A Non-Invasive IR Sensor Technique to Differentiate Parkinson’s Disease from Other Neurological Disorders Using Autonomic Dysfunction as Diagnostic Criterion

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 1; p. 266
• Main Authors: Anbalagan, Brindha, Karnam Anantha, Sunitha, Arjunan, Sridhar, Balasubramanian, Venkatraman, Murugesan, Menaka, R, Kalpana
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.12.2021; MDPI
• Subjects: autonomic dysfunction; Central Nervous System; Cold; cold stress test; Data collection; Disease Progression; Essential Tremor; Heart rate; Humans; Neurological disorders; Parkinson Disease; Parkinson's disease; ROC Curve; Skin; thermography; vasoconstriction; skin temperature; cold stress test; autonomic dysfunction; essential tremor; vasoconstriction; thermography; Parkinson’s disease
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22010266

Early diagnosis of Parkinson’s disease (PD) plays a critical role in effective disease management and delayed disease progression. This study reports a technique that could diagnose and differentiate PD from essential tremor (ET) in its earlier stage using a non-motor phenotype. Autonomic dysfunction, an early symptom in PD patients, is caused by α-synuclein pathogenesis in the central nervous system and can be diagnosed using skin vasomotor response to cold stimuli. In this study, the investigations were performed using data collected from 20 PD, 20 ET and 20 healthy subjects. Infrared thermography was used for the cold stress test to observe subjects’ hand temperature before and after cold stimuli. The results show that the recovery rate of hand temperature was significantly different between the groups. The data obtained in the cold stress test were verified using Pearson’s cross-correlation technique, which showed that few disease parameters like medication and motor rating score had an impact on the recovery rate of hand temperature in PD subjects. The characteristics of the three groups were compared and classified using the k-means clustering algorithm. The sensitivity and specificity of these techniques were analyzed using an Receiver Operating Characteristic (ROC) curve analyzer. These results show that this non-invasive technique can be used as an effective tool in the diagnosis and differentiation of PD in its early stage.