Assessment of bipolar disorder using heterogeneous data of smartphone-based digital phenotyping

Hung Yi Su, Chung Hsien Wu, Cheng Ray Liou, Esther Ching Lan Lin, Po See Chen

Research output: Contribution to journalConference articlepeer-review

Abstract

In mental health disorder, Bipolar Disorder (BD) is one of the most common mental illness. Using rating scales for assessment is one of the approaches for diagnosing and tracking BD patients. However, the requirement for manpower and time is heavy in the process of evaluation. In order to reduce the cost of social and medical resources, this study collects the user’s data by the App on smartphones, consisting of location data (GPS), self-report scales, daily mood, sleeping time and records of multi-media (text, speech, video) which are heterogeneous digital phenotyping data, to build a database. The features of each heterogeneous digital phenotyping data are extracted independently. Lasso Regression and ElasticNet Regression methods are employed to predict the score of Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS), as a reference for the evaluation of BD. As incomplete and missing data are very common in medical research, the ensemble method is adopted to combine the results from different models trained with different combinations of missing data. The collected heterogeneous digital phenotyping data from 84 BD patients were used for training and evaluation of the proposed approach based on five-fold cross validation method. Experimental results show that the performance of the assessment system using the proposed method are encouraging.

Original languageEnglish
Pages (from-to)4260-4264
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 2021 Jun 62021 Jun 11

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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