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Predicting risk of suicide using resting state heart rate

Authors: 

Daud Sikander, Mahnaz Arvaneh, Francesco Amico, Graham Healy, Tomas Ward, Damien Kearney, Eva Mohedano, Jennifer Fagan, John Yek, Alan Smeaton, Justin Brophy

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
This study investigates the potential of using heart rate-related measurements to aid clinicians in predicting suicide risk. For this purpose, heart rate was recorded during 10 minutes resting state from 15 patients with suicide ideation and 15 healthy subjects using an affordable and wearable sensor. Our results showed statistically significant differences (p<0.05) in two time-domain features measuring overall heart rate variability and short term heart rate variations. KNN and SVM classifiers were implemented on the features obtained. Our results showed that using heart rate-related features the risk of suicide could be predicted by an average accuracy of 80%.
Conference Name: 
Signal and Information Processing Association Annual Summit and Conference
Proceedings: 
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016 Asia-Pacific
Digital Object Identifer (DOI): 
10.1109/APSIPA.2016.7820833
Publication Date: 
13/12/2016
Conference Location: 
China
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
No