Prediction and Tracking Changes in Bio-medical Sensor Data

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Shamsuddin:2015:ICHI,
  author =       "Rittika Shamsuddin",
  booktitle =    "2015 International Conference on Healthcare
                 Informatics (ICHI)",
  title =        "Prediction and Tracking Changes in Bio-medical Sensor
                 Data",
  year =         "2015",
  pages =        "468--468",
  abstract =     "Summary form only given. Both our preliminary works
                 [1], [2], on biomedical sensor signal processing, have
                 focused on abdominal tumour motion traces. In
                 stereo-tactic radiotherapy for thoracic and abdominal
                 tumours, respiratory motion management is crucial for
                 improving efficacy of treatment, while minimizing risk
                 to healthy tissue and organs. Since tumour motion
                 exhibits dynamic variation in characteristics, between
                 and within patients, our first work concentrated on
                 predicting imminent anomalous or irregular tumour
                 motion ahead of its occurrence, and our second work
                 consists of analysis of behavioural distribution of
                 tumour motion, used for patient grouping with the aim
                 to improve treatment planning. We propose to develop a
                 module that will automatically decide, which
                 methods/parameter to use based on the signal type. For
                 example, often the initial step for signal processing
                 involves dividing the sensors time-series into segments
                 and also we proposed a variable length segmentation
                 method and compared its performance against a
                 segmentation method that divided the signal at fixed
                 and equal interval of length, L. Depending on user
                 input or persistent memory (storing past experience or
                 expert opinion) associated with the module, the module
                 will decide which segmentation to use on the current
                 signal. If it decides that the best way to proceed
                 would which segmentation to use on the current signal.
                 If it decides that the best way to proceed would be to
                 use fixed length segmentation, then it will use a
                 variation of genetic programming to determine the best
                 value L.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICHI.2015.76",
  month =        oct,
  notes =        "Erik Jonsson Sch. of Eng. & Comput. Sci., Univ. of
                 Texas,

                 Also known as \cite{7349738}",
}

Genetic Programming entries for Rittika Shamsuddin

Citations