The Experts below are selected from a list of 7419 Experts worldwide ranked by ideXlab platform

Jane Alty - One of the best experts on this subject based on the ideXlab platform.

  • supervised classification of Bradykinesia in parkinson s disease from smartphone videos
    Artificial Intelligence in Medicine, 2020
    Co-Authors: Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, Rami Qahwaji, Christophe D Graham, David Wong
    Abstract:

    Abstract Background Slowness of movement, known as Bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess Bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of Bradykinesia. Methods We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of Bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naive Bayes, Logistic Regression, Support Vector Machine) to predict no/slight Bradykinesia (UPDRS=0-1) or mild/moderate/severe Bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis. Results A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe Bradykinesia with an estimated test accuracy of 0.8. A Naive Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion The method described here presents an approach for predicting Bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of Bradykinesia prediction is comparable to that recorded by blinded human experts.

  • the discerning eye of computer vision can it measure parkinson s finger tap Bradykinesia
    Journal of the Neurological Sciences, 2020
    Co-Authors: Stefa Williams, Zhibi Zhao, Samuel Relto, Jane Alty, David Wong, Hui Fang, Awais Hafeez
    Abstract:

    Abstract Objective The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, Bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's Bradykinesia from smartphone videos of finger tapping. Methods Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Results DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of Bradykinesia (Spearman coefficients): −0.74 speed, 0.66 amplitude, −0.65 rhythm for MBRS; −0.56 speed, 0.61 amplitude, −0.50 rhythm for MDS-UPDRS; −0.69 combined for MDS-UPDRS. All p  Conclusion New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's Bradykinesia from smartphone videos of finger tapping. Objective ‘contactless’ measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely ‘contactless’. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements.

  • time series clustering to examine presence of decrement in parkinson s finger tapping Bradykinesia
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2020
    Co-Authors: Zhibi Zhao, Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, Alexande J Casso, David Wong
    Abstract:

    Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no Bradykinesia and participants with severe Bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance— Our work visually presents the archetypal types of Bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no Bradykinesia, and the other showing linear decrement over time.

  • Time series clustering to examine presence of decrement in Parkinson’s finger-tapping Bradykinesia
    2020
    Co-Authors: Zhibi Zhao, Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, David Wong
    Abstract:

    — Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely. We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smartphone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster. Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no Bradykinesia and participants with severe Bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time. Clinical relevance— Our work visually presents the archetypal types of Bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no Bradykinesia, and the other showing linear decrement over time

  • objective assessment of Bradykinesia in parkinson s disease using evolutionary algorithms clinical validation
    Translational neurodegeneration, 2018
    Co-Authors: Chao Gao, Jane Alty, Stephe L Smith, Michael A Lones, Stua Jamieso, Jeremy Cosgrove, Pingche Zhang, Ji Liu, Yimeng Che, Shishuang Cui
    Abstract:

    There is an urgent need for developing objective, effective and convenient measurements to help clinicians accurately identify Bradykinesia. The purpose of this study is to evaluate the accuracy of an objective approach assessing Bradykinesia in finger tapping (FT) that uses evolutionary algorithms (EAs) and explore whether it can be used to identify early stage Parkinson’s disease (PD). One hundred and seven PD, 41 essential tremor (ET) patients and 49 normal controls (NC) were recruited. Participants performed a standard FT task with two electromagnetic tracking sensors attached to the thumb and index finger. Readings from the sensors were transmitted to a tablet computer and subsequently analyzed by using EAs. The output from the device (referred to as "PD-Monitor") scaled from − 1 to + 1 (where higher scores indicate greater severity of Bradykinesia). Meanwhile, the Bradykinesia was rated clinically using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) FT item. With an increasing MDS-UPDRS FT score, the PD-Monitor score from the same hand side increased correspondingly. PD-Monitor score correlated well with MDS-UPDRS FT score (right side: r = 0.819, P = 0.000; left side: r = 0.783, P = 0.000). Moreover, PD-Monitor scores in 97 PD patients with MDS-UPDRS FT Bradykinesia and each PD subgroup (FT Bradykinesia scored from 1 to 3) were all higher than that in NC. Receiver operating characteristic (ROC) curves revealed that PD-Monitor FT scores could detect different severity of Bradykinesia with high accuracy (≥89.7%) in the right dominant hand. Furthermore, PD-Monitor scores could discriminate early stage PD from NC, with area under the ROC curve greater than or equal to 0.899. Additionally, ET without Bradykinesia could be differentiated from PD by PD-Monitor scores. A positive correlation of PD-Monitor scores with modified Hoehn and Yahr stage was found in the left hand sides. Our study demonstrated that a simple to use device employing classifiers derived from EAs could not only be used to accurately measure different severity of Bradykinesia in PD, but also had the potential to differentiate early stage PD from normality.

Laura Santoso - One of the best experts on this subject based on the ideXlab platform.

  • substantia nigra volume dissociates Bradykinesia and rigidity from tremor in parkinson s disease a 7 tesla imaging study
    Journal of Parkinson's disease, 2020
    Co-Authors: Kathlee L Posto, Yi Wang, Matthew Ua A I Cruadhlaoich, Laura Santoso, Jeffrey D Ernstei, Tia Liu, Geoffrey A Kerchne
    Abstract:

    BACKGROUND In postmortem analysis of late stage Parkinson's disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of Bradykinesia and rigidity, but not tremor. OBJECTIVE To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. METHODS We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for Bradykinesia/rigidity, tremor, and postural instability/gait difficulty). RESULTS We found that smaller SN correlated with longer disease duration (r = -0.49, p = 0.004), more severe MDS-UPDRS motor score (r = -0.42, p = 0.016), and more severe Bradykinesia-rigidity subscore (r = -0.47, p = 0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, Bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that Bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume. CONCLUSIONS In mild-to-moderate PD, SN volume relates to motor manifestations in a motor domain-specific and laterality-dependent manner. Non-invasive in vivo 7.0 Tesla QSM may serve as a biomarker in longitudinal studies of SN atrophy and in studies of people at risk for developing PD.

David Wong - One of the best experts on this subject based on the ideXlab platform.

  • supervised classification of Bradykinesia in parkinson s disease from smartphone videos
    Artificial Intelligence in Medicine, 2020
    Co-Authors: Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, Rami Qahwaji, Christophe D Graham, David Wong
    Abstract:

    Abstract Background Slowness of movement, known as Bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess Bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of Bradykinesia. Methods We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of Bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naive Bayes, Logistic Regression, Support Vector Machine) to predict no/slight Bradykinesia (UPDRS=0-1) or mild/moderate/severe Bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis. Results A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe Bradykinesia with an estimated test accuracy of 0.8. A Naive Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion The method described here presents an approach for predicting Bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of Bradykinesia prediction is comparable to that recorded by blinded human experts.

  • the discerning eye of computer vision can it measure parkinson s finger tap Bradykinesia
    Journal of the Neurological Sciences, 2020
    Co-Authors: Stefa Williams, Zhibi Zhao, Samuel Relto, Jane Alty, David Wong, Hui Fang, Awais Hafeez
    Abstract:

    Abstract Objective The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, Bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's Bradykinesia from smartphone videos of finger tapping. Methods Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Results DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of Bradykinesia (Spearman coefficients): −0.74 speed, 0.66 amplitude, −0.65 rhythm for MBRS; −0.56 speed, 0.61 amplitude, −0.50 rhythm for MDS-UPDRS; −0.69 combined for MDS-UPDRS. All p  Conclusion New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's Bradykinesia from smartphone videos of finger tapping. Objective ‘contactless’ measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely ‘contactless’. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements.

  • time series clustering to examine presence of decrement in parkinson s finger tapping Bradykinesia
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2020
    Co-Authors: Zhibi Zhao, Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, Alexande J Casso, David Wong
    Abstract:

    Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no Bradykinesia and participants with severe Bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance— Our work visually presents the archetypal types of Bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no Bradykinesia, and the other showing linear decrement over time.

  • Time series clustering to examine presence of decrement in Parkinson’s finger-tapping Bradykinesia
    2020
    Co-Authors: Zhibi Zhao, Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, David Wong
    Abstract:

    — Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely. We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smartphone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster. Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no Bradykinesia and participants with severe Bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time. Clinical relevance— Our work visually presents the archetypal types of Bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no Bradykinesia, and the other showing linear decrement over time

Geoffrey A Kerchne - One of the best experts on this subject based on the ideXlab platform.

  • substantia nigra volume dissociates Bradykinesia and rigidity from tremor in parkinson s disease a 7 tesla imaging study
    Journal of Parkinson's disease, 2020
    Co-Authors: Kathlee L Posto, Yi Wang, Matthew Ua A I Cruadhlaoich, Laura Santoso, Jeffrey D Ernstei, Tia Liu, Geoffrey A Kerchne
    Abstract:

    BACKGROUND In postmortem analysis of late stage Parkinson's disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of Bradykinesia and rigidity, but not tremor. OBJECTIVE To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. METHODS We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for Bradykinesia/rigidity, tremor, and postural instability/gait difficulty). RESULTS We found that smaller SN correlated with longer disease duration (r = -0.49, p = 0.004), more severe MDS-UPDRS motor score (r = -0.42, p = 0.016), and more severe Bradykinesia-rigidity subscore (r = -0.47, p = 0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, Bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that Bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume. CONCLUSIONS In mild-to-moderate PD, SN volume relates to motor manifestations in a motor domain-specific and laterality-dependent manner. Non-invasive in vivo 7.0 Tesla QSM may serve as a biomarker in longitudinal studies of SN atrophy and in studies of people at risk for developing PD.

Stefa Williams - One of the best experts on this subject based on the ideXlab platform.

  • supervised classification of Bradykinesia in parkinson s disease from smartphone videos
    Artificial Intelligence in Medicine, 2020
    Co-Authors: Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, Rami Qahwaji, Christophe D Graham, David Wong
    Abstract:

    Abstract Background Slowness of movement, known as Bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess Bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of Bradykinesia. Methods We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of Bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naive Bayes, Logistic Regression, Support Vector Machine) to predict no/slight Bradykinesia (UPDRS=0-1) or mild/moderate/severe Bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis. Results A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe Bradykinesia with an estimated test accuracy of 0.8. A Naive Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion The method described here presents an approach for predicting Bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of Bradykinesia prediction is comparable to that recorded by blinded human experts.

  • the discerning eye of computer vision can it measure parkinson s finger tap Bradykinesia
    Journal of the Neurological Sciences, 2020
    Co-Authors: Stefa Williams, Zhibi Zhao, Samuel Relto, Jane Alty, David Wong, Hui Fang, Awais Hafeez
    Abstract:

    Abstract Objective The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, Bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's Bradykinesia from smartphone videos of finger tapping. Methods Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Results DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of Bradykinesia (Spearman coefficients): −0.74 speed, 0.66 amplitude, −0.65 rhythm for MBRS; −0.56 speed, 0.61 amplitude, −0.50 rhythm for MDS-UPDRS; −0.69 combined for MDS-UPDRS. All p  Conclusion New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's Bradykinesia from smartphone videos of finger tapping. Objective ‘contactless’ measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely ‘contactless’. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements.

  • time series clustering to examine presence of decrement in parkinson s finger tapping Bradykinesia
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2020
    Co-Authors: Zhibi Zhao, Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, Alexande J Casso, David Wong
    Abstract:

    Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no Bradykinesia and participants with severe Bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance— Our work visually presents the archetypal types of Bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no Bradykinesia, and the other showing linear decrement over time.

  • Time series clustering to examine presence of decrement in Parkinson’s finger-tapping Bradykinesia
    2020
    Co-Authors: Zhibi Zhao, Stefa Williams, Samuel Relto, Jane Alty, Hui Fang, David Wong
    Abstract:

    — Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely. We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smartphone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster. Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no Bradykinesia and participants with severe Bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time. Clinical relevance— Our work visually presents the archetypal types of Bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no Bradykinesia, and the other showing linear decrement over time