Ischemia classification via ECG using MLP neural networks

Peláez, José Ignacio; Doña, Jesús M.; Fornari, Javier; Serra, Giuseppe

doi:10.1080/18756891.2014.889498

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…To optimize the hyper-parameters of the MLP model, we use KerasTuner. 22 We evaluated different hidden layers and hidden units on each layer in the range of 1–5 and 3–30, 23,24 respectively. The final model, as shown in Figure 4, contains three dense layers (fully connected) followed by ReLU activation and dropout regularization.…”

Section: Methodsmentioning

confidence: 99%

“…The use of SPs for this study was strictly for obtaining ECG signal data and testing site detection's accuracy and real-time capabilities. Among the 19 males and 22 females tested, the average age of SPs was 42 (in the range of 27-61), and the average body mass index was 26 (in the range of [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. A Welch Allyn Meditron stethoscope apparatus was used to collect 10 s of heart sounds from each of the four auscultation sites.…”

Section: Subjects Equipment and Data Acquisitionmentioning

confidence: 99%

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Development of deep-learning models for a hybrid simulation of auscultation training on standard patients using an ECG-based virtual pathology stethoscope

et al. 2023

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Cardiac auscultation (CA), the act of listening to the heart’s sound, is a critical skill that provides valuable information for identifying serious heart diseases. Proficiency in cardiac auscultation requires repeated stethoscope practice and experience in identifying abnormal or irregular cardiac rhythms. However, nowadays, most hospital admissions are short and intensely focused, with fewer opportunities for medical trainees to learn and practice bedside examination skills. It is common practice in many institutions to incorporate standardized patients (SPs) into CA training because these actors are able to represent the patient and convey the symptoms. However, SPs are typically healthy individuals, limiting the kinds of abnormalities that students can hear. In this work, we develop a novel real-time simulation-based method for virtual pathology stethoscope (VPS) detection. The VPS system uses augmented reality (AR) to teach medical students how to perform cardiac examinations by listening to abnormal heart sounds in SPs who are otherwise healthy. A digital stethoscope with two electrodes on the chest piece collects electrocardiogram (ECG) signal data sets from SPs at the four primary auscultation sites. Next, different deep-learning methods are evaluated for classifying the location of the stethoscope by taking advantage of subtle differences in the ECG signals. This study would significantly extend the simulation capabilities of SPs by allowing medical students and trainees to perform realistic CA and hear CA in a clinical environment.

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Section: Methodsmentioning

confidence: 99%

Section: Subjects Equipment and Data Acquisitionmentioning

confidence: 99%

Development of deep-learning models for a hybrid simulation of auscultation training on standard patients using an ECG-based virtual pathology stethoscope

et al. 2023

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“…Moreover, they can detect the complex and high nonlinearity relations existing in the datasets. 33,53 They have been widely used in medical applications. 25,28,54 KNN is a simple, straightforward, and highly efficient classifier even with noisy data.…”

Section: Materials and Methodologymentioning

confidence: 99%

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

Attallah

Karthikesalingam

Holt

et al. 2017

Proc Inst Mech Eng H

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Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.

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“…MLPs can be trained by back-propagation. The main use for MLPs with 3-10 neuron layers involves simple classification, such as handwritten pattern recognition [150][151][152]. CNNs are designed to process data that come in the form of multiple arrays, for example a color image composed of three 2D images containing pixel intensities in three color channels.…”

Section: Neural Networkmentioning

confidence: 99%

Memristor devices for neural networks

Jeong

Shi

2018

J. Phys. D: Appl. Phys.

113

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Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfactory solutions for neural networks. Recently, several types of memristor devices have become popular alternatives because of their outstanding characteristics such as scalability, high performance, and non-volatility. To understand the characteristics of memristors, a comparison among memristors has been made, considering both maturity and performance. Magneto-resistance random access memory, phase-change random access memory, and resistive random access memory among the proposed memristors are good candidates as synaptic devices for weight storage and matrixvector multiplication required in artificial neural networks (ANNs). Moreover, these devices play key roles as synaptic devices in research for bio-plausible spiking neural networks (SNNs) because their distinctive switching properties are well matched for emulating synaptic and neuron functions of biological neural networks. In this paper we review motivation, advantage, technology, and applications of memristor devices for neural networks from practical approaches of ANNs to futuristic research of SNNs, considering the current status of memristor technology.

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Ischemia classification via ECG using MLP neural networks

Cited by 12 publications

References 21 publications

Development of deep-learning models for a hybrid simulation of auscultation training on standard patients using an ECG-based virtual pathology stethoscope

Development of deep-learning models for a hybrid simulation of auscultation training on standard patients using an ECG-based virtual pathology stethoscope

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

Memristor devices for neural networks

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