ORIGINAL ARTICLES
Year : 2023 | Volume
: 6 | Issue : 1 | Page : 17--25
Automated analysis of pen-on-paper spirals for tremor detection, quantification, and differentiation
Roopa Rajan1, Reghu Anandapadmanabhan1, Sharmila Nageswaran2, Vineeth Radhakrishnan3, Arti Saini1, Syam Krishnan3, Anu Gupta1, Venugopalan Y Vishnu1, Awadh K Pandit1, Rajesh Kumar Singh1, Divya M Radhakrishnan1, Mamta Bhushan Singh1, Rohit Bhatia1, Achal Srivastava1, Asha Kishore4, MV Padma Srivastava1
1 Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
2 Department of Sensor and Biomedical Technology, VIT University, Vellore, Tamil Nadu, India
3 Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Kerala, India
4 Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Kerala, India; Department of Neurology, Centre of Excellence in Neurosciences, Aster Medicity, Kochi, Kerala, India
Correspondence Address:
Roopa Rajan
Department of Neurology, All India Institute of Medical Sciences, New Delhi – 110029
India
Objective: To develop an automated algorithm to detect, quantify, and differentiate between tremor using pen-on-paper spirals. Methods: Patients with essential tremor (n = 25), dystonic tremor (n = 25), Parkinson’s disease (n = 25), and healthy volunteers (HV, n = 25) drew free-hand spirals. The algorithm derived the mean deviation (MD) and tremor variability from scanned images. MD and tremor variability were compared with 1) the Bain and Findley scale, 2) the Fahn–Tolosa–Marin tremor rating scale (FTM–TRS), and 3) the peak power and total power of the accelerometer spectra. Inter and intra loop widths were computed to differentiate between the tremor. Results: MD was higher in the tremor group (48.9 ± 26.3) than in HV (26.4 ± 5.3; p < 0.001). The cut-off value of 30.3 had 80.9% sensitivity and 76.0% specificity for the detection of the tremor [area under the curve: 0.83; 95% confidence index (CI): 0.75, 0.91, p < 0.001]. MD correlated with the Bain and Findley ratings (rho = 0.491, p = 0 < 0.001), FTM–TRS part B (rho = 0.260, p = 0.032) and accelerometric measures of postural tremor (total power, rho = 0.366, p < 0.001; peak power, rho = 0.402, p < 0.001). Minimum Detectable Change was 19.9%. Inter loop width distinguished Parkinson’s disease spirals from dystonic tremor (p < 0.001, 95% CI: 54.6, 211.1), essential tremor (p = 0.003, 95% CI: 28.5, 184.9), or HV (p = 0.036, 95% CI: -160.4, -3.9). Conclusion: The automated analysis of pen-on-paper spirals generated robust variables to quantify the tremor and putative variables to distinguish them from each other. Significance: This technique maybe useful for epidemiological surveys and follow-up studies on tremor.
How to cite this article:
Rajan R, Anandapadmanabhan R, Nageswaran S, Radhakrishnan V, Saini A, Krishnan S, Gupta A, Vishnu VY, Pandit AK, Kumar Singh R, Radhakrishnan DM, Bhushan Singh M, Bhatia R, Srivastava A, Kishore A, Padma Srivastava M V. Automated analysis of pen-on-paper spirals for tremor detection, quantification, and differentiation.Ann Mov Disord 2023;6:17-25
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How to cite this URL:
Rajan R, Anandapadmanabhan R, Nageswaran S, Radhakrishnan V, Saini A, Krishnan S, Gupta A, Vishnu VY, Pandit AK, Kumar Singh R, Radhakrishnan DM, Bhushan Singh M, Bhatia R, Srivastava A, Kishore A, Padma Srivastava M V. Automated analysis of pen-on-paper spirals for tremor detection, quantification, and differentiation. Ann Mov Disord [serial online] 2023 [cited 2023 May 28 ];6:17-25
Available from: https://www.aomd.in/article.asp?issn=2590-3446;year=2023;volume=6;issue=1;spage=17;epage=25;aulast=Rajan;type=0 |
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