Visualizing Neonatal Spells: Temporal Visual Analytics of High Frequency Cardiorespiratory Physiological Event Streams
The authors introduce a novel visualization for neonatal spells analytics  that enables clinical researchers to visually detect trends and patterns in large collections of physiological data monitored in a neonatal intensive care unit.
Neonatal clinicians are able to explore the dataset for abnormalities and react rapidly. Visualizing such a large dataset also minimizes the impact of misinformation caused by errors (in this case, medical monitor failures) .
Figure 1 shows an overview of the system visualizing an infant's breathing pauses over 42 days. The x-axis represents the bin size used, which is the summed number of breathing pauses per hour. The y-axis indicates the date. Darker color indicates higher frequency.
The authors manually set the bin sizes by evaluating intervals in the dataset. The system also provides sampling and hierarchical clustering by occurrence and severity.
- Data source: The neonatal intensive care unit at The Hospital for Sick Children
- Size: 42 days of continuous monitoring of 1 infant
- Spatial dimensionality: 2D
- Temporal dimensionality: static
- Type: multivariate
- Libraries used: web-based, D3.js, Nodejs
- Electronic Health Record Visualization
- Medical focus: Neonatal Intensive Care
Kamaleswaran, R., Pugh, J. E., Thommandram, A., James, A., & Mcgregor, C. (2014). Visualizing Neonatal Spells: Temporal Visual Analytics of High Frequency Cardiorespiratory Physiological Event Streams. IEEE VIS 2014 Workshop on Visualization of Electronic Health Records, 1–4. ↩︎
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