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2020 | OriginalPaper | Buchkapitel

Wearable-Based Parkinson’s Disease Severity Monitoring Using Deep Learning

verfasst von: Jann Goschenhofer, Franz M. J. Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, Janek Thomas

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

One major challenge in the medication of Parkinson’s disease is that the severity of the disease, reflected in the patients’ motor state, cannot be measured using accessible biomarkers. Therefore, we develop and examine a variety of statistical models to detect the motor state of such patients based on sensor data from a wearable device. We find that deep learning models consistently outperform a classical machine learning model applied on hand-crafted features in this time series classification task. Furthermore, our results suggest that treating this problem as a regression instead of an ordinal regression or a classification task is most appropriate. For consistent model evaluation and training, we adopt the leave-one-subject-out validation scheme to the training of deep learning models. We also employ a class-weighting scheme to successfully mitigate the problem of high multi-class imbalances in this domain. In addition, we propose a customized performance measure that reflects the requirements of the involved medical staff on the model. To solve the problem of limited availability of high quality training data, we propose a transfer learning technique which helps to improve model performance substantially. Our results suggest that deep learning techniques offer a high potential to autonomously detect motor states of patients with Parkinson’s disease.
Fußnoten
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Feedback was collected by comparing multiple cost matrices as shown in Fig. 3.
 
Literatur
1.
Zurück zum Zitat Ahlrichs, C., Lawo, M.: Parkinson’s disease motor symptoms in machine learning: a review. Health Informatics 2, (2013) Ahlrichs, C., Lawo, M.: Parkinson’s disease motor symptoms in machine learning: a review. Health Informatics 2, (2013)
5.
Zurück zum Zitat Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-CNN for age estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (2017) Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-CNN for age estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (2017)
6.
Zurück zum Zitat Christ, M., Kempa-Liehr, A.W., Feindt, M.: Distributed and parallel time series feature extraction for industrial big data applications. arXiv preprint arXiv:​1610.​07717 (2016) Christ, M., Kempa-Liehr, A.W., Feindt, M.: Distributed and parallel time series feature extraction for industrial big data applications. arXiv preprint arXiv:​1610.​07717 (2016)
7.
Zurück zum Zitat Curtze, C., Nutt, J.G., Carlson-Kuhta, P., Mancini, M., Horak, F.B.: Levodopa is a double edged sword for balance and gait in people with parkinson’s disease. Mov. Disord. 30(10), 1361–1370 (2015) CrossRef Curtze, C., Nutt, J.G., Carlson-Kuhta, P., Mancini, M., Horak, F.B.: Levodopa is a double edged sword for balance and gait in people with parkinson’s disease. Mov. Disord. 30(10), 1361–1370 (2015) CrossRef
8.
Zurück zum Zitat Elliott, G., Timmermann, A., Komunjer, I.: Estimation and testing of forecast rationality under flexible loss. Rev. Econ. Stud. 72(4), 1107–1125 (2005) MathSciNetMATHCrossRef Elliott, G., Timmermann, A., Komunjer, I.: Estimation and testing of forecast rationality under flexible loss. Rev. Econ. Stud. 72(4), 1107–1125 (2005) MathSciNetMATHCrossRef
9.
Zurück zum Zitat Eskofier, B.M., et al.: Recent machine learning advancements in sensor-based mobility analysis: deep learning for parkinson’s disease assessment. In: Engineering in Medicine and Biology Society, pp. 655–658 (2016) Eskofier, B.M., et al.: Recent machine learning advancements in sensor-based mobility analysis: deep learning for parkinson’s disease assessment. In: Engineering in Medicine and Biology Society, pp. 655–658 (2016)
10.
Zurück zum Zitat Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. arXiv preprint arXiv:​1809.​04356 (2018) Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. arXiv preprint arXiv:​1809.​04356 (2018)
11.
Zurück zum Zitat Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. arXiv preprint arXiv:​1811.​01533 (2018) Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. arXiv preprint arXiv:​1811.​01533 (2018)
12.
Zurück zum Zitat Fisher, J.M., Hammerla, N.Y., Ploetz, T., Andras, P., Rochester, L., Walker, R.W.: Unsupervised home monitoring of parkinson’s disease motor symptoms using body-worn accelerometers. Parkinsonism Relat. Disord. 33, 44–50 (2016) CrossRef Fisher, J.M., Hammerla, N.Y., Ploetz, T., Andras, P., Rochester, L., Walker, R.W.: Unsupervised home monitoring of parkinson’s disease motor symptoms using body-worn accelerometers. Parkinsonism Relat. Disord. 33, 44–50 (2016) CrossRef
14.
Zurück zum Zitat Ghika, J., Wiegner, A.W., Fang, J.J., Davies, L., Young, R.R., Growdon, J.H.: Portable system for quantifying motor abnormalities in parkinson’s disease. IEEE Trans. Biomed. Eng. 40(3), 276–283 (1993) CrossRef Ghika, J., Wiegner, A.W., Fang, J.J., Davies, L., Young, R.R., Growdon, J.H.: Portable system for quantifying motor abnormalities in parkinson’s disease. IEEE Trans. Biomed. Eng. 40(3), 276–283 (1993) CrossRef
15.
Zurück zum Zitat Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks, pp. 249–256 (2010) Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks, pp. 249–256 (2010)
16.
Zurück zum Zitat Goetz, C.G., et al.: Movement disorder society-sponsored revision of the unified parkinson’s disease rating scale (mds-updrs): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008) CrossRef Goetz, C.G., et al.: Movement disorder society-sponsored revision of the unified parkinson’s disease rating scale (mds-updrs): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008) CrossRef
18.
Zurück zum Zitat Gutierrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., Fernandez-Navarro, F., Hervas-Martinez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127–146 (2016) CrossRef Gutierrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., Fernandez-Navarro, F., Hervas-Martinez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127–146 (2016) CrossRef
19.
Zurück zum Zitat Hammerla, N.Y., Halloran, S., Ploetz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (2016) Hammerla, N.Y., Halloran, S., Ploetz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (2016)
20.
Zurück zum Zitat Hammerla, N.Y., Fisher, J., Andras, P., Rochester, L., Walker, R., Plötz, T.: PD disease state assessment in naturalistic environments using deep learning, pp. 1742–1748 (2015) Hammerla, N.Y., Fisher, J., Andras, P., Rochester, L., Walker, R., Plötz, T.: PD disease state assessment in naturalistic environments using deep learning, pp. 1742–1748 (2015)
21.
Zurück zum Zitat He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 9, 1263–1284 (2009) He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 9, 1263–1284 (2009)
22.
Zurück zum Zitat He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
23.
Zurück zum Zitat Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: International Conference on Artificial Neural Networks (1999) Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: International Conference on Artificial Neural Networks (1999)
24.
Zurück zum Zitat Hssayeni, M.D., Burack, M.A., Ghoraani, B.: Automatic assessment of medication states of patients with parkinson’s disease using wearable sensors, pp. 6082–6085 (2016) Hssayeni, M.D., Burack, M.A., Ghoraani, B.: Automatic assessment of medication states of patients with parkinson’s disease using wearable sensors, pp. 6082–6085 (2016)
25.
Zurück zum Zitat Hssayeni, M.D., Burack, M.A., Jimenez-Shahed, J., Ghoraani, B., et al.: Wearable-based medication state detection in individuals with parkinson’s disease. arXiv preprint arXiv:​1809.​06973 (2018) Hssayeni, M.D., Burack, M.A., Jimenez-Shahed, J., Ghoraani, B., et al.: Wearable-based medication state detection in individuals with parkinson’s disease. arXiv preprint arXiv:​1809.​06973 (2018)
26.
Zurück zum Zitat Keijsers, N.L., Horstink, M.W., Gielen, S.C.: Automatic assessment of levodopa-induced dyskinesias in daily life by neural networks. Mov. Disord. 18, 70–80 (2003) CrossRef Keijsers, N.L., Horstink, M.W., Gielen, S.C.: Automatic assessment of levodopa-induced dyskinesias in daily life by neural networks. Mov. Disord. 18, 70–80 (2003) CrossRef
27.
Zurück zum Zitat Keijsers, N.L., Horstink, M.W., Gielen, S.C.: Ambulatory motor assessment in parkinson’s disease. Mov. Disord. 21, 34–44 (2006) CrossRef Keijsers, N.L., Horstink, M.W., Gielen, S.C.: Ambulatory motor assessment in parkinson’s disease. Mov. Disord. 21, 34–44 (2006) CrossRef
29.
Zurück zum Zitat Lane, R.D., Glazer, W.M., Hansen, T.E., Berman, W.H., Kramer, S.I.: Assessment of tardive dyskinesia using the abnormal involuntary movement scale. J. Nerv. Ment. Dis. 173, 353–357 (1985) CrossRef Lane, R.D., Glazer, W.M., Hansen, T.E., Berman, W.H., Kramer, S.I.: Assessment of tardive dyskinesia using the abnormal involuntary movement scale. J. Nerv. Ment. Dis. 173, 353–357 (1985) CrossRef
30.
Zurück zum Zitat Lonini, L., et al.: Wearable sensors for parkinson’s disease: which data are worth collecting for training symptom detection models. NPJ Digit. Med. 1, 1–8 (2018) CrossRef Lonini, L., et al.: Wearable sensors for parkinson’s disease: which data are worth collecting for training symptom detection models. NPJ Digit. Med. 1, 1–8 (2018) CrossRef
31.
Zurück zum Zitat Marsden, C.D., Parkes, J.: “On-off" effects in patients with parkinson’s disease on chronic levodopa therapy. Lancet 307(7954), 292–296 (1976) Marsden, C.D., Parkes, J.: “On-off" effects in patients with parkinson’s disease on chronic levodopa therapy. Lancet 307(7954), 292–296 (1976)
33.
Zurück zum Zitat Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output cnn for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920–4928 (2016) Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output cnn for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920–4928 (2016)
34.
Zurück zum Zitat Paszke, A., et al.: Automatic differentiation in pytorch (2017) Paszke, A., et al.: Automatic differentiation in pytorch (2017)
35.
Zurück zum Zitat Postuma, R.B., et al.: MDS clinical diagnostic criteria for parkinson’s disease. Mov. Disord. 30(12), 1591–1601 (2015) Postuma, R.B., et al.: MDS clinical diagnostic criteria for parkinson’s disease. Mov. Disord. 30(12), 1591–1601 (2015)
36.
Zurück zum Zitat Pringsheim, T., Jette, N., Frolkis, A., Steeves, T.D.: The prevalence of parkinson’s disease: a systematic review and meta-analysis. Mov. Disord. 29(13), 1583–1590 (2014) CrossRef Pringsheim, T., Jette, N., Frolkis, A., Steeves, T.D.: The prevalence of parkinson’s disease: a systematic review and meta-analysis. Mov. Disord. 29(13), 1583–1590 (2014) CrossRef
37.
Zurück zum Zitat Saeb, S., Lonini, L., Jayaraman, A., Mohr, D.C., Kording, K.P.: The need to approximate the use-case in clinical machine learning. Gigascience 6(5), 1–9 (2017) CrossRef Saeb, S., Lonini, L., Jayaraman, A., Mohr, D.C., Kording, K.P.: The need to approximate the use-case in clinical machine learning. Gigascience 6(5), 1–9 (2017) CrossRef
38.
Zurück zum Zitat Sama, A., et al.: Dyskinesia and motor state detection in parkinson’s disease patients with a single movement sensor. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1194–1197 (2012) Sama, A., et al.: Dyskinesia and motor state detection in parkinson’s disease patients with a single movement sensor. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1194–1197 (2012)
39.
Zurück zum Zitat Szegedy, C., et al.: Going deeper with convolutions. In: Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015) Szegedy, C., et al.: Going deeper with convolutions. In: Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
40.
Zurück zum Zitat Tsipouras, M.G., Tzallas, A.T., Fotiadis, D.I., Konitsiotis, S.: On automated assessment of levodopa-induced dyskinesia in parkinson’s disease. In: Engineering in Medicine and Biology Society, pp. 2679–2682 (2011) Tsipouras, M.G., Tzallas, A.T., Fotiadis, D.I., Konitsiotis, S.: On automated assessment of levodopa-induced dyskinesia in parkinson’s disease. In: Engineering in Medicine and Biology Society, pp. 2679–2682 (2011)
41.
Zurück zum Zitat Um, T.T., et al.: Parkinson’s disease assessment from a wrist-worn wearable sensor in free-living conditions: deep ensemble learning and visualization. arXiv preprint arXiv:​1808.​02870 (2018) Um, T.T., et al.: Parkinson’s disease assessment from a wrist-worn wearable sensor in free-living conditions: deep ensemble learning and visualization. arXiv preprint arXiv:​1808.​02870 (2018)
42.
Zurück zum Zitat Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: International Joint Conference on Neural Networks, pp. 1578–1585 (2017) Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: International Joint Conference on Neural Networks, pp. 1578–1585 (2017)
43.
Zurück zum Zitat Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014) Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)
44.
Zurück zum Zitat Zeng, M., et al.: Convolutional neural networks for human activity recognition using mobile sensors. In: 2014 6th International Conference on Mobile Computing, Applications and Services (MobiCASE), pp. 197–205 (2014) Zeng, M., et al.: Convolutional neural networks for human activity recognition using mobile sensors. In: 2014 6th International Conference on Mobile Computing, Applications and Services (MobiCASE), pp. 197–205 (2014)
Metadaten
Titel
Wearable-Based Parkinson’s Disease Severity Monitoring Using Deep Learning
verfasst von
Jann Goschenhofer
Franz M. J. Pfister
Kamer Ali Yuksel
Bernd Bischl
Urban Fietzek
Janek Thomas
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-46133-1_24

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