Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

Ponsiglione, Alfonso Maria; Amato, Francesco; Romano, Maria

doi:10.3390/bioengineering9010008

Cited by 34 publications

(8 citation statements)

References 74 publications

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“…The use of ML to predict total in-hospital LOS for medical patients has been assessed by several studies with different methodologies and results [ 36 ]. Examining the variables that influence LOS, Rodd et al [ 26 ] show that diabetes mellitus is also a predictive factor in their study. In contrast to our study, Hernandez et al [ 22 ] show how female sex is associated with prolonged LOS, but both highlight how there is no relationship with factors such as myocardial infarction or atrial fibrillation.…”

Section: Discussionmentioning

confidence: 98%

“…Identifying the factors that cause prolonged LOS is essential to improve the patient’s condition and reduce healthcare costs [ 22 , 24 , 25 ]. Several studies report advanced processing of cardiac data for diagnostic purposes [ 26 , 27 , 28 , 29 , 30 , 31 ] or to support the monitoring process [ 32 , 33 ]. The aim of the present work is to determine the factors associated with prolonged hospitalization following endarterectomy, using the clinical and organizational data collected at the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

2022

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Background: In recent years, the length of hospital stay (LOS) following endarterectomy has decreased significantly from 4 days to 1 day. LOS is influenced by several common complications and factors that can adversely affect the patient’s health and may vary from one healthcare facility to another. The aim of this work is to develop a forecasting model of the LOS value to investigate the main factors affecting LOS in order to save healthcare cost and improve management. Methods: We used different regression and machine learning models to predict the LOS value based on the clinical and organizational data of patients undergoing endarterectomy. Data were obtained from the discharge forms of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy). R2 goodness of fit and the results in terms of accuracy, precision, recall and F1-score were used to compare the performance of various algorithms. Results: Before implementing the models, the preliminary correlation study showed that LOS was more dependent on the type of endarterectomy performed. Among the regression algorithms, the best was the multiple linear regression model with an R2 value of 0.854, while among the classification algorithms for LOS divided into classes, the best was decision tree, with an accuracy of 80%. The best performance was obtained in the third class, which identifies patients with prolonged LOS, with a precision of 95%. Among the independent variables, the most influential on LOS was type of endarterectomy, followed by diabetes and kidney disorders. Conclusion: The resulting forecast model demonstrates its effectiveness in predicting the value of LOS that could be used to improve the endarterectomy surgery planning.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

2022

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“…In [ 35 , 36 , 37 ], the neural network, deep learning models and transfer learning-based models were discussed for different physiological signals and physiological signal-based datasets.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Physiological Signal-Based Emotion Recognition

Ahmad

Khan

2022

Bioengineering

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Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

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“…Many scientific studies from various fields of study have recently addressed the problem of improving the quality of healthcare services through the use of managerial, statistical, and modeling tools to address issues such as prolonged hospital stays, increased waiting times, appointment scheduling, and other issues [7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Overcrowding analysis in emergency department through indexes: a single center study

et al. 2022

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Introduction Overcrowding in the Emergency Department (ED) is one of the major issues that must be addressed in order to improve the services provided in emergency circumstances and to optimize their quality. As a result, in order to help the patients and professionals engaged, hospital organizations must implement remedial and preventative measures. Overcrowding has a number of consequences, including inadequate treatment and longer hospital stays; as a result, mortality and the average duration of stay in critical care units both rise. In the literature, a number of indicators have been used to measure ED congestion. EDWIN, NEDOCS and READI scales are considered the most efficient ones, each of which is based on different parameters regarding the patient management in the ED. Methods In this work, EDWIN Index and NEDOCS Index have been calculated every hour for a month period from February 9th to March 9th, 2020 and for a month period from March 10th to April 9th, 2020. The choice of the period is related to the date of the establishment of the lockdown in Italy due to the spread of Coronavirus; in fact on 9 March 2020 the Italian government issued the first decree regarding the urgent provisions in relation to the COVID-19 emergency. Besides, the Pearson correlation coefficient has been used to evaluate how much the EDWIN and NEDOCS indexes are linearly dependent. Results EDWIN index follows a trend consistent with the situation of the first lockdown period in Italy, defined by extreme limitations imposed by Covid-19 pandemic. The 8:00–20:00 time frame was the most congested, with peak values between 8:00 and 12:00. on the contrary, in NEDOCS index doesn’t show a trend similar to the EDWIN one, resulting less reliable. The Pearson correlation coefficient between the two scales is 0,317. Conclusion In this study, the EDWIN Index and the NEDOCS Index were compared and correlated in order to assess their efficacy, applying them to the case study of the Emergency Department of “San Giovanni di Dio e Ruggi d’Aragona” University Hospital during the Covid-19 pandemic. The EDWIN scale turned out to be the most realistic model in relation to the actual crowding of the ED subject of our study. Besides, the two scales didn’t show a significant correlation value.

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Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

Cited by 34 publications

References 74 publications

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

A Survey on Physiological Signal-Based Emotion Recognition

Overcrowding analysis in emergency department through indexes: a single center study

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