1. Classification of orthostatic intolerance through data analytics
- Author
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Steven Gilmore, Mette S. Olufsen, Jesper Mehlsen, Justen Geddes, Pierre-Alain Gremaud, Joseph Hart, and Christian Haargaard Olsen
- Subjects
medicine.medical_specialty ,Lightheadedness ,0206 medical engineering ,Biomedical Engineering ,Orthostatic intolerance ,Blood Pressure ,02 engineering and technology ,Disease ,Autonomic Nervous System ,Syncope ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Physical medicine and rehabilitation ,Heart Rate ,Tilt-Table Test ,Heart rate ,medicine ,Humans ,business.industry ,Data Science ,medicine.disease ,020601 biomedical engineering ,Computer Science Applications ,Random forest ,Blood pressure ,Categorization ,Orthostatic Intolerance ,Orthostatic tachycardia ,medicine.symptom ,business - Abstract
Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. We use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges. Graphical abstract Machine learning tools utilized to analyze heart rate (HR) and blood pressure (BP) time-series data from syncope and control patients. Results show that machine learning can provide accurate classification of disease groups for 98% of patients and we identified two subgroups within the control patients differentiated by their BP response.
- Published
- 2021
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