Classifying unevenly spaced clinical time series data using forecast error approximation based bottom-up (FeAB) segmented time delay neural network

ABSTRACT

Clinical time series data contain large set of time-stamped data points that describe the patient’s health. The observations of these data points are done at irregular intervals and hence knowledge mining turns challenging. To overcome this, there is a need to reduce the dimension (length) of time series data into smaller representations with minimal loss of information. The objective of this work is to present a forecast-error approximation-based bottom-up (FeAB) segmentation for segmenting and classifying clinical time series data using time delay neural network (TDNN). The proposed approach includes two functionalities namely temporal data summarisation and classification. In temporal data summarisation, clinical time-series data are divided into sequence of temporal interpreted segments using FeAB segmentation. FeAB adopts a double exponential smoothing technique to derive the growth rate, mean and forecast-error for each clinical observation. The obtained forecast-error is used to compute the merge-cost for FeAB segmentation. TDNN classifier builds classification model for the segmented time series. The classifier is trained using backpropagation with Levenberg-Marquardt algorithm. The time series dataset of hepatitis and thrombosis patients are used for experimentation. The results illustrate that the proposed framework has effectively handled the temporal data irregularities and has shown improvement in classification accuracy.

KEYWORDS:

• Clinical time series data
• temporal summarisation
• segmentation
• time delay neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Y. Nancy Jane

Nancy Jane Y completed B.Tech. in Information Technology (2005), M.Tech. in Information Technology (2007) and Ph.D. in Computer Science and Engineering (2017) from Anna University, Chennai, Tamil Nadu, India. She is currently working as an Assistant Professor in Madras Institute of Technology Campus, Anna University, Chennai, Tamil Nadu, India. Her area of interest include Data Mining, Temporal Data Analytics, Machine Learning and Health-Care Intelligence.

H. Khanna Nehemiah

Khanna Nehemiah H completed B.E. in Computer Science and Engineering (1997), M.E. in Computer Science and Engineering (1998) from University of Madras, Chennai, Tamil Nadu, India and Ph.D. in Computer Science and Engineering (2007) from Anna University, Chennai, Tamil Nadu, India. He is currently working as a Professor in Ramanujan Computing Centre, Anna University, Chennai, Tamil Nadu, India. His areas of interest include Software Engineering, Database Systems, Data Mining, Medical Image Processing, Artificial Intelligence, Soft computing and Bio-inspired Computing.

Arputharaj Kannan

Kannan Arputharaj completed M.E in Computer Science and Engineering (1991) and Ph.D. in Computer Science and Engineering (2000) from Anna University, Chennai, Tamil Nadu, India. He retired as a Professor from the Department of Information Science and Technology, Anna University, Chennai, Tamil Nadu, India. He is currently working as a Senior Professor in Vellore Institute of Technology, Vellore. His areas of interest include Database Management Systems, Artificial Intelligence, Bio-inspired Computing and Information Security.