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HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)

Authors:

Steven A. Hicks,

Andrea Stautland,

Ole Bernt Fasmer,

Wenche Førland,

Hugo Lewi Hammer,

Pål Halvorsen,

Kristin Mjeldheim,

Ketil Joachim Oedegaard,

Vigdis Elin Giæver Syrstad,

Michael Alexander Riegler,

Petter JakobsenAuthors Info & Claims

MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference

Pages 314 - 319

https://doi.org/10.1145/3458305.3478454

Published: 22 September 2021 Publication History

Abstract

Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirements that come with patient-related data. Consequentially, many algorithms and models get published on the same topic without a standard benchmark to measure against. Therefore, this paper presents HYPERAKTIV, a public dataset containing health, activity, and heart rate data from patients diagnosed with attention deficit hyperactivity disorder, better known as ADHD. The dataset consists of data collected from 51 patients with ADHD and 52 clinical controls. In addition to the activity and heart rate data, we also include a series of patient attributes such as their age, sex, and information about their mental state, as well as output data from a computerized neuropsychological test. Together with the presented dataset, we also provide baseline experiments using traditional machine learning algorithms to predict ADHD based on the included activity data. We hope that this dataset can be used as a starting point for computer scientists who want to contribute to the field of mental health, and as a common benchmark for future work in ADHD analysis.

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Cited By

Bouchouras GSofianidis GKotis K(2024)Temporal Anomaly Detection in Attention-Deficit/Hyperactivity Disorder Using Recurrent Neural NetworksCureus10.7759/cureus.76496Online publication date: 27-Dec-2024
https://doi.org/10.7759/cureus.76496
Santarrosa-López IAlor-Hernández GBustos-López MHernández-Capistrán JSánchez-Morales LSánchez-Cervantes JMarín-Vega H(2024)DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and ElectroencephalographyBig Data and Cognitive Computing10.3390/bdcc90100039:1(3)Online publication date: 30-Dec-2024
https://doi.org/10.3390/bdcc9010003
Thelagathoti RAli H(2024)Mobility-Based Community Analysis for Early Detection of Complex Psychiatric Disorders2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI61247.2024.00034(205-213)Online publication date: 3-Jun-2024
https://doi.org/10.1109/ICHI61247.2024.00034
Show More Cited By

Index Terms

HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Cross-validation
    2. Learning paradigms
      1. Supervised learning

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cover image ACM Conferences

MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference

June 2021

254 pages

ISBN:9781450384346

DOI:10.1145/3458305

General Chairs:
Özgü Alay
University of Oslo and Simula Metropolitan
,
Cheng-Hsin Hsu
National Tsing Hua University
,
Ali C. Begen
Ozyegin University and Networked Media

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

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SIGMM: ACM Special Interest Group on Multimedia

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Published: 22 September 2021

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MMSys '21

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MMSys '21: 12th ACM Multimedia Systems Conference

September 28 - October 1, 2021

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MMSys '21 Paper Acceptance Rate 18 of 55 submissions, 33%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Bouchouras GSofianidis GKotis K(2024)Temporal Anomaly Detection in Attention-Deficit/Hyperactivity Disorder Using Recurrent Neural NetworksCureus10.7759/cureus.76496Online publication date: 27-Dec-2024
https://doi.org/10.7759/cureus.76496
Santarrosa-López IAlor-Hernández GBustos-López MHernández-Capistrán JSánchez-Morales LSánchez-Cervantes JMarín-Vega H(2024)DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and ElectroencephalographyBig Data and Cognitive Computing10.3390/bdcc90100039:1(3)Online publication date: 30-Dec-2024
https://doi.org/10.3390/bdcc9010003
Thelagathoti RAli H(2024)Mobility-Based Community Analysis for Early Detection of Complex Psychiatric Disorders2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI61247.2024.00034(205-213)Online publication date: 3-Jun-2024
https://doi.org/10.1109/ICHI61247.2024.00034
V. SS.D. M(2024)Optimal interval and feature selection in activity data for detecting attention deficit hyperactivity disorderComputers in Biology and Medicine10.1016/j.compbiomed.2024.108909179:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108909
Misgar MBhatia M(2024)Advancing ADHD diagnosis: using machine learning for unveiling ADHD patterns through dimensionality reduction on IoMT actigraphy signalsInternational Journal of Information Technology10.1007/s41870-024-01895-xOnline publication date: 7-May-2024
https://doi.org/10.1007/s41870-024-01895-x
Thelagathoti RAli H(2023)A Network Analysis Approach for the Classification of Psychiatric Disorders Using Multi-Modal Data2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385720(2831-2836)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385720
Hernández-Capistran JSánchez-Morales LAlor-Hernández GBustos-López MSánchez-Cervantes J(2023)Machine and Deep Learning Algorithms for ADHD Detection: A ReviewInnovations in Machine and Deep Learning10.1007/978-3-031-40688-1_8(163-191)Online publication date: 29-Sep-2023
https://doi.org/10.1007/978-3-031-40688-1_8
Kaur AKahlon K(2022)Accurate Identification of ADHD among Adults Using Real-Time Activity DataBrain Sciences10.3390/brainsci1207083112:7(831)Online publication date: 26-Jun-2022
https://doi.org/10.3390/brainsci12070831

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