Evaluation Methodology of Emotional States for People With Cerebral Palsy

NCT05621057 | Active Not Recruiting

• 1 other identifier: 2022_D6
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

Brief Summary

The objective of this study is to determine what are the most robust parameters for the measurement of emotional states in users suffering from cerebral palsy. Users have different ages (adults and children) with different capacities. Measures will be taken in different contexts where users will do several tasks pleasant and unpleasant. Some of the tasks involve physical activity, which must be taken into account due to the possible disturbance that it can introduce in the measures taken. It is intended to detect states of demotivation, fatigue, or physical or emotional stress. For this, we will use signals of two types: physiological measurements and inertial sensors. The handicap we find is that the subjects have difficulties expressing and recognizing emotional states, which rules out the use of a self-assessment test to contrast the measures taken. This makes us turn to their caregivers or family members or alternatively or in a complementary way to take measurements in contexts or situations of daily life where the emotional state induced in the subject is known. Once the parameters were established, the measurement of the emotional state will allow us to make a real-time evaluation of how the users are feeling during the tasks, in this way the activity can be better conducted by adapting it so that it is as efficient as possible and takes us to good results. Music will be studied as a motivating factor and for improving the emotional state when approaching rehabilitation therapies. There will be 4 sessions during which measurements will be recorded. 1: measurement of this parameter when he or she is in an activity of daily life that is pleasurable. 2: measurement of this parameter when he or she is in an activity of daily life that is of discomfort. 3: Measurement of this parameter during the performance of rehabilitation activities. 4: Measurement of this parameter during rehabilitation activities accompanied with music according to the preferences.

Conditions

Cerebral Palsy; Physically Challenged; Emotional Adjustment

Keywords

emotional measure; image processing; physiological parameters

Outcome Measures

Primary Outcomes (10)

• Average kinetic energy measurements (in joules) using inertial sensors

  Four wearable devices in wrist, ankle, chest and head are used. All have inertial units. They provide information in the different contexts (scheduled sessions) about the energy expenditure that these entail. It should be studied whether this parameter is related to the emotional state.

  Fifteen minutes.

• Instantaneous Heart Rate (in seconds)

  We used a wearable placed in the chest with Ag/AgCl electrodes for ECG, placed following the Einthoven's II lead positions. The position of R wave is determined using an appropriate algorithm and then time difference between two consecutive R waves is calculated, this time difference is used to calculed HR. We used 30s-length sliding windows with an overlap of 50%. The instantaneous HR is given by the average HR in such a window after removing the outliers.

  Fifteen minutes.

• The ratio between low frequency, (LF) and high frequency, (HF), (LF/HF)

  We used a wearable placed in the chest with Ag/AgCl electrodes for Electrocardiogram (ECG) ,placed following the Einthoven's II lead positions. The ratio between low frequency , \[0.04 - 0.15\] Hz (LF) and high frequency, \[0.15 - 4\] Hz (HF) components of HRV, (LF/HF), shows the balance between the SNS (Sympathetic Nervous System) and the PNS (Parasympathetic Nervous System).

  Fifteen minutes.

• Temporal parameters of Heart Rate Variability (HRV)

  We used a wearable placed in the chest with Ag/AgCl electrodes for Electrocardiogram (ECG) ,placed following the Einthoven's II lead positions. The HRV is especially interesting because it allows to assess the activity of the parasympathetic and sympathetic pathways of the ANS (Autonomic Nervous System). HVR can be measured using temporal parameters such as: SDNN Standard deviation of NN intervals; RMSSD Root mean square of successive differences between normal heartbeats; pNN50 Percentage of successive RR intervals that differ by more than 50 ms.

  Fifteen minutes.

• Tonic Skin Conductance Level (SCL)

  This signal is the background tonic of the Electrodermal Activity signal (EDA). It will be measured by dry electrodes that were placed on the hearten and hypothenar eminences of the dominant hand.

  Fifteen minutes.

• Parameters of Phasic Skin Conductance Response (SCR)

  It will be measured by dry electrodes that were placed on the hearten and hypothenar eminences of the dominant hand This signal are constituted by the rapid phase components of the Electrodermal Activity signal (EDA). An SCR that cannot be attributed to a distinct stimulus is referred to as non-specific skin conductance response (NS-SCR). This category includes the spontaneous fluctuations in skin conductance that are our case because we measured the signal in periods without stimulus.

  Fifteen minutes.

• Fractal dimension of Electroencephalogram signal (EEG)

  The EEG portrays the functioning of the brain. the recording of those signals will be done at a sampling rate of 125 Hz by OpenBCI. OpenBCI (https://openbci.com/) is a low-cost open-hardware device for the measure of EEG signals using 16 channels in positions FP1, FP2, F1, F2, F5, F6, Cz, C3, C4, T7, T8, Pz, P3, P4, O1, O2. The EEG signals are highly complex and dynamic in nature. Fractal dimension (FD) is emerging as a novel feature for computing its complexity. We will use the Higuchi's algorithm.

  Fifteen minutes.

• Spectral Entropy (SE) of EEG signal

  The EEG portrays the functioning of the brain. the recording of those signals will be done at a sampling rate of 125 Hz by OpenBCI. SE can be used for computing EEG complexity. To do that, the power spectral density (PSD) must be obtained as a first step . After normalizing the PSD by the number of bins, which can be viewed as a probability density function conversion, the classical Shannon's entropy for information systems is then calculated.

  Fifteen minutes.

• EEG coherence

  The interactions between neural systems, operating in each frequency band, are estimated by means of the EEG coherence. While neural synchronization influences EEG amplitude, the coherence between signals captured by one pair of electrodes refers to the consistence and stability of the signal amplitude and its phase. Two brain areas connected should show a signal delay in time domain that is measured as a phase shift in the frequency domain.

  Fifteen minutes.

• Activity of the regions in the brain

  We will use Loreta. Loreta is a specific solution to the inverse problem, using algorithms that localize the cortical generators of the observed neuronal firing.

  Fifteen minutes.

Secondary Outcomes (6)

• International Classification of Functioning, Disability and Health (ICF)

  Through study completion, an average of 2 weeks.

• Gross Motor Function Classification System (GMFCS)

  Through study completion, an average of 2 weeks.

• Manual Ability Classification System (MACS)

  Through study completion, an average of 2 weeks.

• Communication Function Classification System (CFCS)

  Through study completion, an average of 2 weeks.

• KIDSCREEN Questionnaire

  Through study completion, an average of 2 weeks.

Eligibility Criteria

• Age: 5 Years - 55 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Probability Sample

Study Population

The population who took part in this study attend two different centers for people with special needs: Asociación Sevillana de Parálisis Cerebral (ASPACE) and Centro Específico de Educación Especial Mercedes Sanromá (CEEEMS). ASPACE is a private organization catering mainly for adults with CP. The other center, CEEEMS, is a public specialist school that forms part of the educational network in Andalusia (Spain) and deals mainly with children and teenagers with motor dysfunctions (including CP).

You may qualify if:

• People with a recognized disability, caused by a permanent illness or health situation.

You may not qualify if:

• Present any health situation that is incompatible with the use of assistive technology designed and prototype in the project.
• Have a very limited cognitive ability, which prevents you from following the instructions for the proper use of assistive technology.
• Not having adequate human support.

Sponsors & Collaborators

• University of Seville: lead

Study Sites (1)

Isabel M. Gomez

Seville, Andalusia, 41012, Spain

Location

Related Publications (15)

• R. Martinez; A. Salazar-Ramirez; A. Arruti; E. Irigoyen; J. I. Martin; J. Muguerza. A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis. 2019. IEEE Access PP(99):1-1

  BACKGROUND

• Can Y.S., Chalabianloo N., Ekiz D., Fernandez-Alvarez J., Repetto C., Riva G., Iles-Smith H., Ersoy C. Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information. IEEE Sensors Journal. 2020.

  BACKGROUND

• Rincon JA, Costa A, Novais P, Julian V, Carrascosa C. ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment. Sensors (Basel). 2020 Feb 5;20(3):852. doi: 10.3390/s20030852.

  PMID: 32033498 BACKGROUND

• Correa, J.A.M.; Abadi, M.K.; Sebe, N.; Patras, I. Amigos: a dataset for affect, personality and mood research on individuals and groups. IEEE Trans. Affect. Comput. 2018.

  BACKGROUND

• Price E., Moore G., Galway L., Linden M. Towards mobile cognitive fatigue assessment as indicated by physical, social, environmental, and emotional factors. IEEE Access. 2019.

  BACKGROUND

• Qureshi S., Hagelbäck J., Iqbal S.M.Z., Javaid H., Lindley C.A. Evaluation of classifiers for emotion detection while performing physical and visual tasks: Tower of Hanoi and IAPS. Intelligent Systems Conference 2018.

  BACKGROUND

• Belmonte S, Montoya P, Gonzalez-Roldan AM, Riquelme I. Reduced brain processing of affective pictures in children with cerebral palsy. Res Dev Disabil. 2019 Nov;94:103457. doi: 10.1016/j.ridd.2019.103457. Epub 2019 Sep 11.

  PMID: 31520963 BACKGROUND

• Albiol-Pérez S., Cano S., Da Silva M.G., Gutierrez E.G., Collazos C.A., Lombano J.L., Estellés E., Ruiz M.A. A novel approach in virtual rehabilitation for children with cerebral palsy: Evaluation of an emotion detection system. Advances in Intelligent Systems and Computing. 2018.

  BACKGROUND

• C. Rosales; L. Jácome; J. Carrión; C. Jaramillo; M. Palma. Computer vision for detection of body expressions of children with cerebral palsy.2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM).

  BACKGROUND

• Kalansooriya P., Ganepola G.A.D,Thalagala T.S. Affective gaming in real-time emotion detection and Smart Computing music emotion recognition: Implementation approach with electroencephalogram. Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2020.

  BACKGROUND

• Molina Cantero, Alberto Jesus, Gómez González, Isabel María, Merino Monge, Manuel, Castro García, Juan Antonio, Cabrera Cabrera, Rafael: Emotions detection based on a single-electrode EEG device. Comunicación en congreso. 4 ª International Conference on Physiological Computing Systems. - Madrid,. 2017

  BACKGROUND

• Merino M, Gomez I, Molina AJ. EEG feature variations under stress situations. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6700-3. doi: 10.1109/EMBC.2015.7319930.

  PMID: 26737830 BACKGROUND

• Merino Monge, Manuel, Gómez González, Isabel María, Castro García, Juan Antonio, Molina Cantero, Alberto Jesus, Quesada, Roylan: A Preliminary Study about the Music Influence on EEG and ECG Signals. Comunicación en congreso. 5th International Conference on Physiological Computing Systems. Sevilla. 2018

  BACKGROUND

• Castro García, Juan Antonio, Molina Cantero, Alberto Jesus, Merino Monge, Manuel, Gómez González, Isabel María: An Open-Source Hardware Acquisition Platform for Physiological Measurements. En: IEEE Sensors Journal. 2019. Vol. 19. 10.1109/Jsen.2019.2933917

  BACKGROUND

• Gomez-Gonzalez IM, Castro-Garcia JA, Merino-Monge M, Sanchez-Anton G, Hamidi F, Mendoza-Sagrera A, Molina-Cantero AJ. Emotional State Measurement Trial (EMOPROEXE): A Protocol for Promoting Exercise in Adults and Children with Cerebral Palsy. J Pers Med. 2024 May 14;14(5):521. doi: 10.3390/jpm14050521.

  PMID: 38793103 DERIVED

MeSH Terms

Conditions

Cerebral Palsy

Condition Hierarchy (Ancestors)

Brain Damage, Chronic; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Study Officials

• Isabel M. Gomez-Gonzalez, Phd

  University of Seville

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Target Duration: 2 Weeks
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: October 12, 2022
• First Posted: November 17, 2022
• Study Start: November 1, 2023
• Primary Completion: December 1, 2024
• Study Completion: February 3, 2025
• Last Updated: September 19, 2024

Record last verified: 2024-09

Locations

ES (1)