Prediction and Tracking Changes in Bio-medical Sensor Data

Both our preliminary works [1], [2], on biomedical sensor signal processing, have focused on abdominal tumor motion traces. In stereotactic radiotherapy for thoracic and abdominal tumors, respiratory motion management is crucial for improving efcacy of treatment, while minimizing risk to heathy tissue and organs. Since tumor motion exhibits dynamic variation in characteristics, between and within patients, our first work concentrated on predicting imminent anomalous or irregular tumor motion ahead of its occurrence, and our second work consists of analysis of behavioral distribution of tumor motion, used for patient grouping with the aim to improve treatment planning. As an immediate step forward for improving treatment planning for patients with abdominal tumor, we are currently focusing on developing patient profiles. The profiles will be in the form of a histogram, based on behaviors illustrated by a patient during a particular treatment session. We expect the patient profiles to advance interpretability of patient groping or clusters, which in return will improve the decision of choosing a treatment plan. We need to decide the best distance metric to use for histogram comparison and wish to reduce the number of working parameters needed to describe the profile. It is evident from these two works that predicting future patterns of a signal is critical to treatment strategies in a clinical setting. If done correctly with high precision and accuracy, predictions can be used to prevent catastrophic events or at least, reduce the risk during such an occurrence e.g. prediction of epileptic seizure. In such cases, the physician might be interested in tracking anomalous changes in a patients accelerometer signal, as it may be suggestive of the development of a future ailment. In this light, as future work, we propose to develop an algorithm that follows a stepwise procedure to either track or predict signal patterns as per user request. At this stage, we do not want to restrict the source of the signal to any one particular body sensor. The algorithm should also be able to handle different dimensionality of the data. Furthermore, our work with tumor motion traces has made us realize the difficulties in processing body sensor signals that have varying periods and amplitudes and have patterns that vary from subject to subject and often during different recording of the same subject. And yet, some sensor data, such as electrocardiogram (ECG) signals, are more rhythmic in nature, with a strict period and limited but predictable changes in amplitude. Thus, it is likely that a set of analysis developed for predicting or tracking ECG data will not perform well with data demonstrating greater variation. This provides the need for choosing the appropriate combination of methods/parameters for analyzing signals from different biomedical sensors. As such, for our long-term goal, 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 time-series into segments. In our second preliminary work, 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 be to use fixed length segmentation, then it will use a variation of genetic programming to determine the best value L, e.g. for ECG data, it should be in the order of a second. The proposal of this module development raises two difficulties: one, a need for an expert user, which can only be overcome via thee use of an indexed data storage system and two, a need for one or more machine learning technique utilizing precise artificial intelligence (AI). While the aim of the module is biomedical sensor signal analysis, the development of such a module requires interdisciplinary skills in form of the management of the data store and creating an AI unit. We hope to continue working with field experts to provide us with the necessary knowledge required to finish the projects described here.