Differentiation of Parkinson’s disease tremor and essential tremor based on a novel hand posture

Mahendran, Sujitha; Bichsel, Oliver; Gassert, Roger; Baumann, Christian; Imbach, Lukas L.; Waldvogel, Daniel

doi:10.1016/j.prdoa.2022.100146

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…A common study design implemented in recent studies includes the use of an accelerometer incorporated in a smartphone or as a wearable (e.g. smartwatch) [45], [46], [47]. Such studies either utilize the threedimensional tremor information as three different recordings [45], or use it to obtain a single (total acceleration magnitude) signal [46], [47].…”

Section: Discussionmentioning

confidence: 99%

“…smartwatch) [45], [46], [47]. Such studies either utilize the threedimensional tremor information as three different recordings [45], or use it to obtain a single (total acceleration magnitude) signal [46], [47]. We believe that in the case that other tremor dimensions were available, this would potentially benefit our study, as this additional information inherent in other tremor axes could be easily exploited in a similar manner to [45].…”

Section: Discussionmentioning

confidence: 99%

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Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-/Post-DBS and Medication Treatment

Lamprou¹,

Ziogas²,

Hadjileontiadis³

2022

Preprint

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Objective: Rest tremor is one of the most common symptoms of Parkinson’s Disease (PD), with diagnosis and severity estimation often being hindered by the subjectivity of clinical methods and limitations of existing screening procedures. Hence, development of methods that can accurately describe properties of PD rest tremor, while accounting for the presence of possible ongoing treatments, such as Deep Brain Stimulation (DBS) and medication, are quite important. Methods: A Higher Order Spectrum (HOS)-based analysis for characterizing and classifying tremor severity and treatment effectiveness, taking into consideration its nonlinear characteristics, is proposed here. Bispectrum- and Bicoherence-based features are extracted from velocity signals recorded from 16 PD patients at their index finger. Two different scenarios are implemented and tested for feature characterization under various conditions of treatment. Moreover, a classification scheme was constructed to evaluate the ability of HOS-based features in accurately predicting the rest tremor level, i.e., Low Amplitude/High Amplitude (LAT/HAT), and the presence of treatment (medication/DBS On-Off). To avoid over-fitting, a leave-one-subject-out cross-validation procedure was adopted. Results: The proposed bispectral analysis resulted in area under the Receiver Operating Characteristics curve (AUC) score of 0.96 (95% Confidence Interval (CI): 0.89-1) with 0.91/0.88 Sensitivity/Specificity, respectively, for the On-Off classification for Medication treatment. For the DBS treatment, the proposed analysis resulted in an AUC score of 0.72 (95% CI: 0.57-0.87), with 0.64/0.65 Sensitivity/Specificity, respectively. For LAT/HAT prediction, the best performing models yield scores of 1/0.93 for AUC/Accuracy metrics, with 1/0.86 Sensitivity/Specificity, respectively. Conclusions: When compared to the existing methods, the proposed methodology outperforms them regarding the prediction of LAT/HAT and treatment effectiveness. Additionally, our HOS-based methodology enables the establishment of new rest tremor classes, based on its nonlinearity and allows for new insights about the dynamic nature of the resting tremor production system. Significance: We propose a method that can effectively recognize rest tremor severity and assess the influence of medication and DBS. This study introduces, for the first time, nonlinearity-based classes for rest tremor and provides an accurate representation of tremor dynamics through HOS.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-/Post-DBS and Medication Treatment

Lamprou¹,

Ziogas²,

Hadjileontiadis³

2022

Preprint

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“…Parkinsonian tremor usually presents while the patient is at rest with frequencies in the 4-7 Hz band [30][31][32]. In order to isolate the tremor signal from the voluntary movement signal, both the accelerometer and gyroscope signals were high-pass-fltered (10th-order Butterworth flter, cutof frequency of 3.5 Hz).…”

Section: Data Preprocessingmentioning

confidence: 99%

Continuous and Unconstrained Tremor Monitoring in Parkinson’s Disease Using Supervised Machine Learning and Wearable Sensors

Rodriguez,

Krauss,

Kluckert

et al. 2024

Parkinson's Disease

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Background. Accurately assessing the severity and frequency of fluctuating motor symptoms is important at all stages of Parkinson’s disease management. Contrarily to time-consuming clinical testing or patient self-reporting with uncertain reliability, recordings with wearable sensors show promise as a tool for continuously and objectively assessing PD symptoms. While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge. Methods. We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. We evaluated the algorithm on a 67-hour database comprising sensor data and clinical tremor scores for 24 Parkinson patients at four extremities for periods of about 3 hours. A random 25% subset of the labelled samples was used as test data, the remainder as training data. Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. Due to the inherent imbalance in tremor scores, we applied dataset rebalancing techniques. Results. Our classifier demonstrated robust performance in detecting tremor events with a sensitivity of 0.90 on the test-portion of the resampled dataset. The overall classification accuracy was high at 0.88. Conclusion. We implemented an accurate classifier for tremor monitoring in free-living environments that can be trained even with modestly sized and imbalanced datasets. This advancement offers significant clinical value in continuously monitoring Parkinson’s disease symptoms beyond the hospital setting, paving the way for personalized management of PD, timely therapeutic adjustments, and improved patient quality of life.

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Distinguishing essential tremor from Parkinson’s disease: clinical and experimental tools

Angelini,

Paparella,

Bologna

2024

Expert Review of Neurotherapeutics

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Differentiation of Parkinson’s disease tremor and essential tremor based on a novel hand posture

Cited by 3 publications

References 23 publications

Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-/Post-DBS and Medication Treatment

Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-/Post-DBS and Medication Treatment

Continuous and Unconstrained Tremor Monitoring in Parkinson’s Disease Using Supervised Machine Learning and Wearable Sensors

Distinguishing essential tremor from Parkinson’s disease: clinical and experimental tools

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