An automatic non-invasive method for Parkinson's disease classification

Joshi, Deepak; Khajuria, Aayushi; Joshi, Pankaj

doi:10.1016/j.cmpb.2017.04.007

Cited by 100 publications

(32 citation statements)

References 33 publications

(28 reference statements)

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“…Machine learning techniques together with wearable devices (e.g., accelerometers) have been used in many PD studies to recognize activities of daily living [22], identify diseases severity level by predicting UPDRS score [23], or predict FOG events, tremor, dyskinesia or bradykinesia [9], [24]- [26]. However, there are very few studies that have used wearable devices to discriminate between gait patterns of PD and healthy individuals [24], [27]- [30]. In the study by Patel et al a system was used to analyze lower and upper extremities and no distinction was found between subjects from PD and control groups in this study [24].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Rastegari¹,

Azizian²,

Ali³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Parkinson's Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson's disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson's Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers' performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson's disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD.

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Section: Introductionmentioning

confidence: 99%

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Rastegari¹,

Azizian²,

Ali³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Other approaches to classify PD patients' gait employed data from considerably different sources for different purposes. While Khorasani and Daliri (2014) and Joshi et al (2017) analyzed data of force-sensitive insoles provided by Hausdorff et al (1998), Wahid et al (2015) used whole body gait data and force platform outcomes to optimize classification results. Here, we report the maximum contrast between healthy subjects and PD patients with DBS switched on or off.…”

Section: Discussionmentioning

confidence: 99%

“…There is already evidence that jerk is abnormal in PD (Teulings et al, 1997;Hogan and Sternad, 2009). Other methods of data reduction by feature extraction involve signal processing methods, e.g., wavelet analysis (Joshi et al, 2017), stochastic models, like the Hidden Markov Model (Joshi et al, 2017), or machine-learning algorithms (Wouda et al, 2016), i.e., using Random Forests (Wahid et al, 2015;Kuhner et al, 2016Kuhner et al, , 2017.…”

Section: Introductionmentioning

confidence: 99%

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study

et al. 2020

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Background: Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessments such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, PD includes motor abnormalities beyond the symptoms and signs as measured by UPDRS, such as the lack of anticipatory adjustments or compromised movement smoothness, which are difficult to assess clinically. Moreover, PD may entail motor abnormalities not yet known. All these abnormalities are quantifiable via motion capture and may serve as biomarkers to diagnose and monitor PD.Objective: In this pilot study, we attempted to identify motion features revealing maximum contrast between healthy subjects and PD patients with deep brain stimulation (DBS) of the nucleus subthalamicus (STN) switched off and on as the first step to develop biomarkers for detecting and monitoring PD patients' motor symptoms. Methods:We performed 3D gait analysis in 7 out of 26 PD patients with DBS switched off and on, and in 25 healthy control subjects. We computed feature values for each stride, related to 22 body segments, four time derivatives, left-right mean vs. difference, and mean vs. variance across stride time. We then ranked the feature values according to their distinguishing power between PD patients and healthy subjects.Results: The foot and lower leg segments proved better in classifying motor anomalies than any other segment. Higher degrees of time derivatives were superior to lower degrees (jerk > acceleration > velocity > displacement). The averaged movements across left and right demonstrated greater distinguishing power than left-right asymmetries. The variability of motion was superior to motion's absolute values.Kuhner et al. Motion Capture in Parkinson's DiseaseConclusions: This small pilot study identified the variability of a smoothness measure, i.e., jerk of the foot, as the optimal signal to separate healthy subjects' from PD patients' gait. This biomarker is invisible to clinicians' naked eye and is therefore not included in current motor assessments such as the UPDRS. We therefore recommend that more extensive investigations be conducted to identify the most powerful biomarkers to characterize motor abnormalities in PD. Future studies may challenge the composition of traditional assessments such as the UPDRS.

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“…Parkinson, beyindeki sinir hücrelerinin kişinin yürüyüş düzenini etkileyen ve "dopamin" olarak bilinen nörotransmitter maddeyi yeterli miktarda üretememesiyle karakterize edilen bir nörodejeneratif hastalıktır [2]. Parkinson gibi nörodejeneratif hastalıkların tanısında fonksiyonel nöro-görüntüleme kullanımı oldukça popüler olmasına rağmen yine de hızlı, kolay ve düşük maliyetli ölçümler sunan alternatif tanı yöntemlerine ihtiyaç duyulmaktadır [2]. Son bulgular serebral korteks, bazal ganglionlar ve omurilik içine inen yolakların insan hareketinde önemli rol oynadığını göstermiştir.…”

Section: Introductionunclassified

Çift Yoğunluklu 1-D Dalgacık Dönüşümü Kullanılarak Parkinson Hastalığının Yaş Faktörüne Göre Tespit Edilmesi

Yücelbaş

2019

European Journal of Science and Technology

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Öz Parkinson, hareketi etkileyen nörolojik bir sinir sistemi rahatsızlığıdır. Parkinson gibi sinir hücreleriyle yakından ilgisi olan bir hastalığın yürüme verileriyle tespit edilebilmesi muhtemeldir. Günümüzde birçok hastalığa ait belirtiler çok erken yaşlarda ortaya çıkmaya başlamıştır. Bu nedenle bütün hastalıklar için yaş faktörüne göre gerçekleştirilen analiz çalışmaları önem kazanmıştır. Bu sebeple bu çalışmada Çift Yoğunluklu 1-D Dalgacık Dönüşümü (ÇY1DDD) kullanılarak deneklerden elde edilen verilerin yaş faktörüne göre analiz edilmesi ve Parkinson hastalığının (PH) yüksek doğrulukla tespit edilmesi amaçlanmıştır. Kullanılan veri seti genç, yetişkin ve yetişkin hasta olmak üzere 15 denekten alınan yürüyüş verilerinden oluşmaktadır. Kaydedilen veriler üzerinde öncelikle ÇY1DDD yöntemi üç seviye olarak uygulanmış ve yaklaşım (YK) ile detay katsayıları (DK) elde edilmiştir. Daha sonra yaş faktörüne göre elde edilen son seviye YK verilerinden 10 adet özellik çıkarılmıştır. Çıkarılan bu özellikler sağlıklı genç-yetişkin hasta ve sağlıklı yetişkin-yetişkin hasta olmak üzere ikili sınıflar şeklinde 4 farklı karar mekanizmasına verilmiştir. Elde edilen sonuçlar birçok istatistiksel metrikle yorumlanmıştır. Uzman sistemler sayesinde anlamlı sonuçlara ulaşılmış ve genç-sağlıklı verilerinin yetişkin-hasta verilerinden daha düşük hata değerleri ve %100 sınıflama doğruluğu (SD) oranı ile ayrılabildiği görülmüştür. Karar mekanizmaları arasında ise sıfıra en yakın hata değerleriyle yapay sinir ağları (YSA), her iki sınıf için de başarısını kanıtlamıştır. Literatürde her ne kadar bu alanda yapılan çalışmalar bulunsa bile, yaş faktörünün PH üzerindeki etkisinin ayrıntılı analizine yeterli derecede yer verilmemesi bu çalışmanın önemini arttırmıştır. Bunun yanında kullanılan etkin özelliklerden bazılarının PH'nin uzman sistemler tarafından otomatik tespit edildiği çalışma alanında daha önce kullanılmamış olması, çalışmanın literatüre katkısını önemli ölçüde desteklemektedir.

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An automatic non-invasive method for Parkinson's disease classification

Cited by 100 publications

References 33 publications

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study

Çift Yoğunluklu 1-D Dalgacık Dönüşümü Kullanılarak Parkinson Hastalığının Yaş Faktörüne Göre Tespit Edilmesi

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