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| Max A. Little; Patrick E. McSharry; Eric J. Hunter; Jennifer Spielman; Lorraine O. Ramig. |
| We present an assessment of the practical value of existing traditional and non-standard measures for discriminating healthy people from people with Parkinson?s disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, Pitch Period Entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected 10 highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that... |
| Tipo: Manuscript |
Palavras-chave: Neuroscience; Bioinformatics. |
| Ano: 2008 |
URL: http://precedings.nature.com/documents/2298/version/1 |
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| Athanasios Tsanas; Max A. Little; Patrick E. McSharry; Lorraine O. Ramig. |
| Tracking Parkinson's disease (PD) symptom progression often uses the Unified Parkinson’s Disease Rating Scale (UPDRS), which requires the patient's presence in clinic, and time-consuming physical examinations by trained medical staff. Thus, symptom monitoring is costly and logistically inconvenient for patient and clinical staff alike, also hindering recruitment for future large-scale clinical trials. Here, for the first time, we demonstrate rapid, remote replication of UPDRS assessment with clinically useful accuracy (about 7.5 UPDRS points difference from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. We characterize speech with signal processing algorithms,... |
| Tipo: Manuscript |
Palavras-chave: Neuroscience; Bioinformatics. |
| Ano: 2009 |
URL: http://precedings.nature.com/documents/3920/version/1 |
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