Continuous assessment of schizophrenia using heart rate and accelerometer data

• Published in: Physiological measurement Vol. 38; no. 7; pp. 1456 - 1471
• Main Authors: Reinertsen, Erik, Osipov, Maxim, Liu, Chengyu, Kane, John M, Petrides, Georgios, Clifford, Gari D
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 27.06.2017
• Subjects: entropy; heart rate; machine learning; mhealth; mobile health; schizophrenia; support vector machine
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/1361-6579/aa724d

Objective: Schizophrenia has been associated with changes in heart rate (HR) and physical activity measures. However, the relationship between analysis window length and classifier accuracy using these features has yet to be quantified. Approach: Here we used objective HR and activity data to classify contiguous days of data as belonging to a schizophrenia patient or a healthy control. HR and physical activity recordings were made on 12 medicated subjects with schizophrenia and 12 healthy controls. Features derived from these data included classical statistical characteristics, rest-activity metrics, transfer entropy, and multiscale fuzzy entropy. We varied the analysis window length from two to eight days, and selected features via minimal-redundancy-maximal-relevance. A support vector machine was trained to classify schizophrenia from control windows on a daily basis. Model performance was assessed via subject-wise leave-one-out-crossfold-validation. Main results: An analysis window length of eight days resulted in an area under a receiver operating characteristic curve (AUC) of 0.96. Reducing the analysis window length to two days only lowered the AUC to 0.91. The type of most predictive features varied with analysis window length. Significance: Our results suggest continuous tracking of subjects with schizophrenia over short time scales may be sufficient to estimate illness severity on a daily basis.