State-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence

Abstract

Recent advancements in the manufacturing and commercialisation of miniaturised sensors and low-cost wearables have enabled an effortless monitoring of lifestyle by detecting and analysing physiological signals. Heart rate variability (HRV) denotes the time interval between consecutive heartbeats.The HRV signal, as detected by the sensors and devices, has been popularly used as an indicative measure to estimate the level of stress, depression, and anxiety. For years, artificial intelligence (AI)-based learning systems have been known for their predictive capabilities, and in recent years, AI models with deep learning (DL) architectures have been successfully applied to achieve unprecedented accuracy. In order to determine effective methodologies applied to the collection, processing, and prediction of stress from HRV data, this work presents an in depth analysis of 43 studies reporting the application of various AI algorithms. The methods are summarised in tables and thoroughly evaluated to ensure the completeness of their findings and reported results. To make the work comprehensive, a detailed review has been conducted on sensing technologies, pre-processing methods applied on multi-modal data, and employed prediction models. This is followed by a critical examination of how various Machine Learning (ML) models, have been utilised in predicting stress from HRV data. In addition, the reported reseults from the selected studies have been carefully analysed to identify features that enable the models to perform better. Finally, the challenges of using HRV to predict stress are listed, along with some possible mitigation strategies. This work aims to highlight the impact of AI-based stress prediction methodologies from HRV data, and is expected to aid the development of more meticulous techniques.

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

“…The band δ (0.5 ÷ 4 Hz) signals slow brain activity linked to cortical damage, θ (4 ÷ 8 Hz) indicates transitions between sleep and wakefulness suggesting potential dysfunctions, α (8 ÷ 12 Hz) is associated with resting states and reflects the alteration of brain organization in AD, and β (12 ÷ 30 Hz) highlights levels of attention and mental activity, which is useful for observing cognitive changes in the patient [32,33]. Finally, the γ rhythm, above 30 Hz, is associated with complex cognitive processes such as object recognition and meaning attribution, and it is mainly detectable in the frontal regions [32][33][34][35][36][37]. Detailed EEG analysis, which includes the observation of specific changes in frequency bands, helps define a neurophysiological profile of AD [38][39][40].…”