# Model Explainability in Physiological and Healthcare-based Neural Networks

arXiv: https://arxiv.org/abs/2304.14495 · HTML: https://mlsanity.com/paper/2304.14495/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [An Empirical Analysis of Feature Engineering for Predictive Modeling](https://mlsanity.com/paper/1701.07852/) (2017)
- [Spectral Reflectance based Heart Rate Measurement from Facial Video](https://mlsanity.com/paper/1901.00273/) (2019)
- [AI Challenges for Society and Ethics](https://mlsanity.com/paper/2206.11068/) (2022)
- [A neuromorphic approach to image processing and machine vision](https://mlsanity.com/paper/2209.02595/) (2022)
- [Implementation of fast ICA using memristor crossbar arrays for blind image source separations](https://mlsanity.com/paper/2208.04317/) (2022)
- [WeightMom: Learning Sparse Networks using Iterative Momentum-based pruning](https://mlsanity.com/paper/2208.05970/) (2022)

## Abstract (arXiv)

The estimation and monitoring of SpO2 are crucial for assessing lung function and treating chronic pulmonary diseases. The COVID-19 pandemic has highlighted the importance of early detection of changes in SpO2, particularly in asymptomatic patients with clinical deterioration. However, conventional SpO2 measurement methods rely on contact-based sensing, presenting the risk of cross-contamination and complications in patients with impaired limb perfusion. Additionally, pulse oximeters may not be available in marginalized communities and undeveloped countries. To address these limitations and provide a more comfortable and unobtrusive way to monitor SpO2, recent studies have investigated SpO2 measurement using videos. However, measuring SpO2 using cameras in a contactless way, particularly from smartphones, is challenging due to weaker physiological signals and lower optical selectivity of smartphone camera sensors. The system includes three main steps: 1) extraction of the region of interest (ROI), which includes the palm and back of the hand, from the smartphone-captured videos; 2) spatial averaging of the ROI to produce R, G, and B time series; and 3) feeding the time series into an optophysiology-inspired CNN for SpO2 estimation. Our proposed method can provide a more efficient and accurate way to monitor SpO2 using videos captured from consumer-grade smartphones, which can be especially useful in telehealth and health screening settings.