2019
DOI: 10.1007/978-3-030-31019-6_32
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Using Machine Learning and Accelerometry Data for Differential Diagnosis of Parkinson’s Disease and Essential Tremor

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Cited by 17 publications
(10 citation statements)
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“…The results obtained in this work supported the above, the most significant feature for the differentiation of patients with PD and ET seems to be the novel HIR feature, as it was implemented in 12 of the 18 best models depicted in Figure 4. Also, as already observed in previous works [4], [19], RE and RPC features provide essential information. The RPC feature also contains relevant information for the differentiation of TP and HS in both analyzed frequency ranges.…”
Section: Discussionsupporting
confidence: 75%
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“…The results obtained in this work supported the above, the most significant feature for the differentiation of patients with PD and ET seems to be the novel HIR feature, as it was implemented in 12 of the 18 best models depicted in Figure 4. Also, as already observed in previous works [4], [19], RE and RPC features provide essential information. The RPC feature also contains relevant information for the differentiation of TP and HS in both analyzed frequency ranges.…”
Section: Discussionsupporting
confidence: 75%
“…The results obtained in this work show that the characterization and differentiation between tremor in PD and ET are possible with a mobile phone's built-in gyroscope. The accuracy of the tremor differentiation using this sensor is comparable to the performance obtained using a mobile phone's built-in accelerometer [4], [19]. Although there is a clear difference between the number of TP (39 in total) and HS (12 in total), the accuracy of the models differentiating the two conditions is high.…”
Section: Discussionmentioning
confidence: 62%
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“…Of the patients with undecided diagnoses, all PD cases (two) and two of four ET cases were correctly classified. Duque et al, also performed machine learning classification using the linear acceleration of tremor recorded by the smartphone’s built-in accelerometer, and showed performances ranging from 90.0% to 100.0% sensitivity, and 80% to 100% specificity [ 60 ]. Thus, the smartphone, a familiar device, is expected to be utilized.…”
Section: Resultsmentioning
confidence: 99%
“…In addition, a study used a NN to successfully discern PD from ET using surface electromyography data [26]. Other machine learning algorithms such as support vector machine (SVM) and k-nearest neighbor (kNN) were used to differentiate between PD and ET based on IMU sensors, but they mainly investigated upper body tremors [27][28][29][30]. To our knowledge, no study has utilized machine learning techniques to differentiate between PD and ET based on data collected from gait and balance characteristics from wearable IMU sensors.…”
Section: Introductionmentioning
confidence: 99%