Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis

Castaldo, Rossana; Melillo, Paolo; Izzo, Raffaele; Luca, Nicola De; Pecchia, Leandro

doi:10.1109/jbhi.2016.2543960

Cited by 34 publications

(25 citation statements)

References 32 publications

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“…Finally, the tool has been designed to be integrated in a cloud-based platform[45], for risk assessment of vascular events[46] and falls. In particular, the current study was performed within the framework of a wider research activity focused on innovative methods for fall risk assessment in ophthalmic and cardiologic patients[47]. …”

Section: Discussionmentioning

confidence: 99%

Identifying fallers among ophthalmic patients using classification tree methodology

et al. 2017

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PurposeTo develop and validate a tool aiming to support ophthalmologists in identifying, during routine ophthalmologic visits, patients at higher risk of falling in the following year.MethodsA group of 141 subjects (age: 73.2 ± 11.4 years), recruited at our Eye Clinic, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. Moreover, visual disability was assessed by the Activity of Daily Vision Scale (ADVS). The subjects were followed up for 12 months in order to record prospective falls. A subject who reported at least one fall within one year from the baseline assessment was considered as faller, otherwise as non-faller. Different tree-based algorithms (i.e., C4.5, AdaBoost and Random Forests) were used to develop automatic classifiers and their performances were evaluated by the cross-validation approach.ResultsOver the follow-up, 25 falls were referred by 13 patients. The logistic regression analysis showed the following variables as significant predictors of prospective falls: pseudophakia and use of prescribed eyeglasses as protective factors, recent worsening of visual acuity as risk factor. Random Forest ranked best corrected visual acuity, number of sleeping hours and job type as the most important features. Finally, AdaBoost enabled the identification of subjects at higher risk of falling in the following 12 months with a sensitivity rate of 69.2% and a specificity rate of 76.6%.ConclusionsThe current study proposes a novel method, based on classification trees applied to self-reported factors and health information assessed by a standardized questionnaire during ophthalmological visits, to identify ophthalmic patients at higher risk of falling in the following 12 months. The findings of the current study pave the way to the validation of the proposed novel tool for fall risk screening on a larger cohort of patients with visual impairment referred to eye clinics.

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Section: Discussionmentioning

confidence: 99%

Identifying fallers among ophthalmic patients using classification tree methodology

et al. 2017

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“…The entire video game dataset was used to test the model. The features selection was two-staged: we performed the relevance analysis and the redundancy analysis as described in [8]. Training of the machine-learning models (including the algorithm parameter tuning) was performed using a 10-fold person-independent cross-validation approach.…”

Section: B Acute Mental Stress Detection In Lab Settings (Experimentmentioning

confidence: 99%

To What Extent Can We Shorten HRV Analysis in Wearable Sensing? A Case Study on Mental Stress Detection.

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Abstract-Mental stress is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. In addition, it reduces performances, on the work place and in daily life. The diffusion of wearable sensors (embedded in smart-watches, phones, etc.) has opened up the potential to assess mental stress detection through ultra-short term Heart Rate Variability (HRV) analysis (i.e., less than 5 min). Although informative analyses of features coming from short HRV (i.e., 5 min) have already been performed, the reliability of ultra-short HRV remains unclear. This study aims to tackle this gap by departing from a systematic review of the existing literature and investigating, in healthy subjects, the associations between acute mental stress and short/ultra-short term HRV features in time, frequency, and non-linear domains. Building on these findings, three experiments were carried out to empirically assess the usefulness of HRV for mental stress detection using ultra-short term analysis and wearable devices. Experiment 1 detected mental stress in a real life situation by exploring to which extent HRV excerpts can be shortened without losing their ability to detect mental stress. This allowed us to advance a method to explore to what extent ultra-short HRV features can be considered as good surrogates of 5 min HRV features. Experiment 2 and 3 sought to develop automatic classifiers to detect mental stress through 2 min HRV excerpts, by using a Stroop Color Word Test (CWT) and a highly paced video game, which are two common laboratory-based stressors.Results from experiment 1 demonstrated that 7 ultrashort HRV features can be considered as good surrogates of short HRV features in detecting mental stress in real life. By leveraging these 7 features, experiment 2 and 3 offered an automatic classifier detecting mental stress with ultrashort features (2min), achieving sensitivity, specificity and accuracy rate above 60%.

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“…Over the last decades heart rate variability (HRV) analysis has become a popular method for the assessment of the autonomous nervous system in diverse fields of research. Depressed HRV has been proven to be an independent predictor for several clinical outcomes, such as death in chronic congestive heart failure [1], myocardial infarction, mental stress detection [2], risk of accidental falls in hypertensive patients [3] and many other. HRV analysis attempts to assess cardiac autonomic regulation by quantifying the sinus rhythm variability [4], which is usually derived from the consecutive QRS intervals (RR) of the electrocardiogram (ECG).…”

Section: Introductionmentioning

confidence: 99%