A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection

Liang, Xiangpeng; Li, Haobo; Vučković, Aleksandra; Mercer, John R.; Heidari, Hadi

doi:10.1109/tbme.2021.3129306

Cited by 6 publications

(1 citation statement)

References 42 publications

(72 reference statements)

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“…This organization allows for a clear representation of the temporal segmentation of the speech signal, providing insights into the variations and characteristics of the signal over time. Further, decomposition of stage 1 IMF component is given by: 𝑦(𝑛) = 𝐶 11 + 𝐶 12 (10) 𝐶 11 −Frequency component of IMF stage 1 and it is low compared to 𝐶 12 and 𝐶 12 has high energy than 𝐶 11 so 𝐶 12 is considered for further decomposition.…”

Section: Figure 5 Steps In Obtaining the Hht Of Pcg Signalmentioning

confidence: 99%

An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

Vinay,

Vidyasagar,

Rohith

et al. 2024

IEEE Access

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The cardiovascular system is responsible for carrying the blood along with nutrition and oxygen throughout the body. This system consists of heart, blood, and blood vessels. The experts, or doctors called as cardiologists, analyze the sounds of heart's (lub-dub) beat and flow of blood to diagnose Cardio Vascular Disease (CVD) using a traditional stethoscope and phonological cardiogram technique. Through the stethoscope, the cardiologist will listen to vibration produced by heart beat and heart beat sound and murmur sound are popularly known as phonocardiogram (PCG) signals, which are being recorded for medical diagnosis purposes using a stethoscope. The development of a technique for the automatic recognition of HVD's assists the experts in recognizing the CVD effectively in the initial stage itself from PCG signals.There are many tools available to help doctors in a clinical setting for the accurate diagnose the CVD in a less time. The main aim of this proposed work is to provide an Artificial Intelligence (AI) based PCG signal analysis for the automatic and early detection of various cardiac conditions using supervised and unsupervised Recurrent Neural Network (RNN) based Bidirectional Long Short-Term Memory (Bi-LSTM) Machine Learning (ML) algorithm. Along with this algorithm, Generative Adversarial Networks (GAN's) is considered because they can create fresh, high-quality, pseudo-real data that resembles their training set which has been demonstrated by using their two unique networks: Discriminator Network (DN) and the Generator Network (GN). The proposed method is tested using heart sound signals from the well-known, freely accessible PhysioNet and Kaggle datasets. The Experimental results are validated based accuracy, precision, F1-score, sensitivity, and specificity.

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Section: Figure 5 Steps In Obtaining the Hht Of Pcg Signalmentioning

confidence: 99%