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Ferina J, Kruger M, Kruger U, Ryan D, Anderson C, Foster J, Hamlin T, Hahn J. Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data. J Pers Med 2023; 13:1513. [PMID: 37888124 PMCID: PMC10608042 DOI: 10.3390/jpm13101513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.
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Affiliation(s)
- Jennifer Ferina
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
| | - Melanie Kruger
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
- Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Uwe Kruger
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
| | - Daniel Ryan
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Conor Anderson
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Jenny Foster
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Theresa Hamlin
- The Center for Discovery, Harris, NY 12742, USA; (D.R.); (C.A.); (J.F.); (T.H.)
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (J.F.); (U.K.)
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Suliman A, Mowla MR, Alivar A, Carlson C, Prakash P, Natarajan B, Warren S, Thompson DE. Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging. SENSORS (BASEL, SWITZERLAND) 2023; 23:2693. [PMID: 36904896 PMCID: PMC10007206 DOI: 10.3390/s23052693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.
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