Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network

Sun

et al. 2021

Entropy

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

Section: Cnn Methods For Heart Sounds Classificationmentioning

confidence: 99%

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Sun

et al. 2021

Entropy

“…Furthermore, in order to increase the probability of a particular class, the model correspondingly decreases the probability of the other class, which is not the case if the sigmoid is used. The softmax outperforms the sigmoid and other activation functions when it is applied on wide range of benchmark datasets in different domains [27]- [30]. This activation function assign a probability distribution for each class where their summation is equal to one.…”

Section: The Proposed Modelmentioning

confidence: 99%

Replay Attack Detection in Smart Cities Using Deep Learning

et al. 2020

Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research. INDEX TERMS Smart cities, intrusion detection, replay attack, deep learning, convolutional neural networks.

Computational Intelligence

“…In Reference 28, a system featuring evolving fuzzy neural network is presented to detect cardiac murmurs at an accuracy of 90.75%. Detection of heart murmurs from unsegmented PCG using Savitzky–Golay filter, and feed‐forward neural network achieved a mean accuracy of 0.8574 29 …”

Section: Introductionmentioning

confidence: 99%

A novel embedded system design for the detection and classification of cardiac disorders

Riaz

Aziz

Khan

et al. 2021

Phonocardiogram (PCG) signals hold significant prognostic and diagnostic information about cardiac health. Numerous PCG or heart sound based automated detection algorithms were previously proposed to assist the disease diagnosis process. Most of the previous studies only focused on algorithmic development. This study presents an intelligent, portable, and low‐cost embedded system for the classification of cardiac disorders associated with heart murmurs. Different stages corresponding to the developed embedded system implementation are summarized as follows: The first stage consists of the acquisition of PCG signals of both normal and patients from various hospitals with a customized and low‐cost stethoscope. The second stage describes the preprocessing, localization of S1 and S2 heart sounds, and the extraction of systole and diastole from a heart signal with an empirical mode decomposition integrated with the self‐developed algorithm. In the third stage, discriminant features are extracted to represent various cardiac classes of PCG signals in a compact manner. In the final stage of the algorithm, the k‐nearest neighbors classifier is trained and tested to distinguish between normal and four cardiac disorders. The proposed algorithm achieved 94% mean accuracy through comprehensive experimentation. The cardiac disorders classification algorithm is implemented on a RP‐based embedded system. Software application with an interactive graphical interface is also designed to assist users. The developed intelligent system is portable, low‐cost, and it enables regular patient‐monitoring. The proposed system has the potential to be employed at remote locations where the availability of doctors remains challenging.