Nanosystems for Personalised Health, Valencia, Spain, 2008. Chevallier, C., Kohls, G., Troiani, V., Brodkin, E.S. and Schultz, R.T. (2012). The social motivation theory of autism. Trends Cogn Sci. 16(4), 231–9. Coben, R., Clarke, A.R., Hudspeth, W., and Barry, R.J. (2008), EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol. 119(5), 1002–1009. Cruciani, F., Donnelly, M.P., Nugent, C.D., Parente, G., Paggetti, C., and Burns, W. (2011). DANTE: a video based annotation tool for smart environments. Sensor Systems and Software Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 57, 179-188. Dawson G, Meltzoff, A.N., Osterling, J., Rinaldi, J. and Brown, E. (1998). Children with autism fail to orient to naturally occurring social stimuli. J Autism Dev Disord. 28(6):479–85. Dawson, G., Jones, E.J., Merkle, K., Venema, K., Lowy, R., Faja, S., et al. (2012). 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Billeci, L, Tonacci, A, Tartarisco, G, Narzisi, A, Di Palma, S, Corda, D, Baldus, G, Cruciani, F, Anzalone, SM, Calderoni, S, Pioggia, G, Muratori, F, Bonfiglio, S, Paggetti, C, Maharatna, K, Bono, V, Donnelly, M, Galway, L, Francisa, M, Giuliano, A, Apicella, F, Lucentini, C, Sicca, F, Chetouani, M, Cohen, D & Xavier, J 2016, 'An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies', Frontiers in Neuroscience, vol. 10, no. 276. https://doi.org/10.3389/fnins.2016.00276

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies. / Billeci, Lucia; Tonacci, Alessandro; Tartarisco, Gennaro; Narzisi, Antonio; Di Palma, Simone; Corda, Daniele; Baldus, Giovanni; Cruciani, Federico; Anzalone, Salvatore M.; Calderoni, Sara; Pioggia, Giovanni; Muratori, Filippo; Bonfiglio, Silvio; Paggetti, Cristiano; Maharatna, Koushik; Bono, Valentina; Donnelly, Mark; Galway, Leo; Francisa, Mayrose; Giuliano, Angele; Apicella, Fabio; Lucentini, Chiara; Sicca, Federico; Chetouani, Mohamed; Cohen, David; Xavier, Jean.

In: Frontiers in Neuroscience, Vol. 10, No. 276, 21.06.2016.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

AU - Billeci, Lucia

AU - Tonacci, Alessandro

AU - Tartarisco, Gennaro

AU - Narzisi, Antonio

AU - Di Palma, Simone

AU - Corda, Daniele

AU - Baldus, Giovanni

AU - Cruciani, Federico

AU - Anzalone, Salvatore M.

AU - Calderoni, Sara

AU - Pioggia, Giovanni

AU - Muratori, Filippo

AU - Bonfiglio, Silvio

AU - Paggetti, Cristiano

AU - Maharatna, Koushik

AU - Bono, Valentina

AU - Donnelly, Mark

AU - Galway, Leo

AU - Francisa, Mayrose

AU - Giuliano, Angele

AU - Apicella, Fabio

AU - Lucentini, Chiara

AU - Sicca, Federico

AU - Chetouani, Mohamed

AU - Cohen, David

AU - Xavier, Jean

N1 - Reference text: Althaus, M., Mulder, L.J.M., Mulder, G., Aarnoudse, C.C., Minderaa, R.B., (1999). Cardiac adaptivity to attention-demanding tasks in children with a pervasive developmental disorder not otherwise specified (PDD-NOS), Biol. Psychiatry, 46(6), 799–809. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. Balocchi, R., Menicucci, D., Santarcangelo, E., Sebastiani, L., Gemignani, A., Ghelarducci, B., et al. (2004). Deriving the respiratory sinus arrhytmia from the heartbeat time series using empirical mode decomposition. Chaos, Solitons & Fractals 20(1), 171-177. Bauer, M., Oostenveld, R., and Fries, P. (2009). Tactile stimulation accelerates behavioral responses to visual stimuli through enhancement of occipital gamma-band activity. Vision Res. 49(9), 931-942. Belmonte, M.K., Allen, G., Beckel-Mitchener, A., Boulanger, L.M., Carper, R.A., and Webb, S.J. (2004). Autism and abnormal development of brain connectivity. J Neurosci. 24(42), 9228–9231. Billeci, L., Sicca, F., Maharatna, K., Apicella, F., Narzisi, A., Campatelli, G., et al. (2013). On the application of quantitative EEG for characterizing autistic brain: a systematic review. Front Hum Neurosci. 7, 442. Billeci, L., Tartarisco, G., Brunori, E., Crifaci, G., Scardigli, S., Balocchi, R., et al. (2015). The role of wearable sensors and wireless technologies for the assessment of heart rate variability in anorexia nervosa. Eat Weight Disord. 20(1), 23-31. Billeci, L., Narzisi, A., Campatelli, G., Crifaci, G., Calderoni, S., Gagliano, A., Calzone, C., Colombi, C., Pioggia, G., Muratori, F., and ALERT group. (2016). Disentangling the initiation from the response in joint attention: an eye-tracking study in toddlers with autism spectrum disorders. Translational Psychiatry, 17:6:e808. Bölte, S., Bartl-Pokorny, K.D., Jonsson, U., Berggren, S., Zhang, D., Kostrzewa, E., et al. (2016). 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In: International Workshop on Wearable Micro and Nanosystems for Personalised Health, Valencia, Spain, 2008. Chevallier, C., Kohls, G., Troiani, V., Brodkin, E.S. and Schultz, R.T. (2012). The social motivation theory of autism. Trends Cogn Sci. 16(4), 231–9. Coben, R., Clarke, A.R., Hudspeth, W., and Barry, R.J. (2008), EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol. 119(5), 1002–1009. Cruciani, F., Donnelly, M.P., Nugent, C.D., Parente, G., Paggetti, C., and Burns, W. (2011). DANTE: a video based annotation tool for smart environments. Sensor Systems and Software Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 57, 179-188. Dawson G, Meltzoff, A.N., Osterling, J., Rinaldi, J. and Brown, E. (1998). Children with autism fail to orient to naturally occurring social stimuli. J Autism Dev Disord. 28(6):479–85. Dawson, G., Jones, E.J., Merkle, K., Venema, K., Lowy, R., Faja, S., et al. (2012). 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PY - 2016/6/21

Y1 - 2016/6/21

N2 - Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.

AB - Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.

KW - Autism Spectrum Disorders (ASD)

KW - quantitative EEG (QEEG)

KW - electrocardiogram (ECG)

KW - wearable sensors

KW - monitoring

KW - naturalistic

KW - personalization

KW - imitation

U2 - 10.3389/fnins.2016.00276

DO - 10.3389/fnins.2016.00276

M3 - Article

VL - 10

JO - Frontiers in Neuroscience

T2 - Frontiers in Neuroscience

JF - Frontiers in Neuroscience

SN - 1662-4548

IS - 276

ER -