MULTISAB: A Web Platform for Analysis of Multivariate Heterogeneous Biomedical Time-Series

There is a growing need for efficient and accurate biomedical software in healthcare community. In this paper, we present MULTISAB, a web platform whose goal is to provide users with detailed analysis capabilities for heterogeneous biomedical time series. We describe the system architecture, including its subprojects: fron-tend, backend and processing. Emphasis is placed on the processing subproject, implemented in Java, which incorporates data analysis methods. The subproject is divided into several frameworks: record input handling, preprocessing, signal visualization, general time series features extraction, specific (domain) time series features extraction, expert system recommendations, data mining, and reporting. Common signal features extraction framework includes a great number of features in time (both linear and nonlinear), frequency and time-frequency domain. Currently, domain specific frameworks for heart rate variability, ECG and EEG feature extraction are supported, which also include preprocessing techniques for noise reduction and detection methods for characteristic waveforms (like QRS complexes, P and T waves in ECG). Parallelization is implemented for feature extraction to increase performance. It is realized using multi-threading on several levels: for multiple records, traces, and segments. Expert system is implemented, which provides automatic recommendation of the set of significant expert features that should be extracted from the analyzed signals, depending on the analysis scenario. The expert system, apart from the role in recommending features, can also participate in automatic diagnosis, after the features are extracted. Current expert system prototype contains diagnostic rules for acute myocardial ischemia, based on medical guidelines. Data mining framework contains dimensionality reduction methods and machine learning classifiers used to construct accurate and interpretable disorder models. A report is produced at the end of the process using openly available libraries. The platform includes best practices from medicine, biomedical engineering, and computer science in order to deliver detailed biomedical time series analysis services to its users.