Generalisable machine learning models trained on heart rate variability data to predict mental fatigue

Abstract

A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to detect fatigue in order to prevent its consequences. However, the results of these studies cannot be generalised because of various methodological issues including the use of only one type of cognitive task to induce fatigue which makes any predictions task-specific. In this study, we combined the datasets of three experiments each of which applied different cognitive tasks for fatigue induction and trained algorithms that detect fatigue and predict its severity. We also tested different time window lengths and compared algorithms trained on resting and task related data. We found that classification performance was best when the support vector classifier was trained on task related HRV calculated for a 5-min time window (AUC = 0.843, accuracy = 0.761). For the prediction of fatigue severity, CatBoost regression showed the best performance when trained on 3-min HRV data and self-reported measures (R2 = 0.248, RMSE = 17.058). These results indicate that both the detection and prediction of fatigue based on HRV are effective when machine learning models are trained on heterogeneous, multi-task datasets.

Time‐domain heart rate variability features for automatic congestive heart failure prediction

“…The chosen performance metrics were selected to evaluate binary classifiers on imbalanced datasets because they provide more informative and less misleading results compared with specificity and receiver operating characteristic (ROC) plots. 37 ROC plots are visual tools for assessing the performance of binary classification models, especially when evaluating their sensitivity and specificity across different decision thresholds. The area under the ROC curve (AUC) is a single metric that summarizes the performance of the model over all possible thresholds.…”

Next Generation Computing and Communication Hub for First Responders in Smart Cities

“…CW monitoring is a process that involves at least two multifaceted processes: stress [21,23,24,86] and mental fatigue [22,87,88]. Stress is a complex psycho-physiological pattern, a response of the body to any demand for change.…”

Огляд Сучасних Підходів До Визначення Фізичної Втоми За Допомогою Моделей Штучного Інтелекту На Основі Аналізу Серцевих Сигналів

“…Останнім часом спостерігається значний інтерес до використання машинного навчання для аналізу серцевих даних. Алгоритми, такі як рекурентні нейронні мережі (RNN) і згорткові нейронні мережі (CNN), стають все популярнішими у цій сфері [4][5].…”

“…Оглядова робота [4] зосереджується на виявленні психічної втоми за допомогою даних про варіабельність серцевого ритму (HRV) за допомогою класифікаційних та регресійних алгоритмів машинного навчання. Це важливо, оскільки тривала когнітивна діяльність часто призводить до психічної втоми, що підвищує ризик аварій.…”