PRISM: a multi-metric framework for evaluating sensor data reconstruction
Publication date
2026-07-07
Document type
Research data
Organisational unit
Publisher
Zenodo
Part of the university bibliography
✅
Language
English
Keyword
Evaluation metrics
Hypoglycemia
Abstract
PRISM is a benchmark dataset for learning and evaluating unified evaluation metrics for heart rate (HR) imputation. The dataset is constructed from two publicly available physiological datasets, D1NAMO and the BIG IDEAS Lab Glycemic Variability and Wearable Device Data, both containing continuous heart rate recordings collected from wearable sensors. The HR signals were first preprocessed to remove invalid values and ensure continuous time series suitable for imputation experiments.
To generate the benchmark dataset, artificial missing segments of 5-minute and 15-minute durations were introduced into complete HR recordings. Each missing segment was independently reconstructed using three statistical imputation techniques: Linear Interpolation, Cubic Interpolation, and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). This process produces multiple imputed versions of the same HR segment, enabling systematic comparison between original and reconstructed signals.
For every imputed segment, five complementary evaluation metrics were computed by comparing the imputed HR values with the corresponding original HR values. These metrics capture both reconstruction accuracy and distributional similarity of the physiological signal. In addition, a Misclassification Rate (MR) was calculated by assigning both the original and imputed HR values to predefined physiological heart rate intervals (10 bpm and 20 bpm bins) and measuring the proportion of incorrectly classified values. The resulting dataset is formulated as a supervised regression dataset, where the five evaluation metrics serve as input features and Misclassification Rate serves as the target variable.
The dataset accompanies the FRAM-SHAP framework, where an explainable machine learning model is trained to predict the Misclassification Rate and determine the relative importance of each evaluation metric using SHAP analysis. The learned feature importance values are subsequently used to construct a unified combined evaluation metric for assessing HR imputation quality. However, the dataset itself is model-independent and can be used with any regression or explainable AI method for developing alternative combined evaluation metrics.
To generate the benchmark dataset, artificial missing segments of 5-minute and 15-minute durations were introduced into complete HR recordings. Each missing segment was independently reconstructed using three statistical imputation techniques: Linear Interpolation, Cubic Interpolation, and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). This process produces multiple imputed versions of the same HR segment, enabling systematic comparison between original and reconstructed signals.
For every imputed segment, five complementary evaluation metrics were computed by comparing the imputed HR values with the corresponding original HR values. These metrics capture both reconstruction accuracy and distributional similarity of the physiological signal. In addition, a Misclassification Rate (MR) was calculated by assigning both the original and imputed HR values to predefined physiological heart rate intervals (10 bpm and 20 bpm bins) and measuring the proportion of incorrectly classified values. The resulting dataset is formulated as a supervised regression dataset, where the five evaluation metrics serve as input features and Misclassification Rate serves as the target variable.
The dataset accompanies the FRAM-SHAP framework, where an explainable machine learning model is trained to predict the Misclassification Rate and determine the relative importance of each evaluation metric using SHAP analysis. The learned feature importance values are subsequently used to construct a unified combined evaluation metric for assessing HR imputation quality. However, the dataset itself is model-independent and can be used with any regression or explainable AI method for developing alternative combined evaluation metrics.
Description
Each row of the dataset corresponds to a single imputed HR segment.
Input Features:
Root Mean Square Error (RMSE): Measures the magnitude of reconstruction errors while assigning greater penalty to larger deviations between original and imputed HR values.
Mean Absolute Error (MAE): Computes the average absolute difference between original and imputed HR values, providing a robust measure of point-wise reconstruction accuracy.
Mean Absolute Percentage Error (MAPE): Measures reconstruction error relative to the magnitude of the original HR values, enabling comparison across different heart rate ranges.
Cohen's Distance Test (CDT): Measures the statistical similarity between the original and imputed HR signals using a threshold-based statistical distance.
Jensen-Shannon Distance (JSD): Quantifies the similarity between the probability distributions of the original and imputed HR values, assessing preservation of the underlying signal distribution.
Target Feature:
Misclassification Rate (MR): The regression target of the dataset. MR represents the proportion of imputed HR values assigned to an incorrect physiological heart rate interval compared with the original HR values. Lower MR values indicate better preservation of the physiological characteristics of the original signal.
Input Features:
Root Mean Square Error (RMSE): Measures the magnitude of reconstruction errors while assigning greater penalty to larger deviations between original and imputed HR values.
Mean Absolute Error (MAE): Computes the average absolute difference between original and imputed HR values, providing a robust measure of point-wise reconstruction accuracy.
Mean Absolute Percentage Error (MAPE): Measures reconstruction error relative to the magnitude of the original HR values, enabling comparison across different heart rate ranges.
Cohen's Distance Test (CDT): Measures the statistical similarity between the original and imputed HR signals using a threshold-based statistical distance.
Jensen-Shannon Distance (JSD): Quantifies the similarity between the probability distributions of the original and imputed HR values, assessing preservation of the underlying signal distribution.
Target Feature:
Misclassification Rate (MR): The regression target of the dataset. MR represents the proportion of imputed HR values assigned to an incorrect physiological heart rate interval compared with the original HR values. Lower MR values indicate better preservation of the physiological characteristics of the original signal.
License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
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