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image of Smart Health Monitoring Approach to Diagnose Attention-Deficit Hyperactivity Disorderbased on Real-Time Activity and Heart Rate Variability using Boosting Models

Abstract

Introduction

Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent chronic mental health condition that significantly impacts the psychological and physical well-being of millions of adolescents. Early detection and accurate diagnosis are crucial for effective treatment and mitigating the disorder's adverse effects. Despite extensive research efforts, current methods often fall short in simultaneously accounting for daily motor activity and heart rate variability in ADHD detection.

Method

Addressing these gaps, this paper introduces a histogram-based gradient-boosting classifier for analyzing real-time activity and heart-rate variability data to automate ADHD diagnosis. By extracting twelve key features from the data and selecting the most significant ones with the extra tree model, we evaluate these features using various classifiers, including histogram-based gradient boosting, light gradient boosting machine, extreme gradient boosting, gradient boosting, and adaptive boosting.

Results

The histogram-based gradient-boosting model, validated through ten-fold cross-validation, outperforms other classifiers with an accuracy of 99.12%, an F1 measure of 99.12%, and a sensitivity of 99.13%. Additionally, it achieves a specificity of 99.1%, an AUC of 0.9995, and a minimal FDR of 0.88%. These results demonstrate that the proposed approach offers a highly effective and precise solution for automated ADHD diagnosis.

Conclusion

The implications of these findings suggest that integrating real-time activity and heart-rate variability data into diagnostic processes can significantly enhance the accuracy and efficiency of ADHD assessment, potentially leading to earlier and more reliable diagnoses, improved patient outcomes, and more tailored treatment strategies.

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2024-11-04
2025-01-12

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References

  1. Austerman J. ADHD and behavioral disorders: Assessment, management, and an update from DSM-5. Cleve. Clin. J. Med. 2015 82 11 suppl 1 S2 S7 10.3949/ccjm.82.s1.01 26555810
    [Google Scholar]
  2. Sarkhel S. Kaplan & sadock’s synopsis of psychiatry: Behavioral sciences/clinical psychiatry. Indian J. Psychiatry 2009 51 4 331 10.4103/0019‑5545.58308
    [Google Scholar]
  3. Salari N. Ghasemi H. Abdoli N. The global prevalence of ADHD in children and adolescents: A systematic review and meta-analysis. Ital J Pediatr 2023 49 1 48 10.1186/s13052‑023‑01456‑1
    [Google Scholar]
  4. Abdelnour E. Jansen M.O. Gold J.A. ADHD diagnostic trends: Increased recognition or overdiagnosis? Mo. Med. 2022 119 5 467 473 36337990
    [Google Scholar]
  5. Rucklidge J.J. Gender differences in attention-deficit/hyperactivity disorder. Psychiatr. Clin. North Am. 2010 33 2 357 373 10.1016/j.psc.2010.01.006 20385342
    [Google Scholar]
  6. Kim J.H. Kim J.Y. Lee J. Jeong G.H. Lee E. Lee S. Lee K.H. Kronbichler A. Stubbs B. Solmi M. Koyanagi A. Hong S.H. Dragioti E. Jacob L. Brunoni A.R. Carvalho A.F. Radua J. Thompson T. Smith L. Oh H. Yang L. Grabovac I. Schuch F. Fornaro M. Stickley A. Rais T.B. Salazar de Pablo G. Shin J.I. Fusar-Poli P. Environmental risk factors, protective factors, and peripheral biomarkers for ADHD: An umbrella review. Lancet Psychiatry 2020 7 11 955 970 10.1016/S2215‑0366(20)30312‑6 33069318
    [Google Scholar]
  7. Kaur A. Kahlon K.S. Accurate identification of ADHD among adults using real-time activity data. Brain Sci. 2022 12 7 831 10.3390/brainsci12070831 35884638
    [Google Scholar]
  8. Hicks S.A. Stautland A. Fasmer OB. HYPERAKTIV: An activity dataset from patients with attention-deficit/hyperactivity disorder (ADHD). Proceedings of the 12th ACM Multimedia Systems Conference, New York, NY, USA, 2021, pp. 314-319. 10.1145/3458305.3478454
    [Google Scholar]
  9. Heller M.D. Roots K. Srivastava S. Schumann J. Srivastava J. Hale T.S. A machine learning-based analysis of game data for attention deficit hyperactivity disorder assessment. Games Health J. 2013 2 5 291 298 10.1089/g4h.2013.0058 26196929
    [Google Scholar]
  10. Loh H.W. Ooi C.P. Barua P.D. Palmer E.E. Molinari F. Acharya U.R. Automated detection of ADHD: Current trends and future perspective. Comput. Biol. Med. 2022 146 105525 10.1016/j.compbiomed.2022.105525 35468405
    [Google Scholar]
  11. Siepmann M. Weidner K. Petrowski K. Siepmann T. Heart rate variability: A measure of cardiovascular health and possible therapeutic target in dysautonomic mental and neurological disorders. Appl. Psychophysiol. Biofeedback 2022 47 4 273 287 10.1007/s10484‑022‑09572‑0 36417141
    [Google Scholar]
  12. Firouzabadi F.D. Ramezanpour S. Firouzabadi M.D. Yousem I.J. Puts N.A.J. Yousem D.M. Neuroimaging in attention-deficit/hyperactivity disorder: Recent advances. AJR Am. J. Roentgenol. 2022 218 2 321 332 10.2214/AJR.21.26316 34406053
    [Google Scholar]
  13. Qureshi M.N.I. Oh J. Min B. Jo H.J. Lee B. Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI. Front. Hum. Neurosci. 2017 11 157 10.3389/fnhum.2017.00157 28420972
    [Google Scholar]
  14. Colby J.B. Rudie J.D. Brown J.A. Douglas P.K. Cohen M.S. Shehzad Z. Insights into multimodal imaging classification of ADHD. Front. Syst. Neurosci. 2012 6 59 10.3389/fnsys.2012.00059 22912605
    [Google Scholar]
  15. Zhang-James Y. Razavi A.S. Hoogman M. Franke B. Faraone S.V. Machine learning and mri-based diagnostic models for ADHD: Are we there yet? J. Atten. Disord. 2023 27 4 335 353 10.1177/10870547221146256 36651494
    [Google Scholar]
  16. Logothetis N.K. What we can do and what we cannot do with fMRI. Nature 2008 453 7197 869 878 10.1038/nature06976 18548064
    [Google Scholar]
  17. Heeger D.J. Ress D. What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 2002 3 2 142 151 10.1038/nrn730 11836522
    [Google Scholar]
  18. Horga G. Kaur T. Peterson B.S. Annual research review: Current limitations and future directions in MRI studies of child- and adult-onset developmental psychopathologies. J. Child Psychol. Psychiatry 2014 55 6 659 680 10.1111/jcpp.12185 24438507
    [Google Scholar]
  19. Nayak K.S. Lim Y. Campbell-Washburn A.E. Steeden J. Real‐time magnetic resonance imaging. J. Magn. Reson. Imaging 2022 55 1 81 99 10.1002/jmri.27411 33295674
    [Google Scholar]
  20. Kamida A. Shimabayashi K. Oguri M. Takamori T. Ueda N. Koyanagi Y. Sannomiya N. Nagira H. Ikunishi S. Hattori Y. Sato K. Fukuda C. Hirooka Y. Maegaki Y. EEG power spectrum analysis in children with ADHD. Yonago Acta Med. 2016 59 2 169 173 27493489
    [Google Scholar]
  21. Heinrich H. Busch K. Studer P. Erbe K. Moll G.H. Kratz O. EEG spectral analysis of attention in ADHD: Implications for neurofeedback training? Front. Hum. Neurosci. 2014 8 611 10.3389/fnhum.2014.00611 25191248
    [Google Scholar]
  22. Ahire N. Awale R.N. Wagh A. Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning. Appl. Neuropsychol. Adult 2023 Aug 1 12 10.1080/23279095.2023.2247702 37647332
    [Google Scholar]
  23. Mahmoud M.B. Ali N.B. Fray S. jamoussi H. Chebbi S. Fredj M. Utility of EEG on attention deficit–hyperactivity disorder (ADHD). Epilepsy Behav. 2021 114 Pt A 107583 10.1016/j.yebeh.2020.107583 33243683
    [Google Scholar]
  24. Koh JEW. Ooi CP. Lim-Ashworth NS. Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals. Comput Biol Med 2022 140 105120 10.1016/j.compbiomed.2021.105120
    [Google Scholar]
  25. Loh H.W. Ooi C.P. Oh S.L. Barua P.D. Tan Y.R. Molinari F. March S. Acharya U.R. Fung D.S.S. Deep neural network technique for automated detection of ADHD and CD using ECG signal. Comput. Methods Programs Biomed. 2023 241 107775 10.1016/j.cmpb.2023.107775 37651817
    [Google Scholar]
  26. Yeh S.C. Lin S.Y. Wu E.H.K. Zhang K.F. Xiu X. Rizzo A. Chung C.R. A virtual-reality system integrated with neuro-behavior sensing for attention-deficit/hyperactivity disorder intelligent assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 2020 28 9 1899 1907 10.1109/TNSRE.2020.3004545 32746303
    [Google Scholar]
  27. Christiansen H. Chavanon M.L. Hirsch O. Schmidt M.H. Meyer C. Müller A. Rumpf H.J. Grigorev I. Hoffmann A. Use of machine learning to classify adult ADHD and other conditions based on the Conners’ adult ADHD rating scales. Sci. Rep. 2020 10 1 18871 10.1038/s41598‑020‑75868‑y 33139794
    [Google Scholar]
  28. Duda M. Haber N. Daniels J. Wall D.P. Crowdsourced validation of a machine-learning classification system for autism and ADHD. Transl. Psychiatry 2017 7 5 e1133 e1133 10.1038/tp.2017.86 28509905
    [Google Scholar]
  29. Kim S. Lee H.K. Lee K. Can the MMPI predict adult ADHD? An approach using machine learning methods. Diagnostics (Basel) 2021 11 6 976 10.3390/diagnostics11060976 34071385
    [Google Scholar]
  30. Varela Casal P. Lorena Esposito F. Morata Martínez I. Capdevila A. Solé Puig M. de la Osa N. Ezpeleta L. Perera i Lluna A. Faraone S.V. Ramos-Quiroga J.A. Supèr H. Cañete J. Clinical validation of eye vergence as an objective marker for diagnosis of ADHD in children. J. Atten. Disord. 2019 23 6 599 614 10.1177/1087054717749931 29357741
    [Google Scholar]
  31. Das W. Khanna S. A robust machine learning based framework for the automated detection of ADHD using pupillometric biomarkers and time series analysis. Sci. Rep. 2021 11 1 16370 10.1038/s41598‑021‑95673‑5 34385511
    [Google Scholar]
  32. Rukmani M.R. Seshadri S.P. Thennarasu K. Raju T.R. Sathyaprabha T.N. Heart rate variability in children with attention-deficit/hyperactivity disorder: A pilot study. Ann. Neurosci. 2016 23 2 81 88 10.1159/000443574 27647958
    [Google Scholar]
  33. Negrao B.L. Bipath P. van der Westhuizen D. Viljoen M. Autonomic correlates at rest and during evoked attention in children with attention-deficit/hyperactivity disorder and effects of methylphenidate. Neuropsychobiology 2011 63 2 82 91 10.1159/000317548 21178382
    [Google Scholar]
  34. Börger N. van Der Meere J. Ronner A. Alberts E. Geuze R. Bogte H. Heart rate variability and sustained attention in ADHD children. J. Abnorm. Child Psychol. 1999 27 1 25 33 10.1023/A:1022610306984 10197404
    [Google Scholar]
  35. Wang T.S. Huang W.L. Kuo T.B.J. Lee G.S. Yang C.C.H. Inattentive and hyperactive preschool-age boys have lower sympathetic and higher parasympathetic activity. J. Physiol. Sci. 2013 63 2 87 94 10.1007/s12576‑012‑0238‑3 23076674
    [Google Scholar]
  36. Crowell S.E. Beauchaine T.P. Gatzke-Kopp L. Sylvers P. Mead H. Chipman-Chacon J. Autonomic correlates of attention-deficit/hyperactivity disorder and oppositional defiant disorder in preschool children. J. Abnorm. Psychol. 2006 115 1 174 178 10.1037/0021‑843X.115.1.174 16492108
    [Google Scholar]
  37. Shibagaki M. Furuya T. Baseline respiratory sinus arrhythmia and heart-rate responses during auditory stimulation of children with attention-deficit hyperactivity disorder. Percept Mot Skills 1997 84 967 75 10.2466/pms.1997.84.3.967
    [Google Scholar]
  38. Musser E.D. Backs R.W. Schmitt C.F. Ablow J.C. Measelle J.R. Nigg J.T. Emotion regulation via the autonomic nervous system in children with attention-deficit/hyperactivity disorder (ADHD). J. Abnorm. Child Psychol. 2011 39 6 841 852 10.1007/s10802‑011‑9499‑1 21394506
    [Google Scholar]
  39. Buchhorn R. Conzelmann A. Willaschek C. Störk D. Taurines R. Renner T.J. Heart rate variability and methylphenidate in children with ADHD. Atten. Defic. Hyperact. Disord. 2012 4 2 85 91 10.1007/s12402‑012‑0072‑8 22328340
    [Google Scholar]
  40. Beauchaine T.P. Katkin E.S. Strassberg Z. Snarr J. Disinhibitory psychopathology in male adolescents: Discriminating conduct disorder from attention-deficit/hyperactivity disorder through concurrent assessment of multiple autonomic states. J. Abnorm. Psychol. 2001 110 4 610 624 10.1037/0021‑843X.110.4.610 11727950
    [Google Scholar]
  41. Lackschewitz H. Hüther G. Kröner-Herwig B. Physiological and psychological stress responses in adults with attention-deficit/hyperactivity disorder (ADHD). Psychoneuroendocrinology 2008 33 5 612 624 10.1016/j.psyneuen.2008.01.016 18329819
    [Google Scholar]
  42. Fasmer O.B. Mjeldheim K. Førland W. Hansen A.L. Dilsaver S. Oedegaard K.J. Berle J.Ø. Motor activity in adult patients with attention deficit hyperactivity disorder. Psychiatry Investig. 2015 12 4 474 482 10.4306/pi.2015.12.4.474 26508958
    [Google Scholar]
  43. Amado-Caballero P. Casaseca-de-la-Higuera P. Alberola-Lopez S. Andres-de-Llano J.M. Villalobos J.A.L. Garmendia-Leiza J.R. Alberola-Lopez C. Objective ADHD diagnosis using convolutional neural networks over daily-life activity records. IEEE J. Biomed. Health Inform. 2020 24 9 2690 2700 10.1109/JBHI.2020.2964072 31905156
    [Google Scholar]
  44. Muñoz-Organero M. Powell L. Heller B. Harpin V. Parker J. Automatic extraction and detection of characteristic movement patterns in children with ADHD based on a convolutional neural network (CNN) and acceleration images. Sensors (Basel) 2018 18 11 3924 10.3390/s18113924 30441774
    [Google Scholar]
  45. Faedda G.L. Ohashi K. Hernandez M. McGreenery C.E. Grant M.C. Baroni A. Polcari A. Teicher M.H. Actigraph measures discriminate pediatric bipolar disorder from attention‐deficit/hyperactivity disorder and typically developing controls. J. Child Psychol. Psychiatry 2016 57 6 706 716 10.1111/jcpp.12520 26799153
    [Google Scholar]
  46. O’Mahony N. Florentino-Liano B. Carballo J.J. Baca-García E. Rodríguez A.A. Objective diagnosis of ADHD using IMUs. Med. Eng. Phys. 2014 36 7 922 926 10.1016/j.medengphy.2014.02.023 24657100
    [Google Scholar]
  47. Basic J. Uusimaa J. Salmi J. Wearable motion sensors in the detection of ADHD: A critical review. Commun. Comput. Inf. Sci. 2024 2084 168 185 10.1007/978‑3‑031‑59091‑7_12
    [Google Scholar]
  48. Earnest T. Shephard E. Tye C. McEwen F. Woodhouse E. Liang H. Sheerin F. Bolton P.F. Actigraph-measured movement correlates of attention-deficit/hyperactivity disorder (ADHD) symptoms in young people with tuberous sclerosis complex (TSC) with and without intellectual disability and autism spectrum disorder (ASD). Brain Sci. 2020 10 8 491 10.3390/brainsci10080491 32731531
    [Google Scholar]
  49. Keogh E. Chu S. Hart D. Pazzani M. Segmenting time series: A survey and novel approach. J. Environ. Biol. 2015 36 3 583 589
    [Google Scholar]
  50. Geethanjali P. Mohan Y.K. Sen J. Time domain feature extraction and classification of EEG data for brain computer interface. 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China, 2012, pp. 1136-1139. 10.1109/FSKD.2012.6234336
    [Google Scholar]
  51. Measures of skewness and kurtosis. 2002 Available from: https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
  52. E K. S C. Javaid Hamad. Charupanit Krit. Application of Hjorth parameters in the classification of healthy aging EEG signals. Songklanakarin J. Sci. Technol. 2021 43 6 1807 1814
    [Google Scholar]
  53. Yan R. Liu Y. Gao R.X. Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines. Mech. Syst. Signal Process. 2012 29 474 484 10.1016/j.ymssp.2011.11.022
    [Google Scholar]
  54. Misra H. Ikbal S. Bourlard H. Hermansky H. Spectral entropy based feature for robust ASR. IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QC, Canada, 2004, pp. I-193. 10.1109/ICASSP.2004.1325955
    [Google Scholar]
  55. Jelinek H.F. Donnan L. Khandoker A.H. Singular value decomposition entropy as a measure of ankle dynamics efficacy in a Y-balance test following supportive lower limb taping. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 2439-2442. 10.1109/EMBC.2019.8856655
    [Google Scholar]
  56. Zhang H. He S.S. Analysis and comparison of permutation entropy, approximate entropy and sample entropy. International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, 2018, pp. 209-212. 10.1109/IS3C.2018.00060
    [Google Scholar]
  57. Geurts P. Ernst D. Wehenkel L. Extremely randomized trees. Mach. Learn. 2006 63 1 3 42 10.1007/s10994‑006‑6226‑1
    [Google Scholar]
  58. Hall M. A. Correlation-based feature for machine learning. 1999
    [Google Scholar]
  59. Yoav F. Schapire R.E. A short introduction to boosting. J Human Soc 1999 14 5 771 780
    [Google Scholar]
  60. Ong Y. J. Zhou Y. Baracaldo N. Ludwig H. Adaptive histogram-based gradient boosted trees for federated learning. arXiv 2020
    [Google Scholar]
  61. Machado M.R. Karray S. De Sousa I.T. LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry. 14th International Conference on Computer Science & Education (ICCSE), Toronto, ON, Canada, 2019, pp. 1111-1116. 10.1109/ICCSE.2019.8845529
    [Google Scholar]
  62. Chen T. Guestrin C. XGBoost: A scalable tree boosting system. arXiv 2016 10.1145/2939672.2939785
    [Google Scholar]
  63. He Z. Lin D. Lau T. Wu M. Gradient boosting machine. Survey (Lond.) 2019 1 9
    [Google Scholar]
  64. Wang R. AdaBoost for feature selection, classification and its relation with SVM, A review. Phys. Procedia 2012 25 800 807 10.1016/j.phpro.2012.03.160
    [Google Scholar]
/content/journals/rascs/10.2174/0126662558344316241022095150
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Smart Health Monitoring Approach to Diagnose Attention-Deficit Hyperactivity Disorderbased on Real-Time Activity and Heart Rate Variability using Boosting Models
Bentham Science Publishers ; https://doi.org/10.2174/0126662558344316241022095150
/content/journals/rascs/10.2174/0126662558344316241022095150
/content/journals/rascs/10.2174/0126662558344316241022095150
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  • Article Type:     Research Article
Keywords: Histogram-based Gradient Boosting ; Machine Learning ; ADHD ; Smart Healthcare ; Heart Rate Variability ; Activity

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