Artificial neural network-based cardiovascular disease prediction using spectral features

Khan, Misha Urooj; Samer, Sana; Alshehri, Mohammad Dahman; Baloch, Naveed Khan; Khan, Hareem; Hussain, Fawad; Kim, Sung Won; Zikria, Yousaf Bin

doi:10.1016/j.compeleceng.2022.108094

Cited by 29 publications

(11 citation statements)

References 14 publications

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“…A deep learning model is usually a three-or more-layered neural network and is a type of AI [31,32]. These neural networks attempt, to mimic the functioning of the human brain by "learning" from vast amounts of data as done in [33,34].…”

Section: Deep Learning Based Fall Detectionmentioning

confidence: 99%

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Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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Section: Deep Learning Based Fall Detectionmentioning

confidence: 99%

“…In [30], Deep-Learning FD is explained and researched with the aid of two approaches, i.e., YOLOK+3DCNN and YOLOK+2DCNN+LSTM. [31] applied FDEP to FallCNN_1, FallCNN_2, Fall_CNN_3, and TLD. [32] FDS using accelerometer-gyro sensors.…”

Section: Computer Vision Based Fall Detectionmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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“…In practice, the visual screening of the PCG signal takes time [ 5 ] and is prone to error. Still, the arbitrary PCG signal inspection and analysis required by doctors requires substantial training and expertise.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

2022

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The major cause of death worldwide is due to cardiovascular disorders (CVDs). For a proper diagnosis of CVD disease, an inexpensive solution based on phonocardiogram (PCG) signals is proposed. (1) Background: Currently, a few deep learning (DL)-based CVD systems have been developed to recognize different stages of CVD. However, the accuracy of these systems is not up-to-the-mark, and the methods require high computational power and huge training datasets. (2) Methods: To address these issues, we developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes. The continuous wavelet transform-based spectrogram (CWTS) strategy was used to extract representative features from PCG data. Following that, a new CVT-Trans architecture was created to categorize the CWTS signals into five groups. (3) Results: The dataset derived from our investigation indicated that the CVT-Trans system had an overall average accuracy ACC of 100%, SE of 99.00%, SP of 99.5%, and F1-score of 98%, based on 10-fold cross validation. (4) Conclusions: The CVD-Trans technique outperformed many state-of-the-art methods. The robustness of the constructed model was confirmed by 10-fold cross-validation. Cardiologists can use this CVT-Trans system to help patients with the diagnosis of heart valve problems.

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“…Located above the diaphragm and between the two lungs, the heart is about the size of a fist. Human heart beats around 100,000 times a day, which corresponds to between 60 and 100 beats per minute [1], [2]. The process from one beat of the heart to the following beat is called as cardiac cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Sounds coming from the heart are rhythmic and consist of certain frequency components. First heart sound consists of components in the frequency ranges 40-200 Hz and second heart sound consists of components in the 50-250 Hz frequency range [1]. On the other hand, the frequency range of the heart murmurs is around 200-600 Hz [5].…”

Section: Introductionmentioning

confidence: 99%

Spektral Özellikler ve MFCC Tabanlı Özellikleri Kullanan Klasik Makine Öğrenmesi Metotlarıyla PCG Parça Sınıflandırması

GÜNDÜZ

Talu

2022

EJOSAT

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Cardiovascular diseases are some of the most common diseases today. Congenital abnormalities, diseases caused by impaired heart rhythm, vascular occlusion, post-operation arrhythmias, heart attacks and irregularities in heart valves are some of the various cardiovascular diseases. Early recognition of them is very important for obtaining positive results in treatment. For this purpose, it is tried to diagnose and detect cardiovascular diseases by listening to the sounds coming from the heart. During the rhythmic work of the heart, the contraction and relaxation of the heart chambers and the filling and discharge of blood from the heart into the veins create the sounds that are identified with the heart. Among the characteristic sounds of the heart, there can be some sounds similar to rustling which are indicators of pathological conditions. These unexpected sounds, similar to rustling, are called heart murmurs. Phonocardiograph device is used to record these mechanical sounds via microphone. Heart sounds recordings captured by a phonocardiograph device are called phonocardiograms (PCGs). Expert physicians try to detect the heart murmurs by listening to the heart sounds and examining PCGs. Ambient noise, the squeak of the microphone, and the patient's breathing sounds are the factors that make this task more difficult and challenging. Computer-aided systems supported with machine learning, signal processing and artificial intelligence algorithms offer solutions to help physicians in this regard. In this study, detection of heart murmur from PCG frames was examined. PCG frames of equal length, obtained by fragmenting the PCG recordings into 1-second-long frames, were classified by widely used machine learning methods namely C4.5 decision tree, Naive Bayes, Support Vector Machines and k-nearest neighbor. To train those classifiers we used spectral features of PCG signals, averages of MFCC values and some refined features obtained from a deep learning model which was inputted MFCC values. At the end of this manuscript the accuracies of those machine learning methods were compared.

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Artificial neural network-based cardiovascular disease prediction using spectral features

Cited by 29 publications

References 14 publications

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

Spektral Özellikler ve MFCC Tabanlı Özellikleri Kullanan Klasik Makine Öğrenmesi Metotlarıyla PCG Parça Sınıflandırması

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