Authors:
Ariella Richardson
1
;
Shani Ben Ari
1
;
Maayan Sinai
1
;
Aviya Atsmon
1
;
Ehud S. Conley
1
;
Yohai Gat
1
and
Gil Segev
2
Affiliations:
1
Lev Academic Center, Jerusalem and Israel
;
2
BGSegev Ltd. (segevlabs.org), Jerusalem and Israel
Keyword(s):
Digital Health, m-Health, Mobile, Stroke, Speech, Cardiovascular, Machine Learning, Data Mining.
Abstract:
Strokes are a cause of serious long-term disability and create an immense burden on healthcare. Among the sea of mobile applications for health, some target stroke patients, and most require active user cooperation. Our proposed application, collects data, without user intervention. We apply data mining methods to create personal feedback to the patient or doctor. We provide a survey of applications for mobile or wearables, specifically for stroke. We also survey papers that apply data mining to stroke. In addition to the survey, we present a feasibility study on using speech for classification of stroke patients. We created a new data set of unstructured speech recordings, increasing applicability. We present experimental results on classification of stroke patients. Our study provides promising insight to detecting stroke patients using a mobile application without requiring active user participation.