Carlo Ricciardi1, Marianna Amboni2, Chiara De Santis3, Giovanni Improta4, Giampiero Volpe5, Luigi Iuppariello6, Gianluca Ricciardelli5, Giovanni D'Addio7, Carmine Vitale8, Paolo Barone3, Mario Cesarelli9. 1. Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Via S. Pansini, 5, Naples 80131, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Via bagni vecchi, 1, Telese Terme (BN), Italy. 2. Center for Neurodegenerative Diseases, Department of Medicine and Surgery, University of Salerno, Via San Leonardo, Salerno 84131, Italy; Istituto di Diagnosi e Cura Hermitage-Capodimonte, Naples, Italy. 3. Center for Neurodegenerative Diseases, Department of Medicine and Surgery, University of Salerno, Via San Leonardo, Salerno 84131, Italy. 4. Department of Public Health, University Hospital of Naples 'Federico II', Via S. Pansini, 5, Naples 80131, Italy. 5. Azienda Ospedaliera Universitaria OO.RR. San Giovanni di Dio Ruggi d'Aragona - Scuola Medica Salernitana, Via San Leonardo, Salerno 84131, Italy. 6. Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Via Claudio, 21, Naples, Italy; Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy. 7. Istituti Clinici Scientifici Maugeri IRCCS, Via bagni vecchi, 1, Telese Terme (BN), Italy. 8. Department of Motor Sciences and Wellness, University of Naples Parthenope, Via Ammiraglio Ferdinando Acton, 38, Naples 80133, Italy. 9. Istituti Clinici Scientifici Maugeri IRCCS, Via bagni vecchi, 1, Telese Terme (BN), Italy; Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Via Claudio, 21, Naples, Italy. Electronic address: cesarell@unina.it.
Abstract
INTRODUCTION: Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis. METHODS: A cohort of 46 subjects (divided into three groups) affected by PD patients at different stages and PSP patients was acquired through gait analysis and spatial and temporal parameters were analysed. Synthetic Minority Over-sampling Technique was used to balance our imbalanced dataset and cross-validation was applied to provide different training and testing sets. Then, Random Forests and Gradient Boosted Trees were implemented. RESULTS: Accuracy, error, precision, recall, specificity and sensitivity were computed for each group and for both algorithms, including 16 features. Random Forests obtained the highest accuracy (86.4%) but also specificity and sensitivity were particularly high, overcoming the 90% for PSP group. CONCLUSION: The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns. This application would be helpful for clinicians to distinguish PSP from PD at early stage, when the differential diagnosis is particularly challenging.
INTRODUCTION:Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PDpatients at different stages of the disease course and PSP using all the variables acquired through gait analysis. METHODS: A cohort of 46 subjects (divided into three groups) affected by PDpatients at different stages and PSPpatients was acquired through gait analysis and spatial and temporal parameters were analysed. Synthetic Minority Over-sampling Technique was used to balance our imbalanced dataset and cross-validation was applied to provide different training and testing sets. Then, Random Forests and Gradient Boosted Trees were implemented. RESULTS: Accuracy, error, precision, recall, specificity and sensitivity were computed for each group and for both algorithms, including 16 features. Random Forests obtained the highest accuracy (86.4%) but also specificity and sensitivity were particularly high, overcoming the 90% for PSP group. CONCLUSION: The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns. This application would be helpful for clinicians to distinguish PSP from PD at early stage, when the differential diagnosis is particularly challenging.
Authors: Marianna Amboni; Carlo Ricciardi; Marina Picillo; Chiara De Santis; Gianluca Ricciardelli; Filomena Abate; Maria Francesca Tepedino; Giovanni D'Addio; Giuseppe Cesarelli; Giampiero Volpe; Maria Consiglia Calabrese; Mario Cesarelli; Paolo Barone Journal: Sci Rep Date: 2021-04-29 Impact factor: 4.379
Authors: Maikel Luis Kolling; Leonardo B Furstenau; Michele Kremer Sott; Bruna Rabaioli; Pedro Henrique Ulmi; Nicola Luigi Bragazzi; Leonel Pablo Carvalho Tedesco Journal: Int J Environ Res Public Health Date: 2021-03-17 Impact factor: 3.390
Authors: Alfonso Maria Ponsiglione; Carlo Ricciardi; Francesco Amato; Mario Cesarelli; Giuseppe Cesarelli; Giovanni D'Addio Journal: Sensors (Basel) Date: 2022-02-22 Impact factor: 3.576