Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion.

Objective: Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenarios, the use of additional physiological signals such as arterial blood pressure (BP) has recently been suggested. Therefore, estimation of heartbeat location requires information fusion from multiple signals.

Reliable resource-constrained telecardiology via compressive detection of anomalous ECG signals.

Telecardiology is envisaged as a supplement to inadequate local cardiac care, especially, in infrastructure deficient communities. Yet the associated infrastructure constraints are often ignored while designing a traditional telecardiology system that simply records and transmits user electrocardiogram (ECG) signals to a professional diagnostic facility. Against this backdrop, we propose a two-tier telecardiology framework, where constraints on resources, such as power and bandwidth, are met by compressively sampling ECG signals, identifying anomalous signals, and transmitting only the anomalous signals.

Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).

Efficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC).

Mapping genomic features of tiling microarray data by TileMapper.

The recent revolution of genomics techniques has allowed the detection of various sequence features and biological variations on whole-genome scale. However, these high-resolution data present significant challenges for experimental biologists to understand and analyze. The conventional way is to use genome browsers to locate and visualize regions of interest.