An explainable echo state network trained from photoplethysmography signals for equine life stage prediction.

Determining the life stage of animals is crucial for assessing their overall health and making informed decisions for their care. However, traditional veterinary practices often rely on subjective methods, resulting in fuzziness or ambiguity. A quantitative metric via measurable vital signs has yet to be developed, which could be due to lack of a sophisticated methodology that can correlate the vital signs with age. To tackle this challenge, we present a method for equine life stage (young to old) classification from photoplethysmography (PPG) waveforms collected from 50 equine subjects with various ages, sex, and breeds. The data were collected using compact, wearable PPG sensors on their tails. The collected waveforms served as the input for the classification by an echo state network (ESN). It was found that the classification accuracy depended on the age split, suggesting a fussiness in classifying the youth and elder. Using the 17th year as the splitting age, the highest training and testing accuracies of 81.3% and 81.1% were achieved, respectively. In addition, input features from the reservoirs of the ESN were extracted and analyzed by a kernel principal component analysis, which afforded a 3D PCA map with clear clusters according to the age groups. This suggests that the ESN can learn hidden information from the PPG waves to classify the life stage. The proposed algorithm exhibits immense potential in veterinary medicine by offering a more objective and reliable approach to animal life stage assessment. [ABSTRACT FROM AUTHOR]