Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks

Beritelli, Francesco; Capizzi, Giacomo; Sciuto, Grazia Lo; Napoli, Christian; Scaglione, Francesco

doi:10.1007/s13534-017-0046-z

Cited by 49 publications

(25 citation statements)

References 37 publications

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“…HS signals are closely related to cardiovascular diseases and have been widely studied, while objects of these researches were different. For example, the identification and classification of HS components [27,28], classification of normal and other abnormal HS [29][30][31], differentiating the murmurs between physiological and pathological [32,33]. However, the previously published papers about classification of HFrEF, HFpEF and normal were few and incomplete.…”

Section: The Comparison Of the Relevant Studiesmentioning

confidence: 99%

Gated recurrent unit-based heart sound analysis for heart failure screening

2020

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Background Heart failure (HF) has attracted widespread attentions due to the high morbidity and mortality, especially with the aging of population. The risk indicators of HF are numerous and complicated. Beside the well-known factors, like obesity, smoking and alcohol abuse, some cardiovascular diseases such as hypertension, earlier heart attack and myocardial infarction have also been verified as the precursors for HF developing in clinical practice [1, 2]. Therefore, keeping a healthy lifestyle and paying attention to the early screening of HF play an important role in the preventive and timely treatment. HF can be divided into two categories-HF with reduced ejection fraction (HFrEF) and HF preserved ejection fraction (HFpEF), and the following conditions are often used to diagnose of HFrEF and HFpEF in clinical [3]: (1) typical symptoms and/or signs of HF; (2) the indicator of left ventricular ejection fraction; (3) the levels of natriuretic peptides;

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Section: The Comparison Of the Relevant Studiesmentioning

confidence: 99%

Gated recurrent unit-based heart sound analysis for heart failure screening

2020

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“…1 greenBag first prototype and Fig. 2 greenBag final prototype The greenBag bin is based on an embedded architecture similar to [15][16][17], since it allows to enter in deep sleep states and to save the device battery.…”

Section: A the Greenbag Binmentioning

confidence: 99%

The greenBag, the New Solution in Waste Separation

Sperandio¹,

Luca

Catini

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The greenBag is an innovative households' Used Cooking Oil (UCO) disposal solution aiming to allow the collection of this feedstock interesting European but also Chinese and US market. The possibility to collect UCO produced by households represents the possibilities to increase the amount of biodiesel, respecting the ILUC directive, of the 60%. So far, there are no concrete solutions able to properly respond to this need, due to their huge prizes and due to the low performances, they provide. greenBag is a solution coming from an intensive research activity completed by an infield validation. It has been designed to reply to all the requirements coming from families and from the waste management companies, the two actors involved in UCO disposal. Indeed, two different types of users in general, and in particular the UCO collection, characterize waste collection solutions: families-that want simple solutions, easy to use and able to reward them; waste collection companies-that want cheap solutions, able to trace waste disposals and able to assure them a high quality of wastes. The currently available market solutions are all focused on waste companies and the current innovative ideas designed by Universities and Startups focus their attention only on one of the two involved actors. greenBag is currently the sole solution that aims to satisfy both actors, exploiting the disposal traceability not only to know how many waste people produces but also to quantify their efforts and making them aware about the overall disposal process.

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“…Recently, deep learning methods have been applied to healthcare data as replacements for conventional feature extraction methods, and have yielded better performances [20,21]. Deep learning automatically detects specific patterns in physiological signals measured using EEG, ECG, and EMG [22].…”

Section: Introductionmentioning

confidence: 99%

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

et al. 2020

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A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-network (DQN) algorithm was designed, using conventional feature engineering and deep convolutional neural network (CNN) methods, to extract the optimal features. The DQN yielded the optimal features: two CNN features from ECG and two conventional features from EEG. The ECG features were more significant for tracking the transitions within the alertness continuum with the DQN. The classification was performed with a small number of features, and the results were similar to those from using all of the features. This suggests that the DQN could be applied to investigating biomarkers for physiological responses and optimizing the classification system to reduce the input resources.

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Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks

Cited by 49 publications

References 37 publications

Gated recurrent unit-based heart sound analysis for heart failure screening

Gated recurrent unit-based heart sound analysis for heart failure screening

The greenBag, the New Solution in Waste Separation

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

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