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Fathima AJ, Fasla MMN. A comprehensive review on heart disease prognostication using different artificial intelligence algorithms. Comput Methods Biomech Biomed Engin 2024; 27:1357-1374. [PMID: 38424704 DOI: 10.1080/10255842.2024.2319706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis.
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Affiliation(s)
- A Jainul Fathima
- Assistant Professor, IT Francis Xavier Engineering College, Tirunelveli - 627003, India
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Issa MF, Yousry A, Tuboly G, Juhasz Z, AbuEl-Atta AH, Selim MM. Heartbeat classification based on single lead-II ECG using deep learning. Heliyon 2023; 9:e17974. [PMID: 37539141 PMCID: PMC10395346 DOI: 10.1016/j.heliyon.2023.e17974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 08/05/2023] Open
Abstract
The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.
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Affiliation(s)
- Mohamed F. Issa
- Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13511, Egypt
- Department of Electrical Engineering and Information Systems, University of Pannonia, 8200, Veszprém, Hungary
| | - Ahmed Yousry
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13511, Egypt
| | - Gergely Tuboly
- Department of Electrical Engineering and Information Systems, University of Pannonia, 8200, Veszprém, Hungary
| | - Zoltan Juhasz
- Department of Electrical Engineering and Information Systems, University of Pannonia, 8200, Veszprém, Hungary
| | - Ahmed H. AbuEl-Atta
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13511, Egypt
| | - Mazen M. Selim
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13511, Egypt
- Department of Mechatronics, Delta University for Science and Technology, Gamasa, 11152, Egypt
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3
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Automatic diagnosis of cardiovascular diseases using wavelet feature extraction and convolutional capsule network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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4
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Sun L, Wu J, Xu Y, Zhang Y. A federated learning and blockchain framework for physiological signal classification based on continual learning. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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5
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Cardiac arrhythmia classification by time–frequency features inputted to the designed convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Papageorgiou VE, Zegkos T, Efthimiadis G, Tsaklidis G. Analysis of digitalized ECG signals based on artificial intelligence and spectral analysis methods specialized in ARVC. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3644. [PMID: 36053812 DOI: 10.1002/cnm.3644] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/13/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we first outline the digitalization process of paper-based ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to ECG waveform, producing undesirable noise. Next, we propose the utilization of a low-complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in contrast with other arrhythmogenic abnormalities. Finally, by performing spectral analysis we investigate significant differentiations in the field of frequencies between normal ECGs and ECGs corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies where we encounter statistically significant differentiations, the normal ECGs are characterized by greater normalized amplitudes compared to the abnormal ones. The overall research carried out in this article highlights the importance of integrating mathematical methods into the examination and effective diagnosis of various diseases, aiming to a substantial contribution to their successful treatment.
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Affiliation(s)
| | - Thomas Zegkos
- 1st Cardiology Department, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Efthimiadis
- 1st Cardiology Department, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach. Sci Rep 2022; 12:18054. [PMID: 36302876 PMCID: PMC9613643 DOI: 10.1038/s41598-022-22939-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 10/21/2022] [Indexed: 01/24/2023] Open
Abstract
Early ischemic lesion on non-contrast computed tomogram (NCCT) in acute stroke can be subtle and need confirmation with magnetic resonance (MR) image for treatment decision-making. We retrospectively included the NCCT slices of 129 normal subjects and 546 ischemic stroke patients (onset < 12 h) with corresponding MR slices as reference standard from a prospective registry of Chang Gung Research Databank. In model selection, NCCT slices were preprocessed and fed into five different pre-trained convolutional neural network (CNN) models including Visual Geometry Group 16 (VGG16), Residual Networks 50, Inception-ResNet-v2, Inception-v3, and Inception-v4. In model derivation, the customized-VGG16 model could achieve an accuracy of 0.83, sensitivity 0.85, F-score 0.80, specificity 0.82, and AP 0.82 after using a tenfold cross-validation method, outperforming the pre-trained VGG16 model. In model evaluation, the customized-VGG16 model could correctly identify 53 in 58 subjects (91.37%) including 29 ischemic stroke patients and 24 normal subjects and reached the sensitivity of 86.95% in identifying ischemic NCCT slices (200/230), irrespective of supratentorial or infratentorial lesions. The customized-VGG16 CNN model can successfully identify the presence of early ischemic lesions on NCCT slices using the concept of automatic feature learning. Further study will be proceeded to detect the location of ischemic lesion.
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Rashed BM, Popescu N. Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:7065. [PMID: 36146414 PMCID: PMC9501859 DOI: 10.3390/s22187065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017-2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.
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Karthan M, Martin R, Holl F, Swoboda W, Kestler HA, Pryss R, Schobel J. Enhancing mHealth data collection applications with sensing capabilities. Front Public Health 2022; 10:926234. [PMID: 36187627 PMCID: PMC9521646 DOI: 10.3389/fpubh.2022.926234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/11/2022] [Indexed: 01/24/2023] Open
Abstract
Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.
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Affiliation(s)
- Maximilian Karthan
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany,Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany,*Correspondence: Maximilian Karthan
| | - Robin Martin
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Felix Holl
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany,Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Walter Swoboda
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Johannes Schobel
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
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Agrawal A, Chauhan A, Shetty MK, P GM, Gupta MD, Gupta A. ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Comput Biol Med 2022; 146:105540. [PMID: 35533456 PMCID: PMC9055384 DOI: 10.1016/j.compbiomed.2022.105540] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/26/2022] [Accepted: 04/15/2022] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Studies showed that many COVID-19 survivors develop sub-clinical to clinical heart damage, even if subjects did not have underlying heart disease before COVID. Since Electrocardiogram (ECG) is a reliable technique for cardiovascular disease diagnosis, this study analyzes the 12-lead ECG recordings of healthy and post-COVID (COVID-recovered) subjects to ascertain ECG changes after suffering from COVID-19. METHOD We propose a shallow 1-D convolutional neural network (CNN) deep learning architecture, namely ECG-iCOVIDNet, to distinguish ECG data of post-COVID subjects and healthy subjects. Further, we employed ShAP technique to interpret ECG segments that are highlighted by the CNN model for the classification of ECG recordings into healthy and post-COVID subjects. RESULTS ECG data of 427 healthy and 105 post-COVID subjects were analyzed. Results show that the proposed ECG-iCOVIDNet model could classify the ECG recordings of healthy and post-COVID subjects better than the state-of-the-art deep learning models. The proposed model yields an F1-score of 100%. CONCLUSION So far, we have not come across any other study with an in-depth ECG signal analysis of the COVID-recovered subjects. In this study, it is shown that the shallow ECG-iCOVIDNet CNN model performed good for distinguishing ECG signals of COVID-recovered subjects from those of healthy subjects. In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed CNN model. Successful deployment of such systems can help the doctors identify the changes in the ECG of the post-COVID subjects on time that can save many lives.
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Affiliation(s)
| | | | | | - Girish M. P
- Department of Cardiology, GIPMER, Delhi, India
| | | | - Anubha Gupta
- SBILab, Department of ECE, IIIT-Delhi, Delhi, India,Corresponding author
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Byun SJ, Kim DG, Park KD, Choi YJ, Kumar P, Ali I, Kim DG, Yoo JM, Huh HK, Jung YJ, Kim SK, Pu YG, Lee KY. A Low-Power Analog Processor-in-Memory-Based Convolutional Neural Network for Biosensor Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:4555. [PMID: 35746337 PMCID: PMC9229394 DOI: 10.3390/s22124555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/06/2022] [Accepted: 06/14/2022] [Indexed: 01/02/2023]
Abstract
This paper presents an on-chip implementation of an analog processor-in-memory (PIM)-based convolutional neural network (CNN) in a biosensor. The operator was designed with low power to implement CNN as an on-chip device on the biosensor, which consists of plates of 32 × 32 material. In this paper, 10T SRAM-based analog PIM, which performs multiple and average (MAV) operations with multiplication and accumulation (MAC), is used as a filter to implement CNN at low power. PIM proceeds with MAV operations, with feature extraction as a filter, using an analog method. To prepare the input feature, an input matrix is formed by scanning a 32 × 32 biosensor based on a digital controller operating at 32 MHz frequency. Memory reuse techniques were applied to the analog SRAM filter, which is the core of low power implementation, and in order to accurately grasp the MAC operational efficiency and classification, we modeled and trained numerous input features based on biosignal data, confirming the classification. When the learned weight data was input, 19 mW of power was consumed during analog-based MAC operation. The implementation showed an energy efficiency of 5.38 TOPS/W and was differentiated through the implementation of 8 bits of high resolution in the 180 nm CMOS process.
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Affiliation(s)
- Sung-June Byun
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
| | - Dong-Gyun Kim
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Korea;
| | - Kyung-Do Park
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Korea;
| | - Yeun-Jin Choi
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
| | - Pervesh Kumar
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
| | - Imran Ali
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
| | - Dong-Gyu Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
| | - June-Mo Yoo
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
| | - Hyung-Ki Huh
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
| | - Yeon-Jae Jung
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
| | - Seok-Kee Kim
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
| | - Young-Gun Pu
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
| | - Kang-Yoon Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (S.-J.B.); (Y.-J.C.); (P.K.); (I.A.); (D.-G.K.); (J.-M.Y.); (Y.-G.P.)
- SKAIChips, Suwon 16419, Korea; (D.-G.K.); (H.-K.H.); (Y.-J.J.); (S.-K.K.)
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Abstract
Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction.
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Mishra A, Dharahas G, Gite S, Kotecha K, Koundal D, Zaguia A, Kaur M, Lee HN. ECG Data Analysis with Denoising Approach and Customized CNNs. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22051928. [PMID: 35271073 PMCID: PMC8915034 DOI: 10.3390/s22051928] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/21/2022] [Accepted: 02/23/2022] [Indexed: 05/03/2023]
Abstract
In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart's rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.
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Affiliation(s)
- Abhinav Mishra
- Symbiosis Institute of Technology, Pune 412115, India; (A.M.); (G.D.); (S.G.)
| | | | - Shilpa Gite
- Symbiosis Institute of Technology, Pune 412115, India; (A.M.); (G.D.); (S.G.)
| | - Ketan Kotecha
- Symbiosis Centre for Applied AI, Symbiosis International (Deemed) University, Pune 412115, India;
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India;
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Manjit Kaur
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
| | - Heung-No Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Correspondence:
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Identification and Prediction of Chronic Diseases Using Machine Learning Approach. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2826127. [PMID: 35251563 PMCID: PMC8896926 DOI: 10.1155/2022/2826127] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/01/2022] [Accepted: 02/07/2022] [Indexed: 01/01/2023]
Abstract
Nowadays, humans face various diseases due to the current environmental condition and their living habits. The identification and prediction of such diseases at their earlier stages are much important, so as to prevent the extremity of it. It is difficult for doctors to manually identify the diseases accurately most of the time. The goal of this paper is to identify and predict the patients with more common chronic illnesses. This could be achieved by using a cutting-edge machine learning technique to ensure that this categorization reliably identifies persons with chronic diseases. The prediction of diseases is also a challenging task. Hence, data mining plays a critical role in disease prediction. The proposed system offers a broad disease prognosis based on patient's symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease prediction outcome. A collection of disease symptoms has been performed for the preparation of the data set along with the person's living habits, and details related to doctor consultations are taken into account in this general disease prediction. Finally, a comparative study of the proposed system with various algorithms such as Naïve Bayes, decision tree, and logistic regression has been demonstrated in this paper.
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15
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Mohebbanaaz, Padma Sai Y, Rajani Kumari L. Cognitive assistant DeepNet model for detection of cardiac arrhythmia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103221] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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16
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Classification of Arrhythmia in Heartbeat Detection Using Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:2195922. [PMID: 34712316 PMCID: PMC8548158 DOI: 10.1155/2021/2195922] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Abstract
The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning techniques on the publicly available dataset to classify arrhythmia. We have used two kinds of the dataset in our research paper. One dataset is the MIT-BIH arrhythmia database, with a sampling frequency of 125 Hz with 1,09,446 ECG beats. The classes included in this first dataset are N, S, V, F, and Q. The second database is PTB Diagnostic ECG Database. The second database has two classes. The techniques used in these two datasets are the CNN model, CNN + LSTM, and CNN + LSTM + Attention Model. 80% of the data is used for the training, and the remaining 20% is used for testing. The result achieved by using these three techniques shows the accuracy of 99.12% for the CNN model, 99.3% for CNN + LSTM, and 99.29% for CNN + LSTM + Attention Model.
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Haleem MS, Castaldo R, Pagliara SM, Petretta M, Salvatore M, Franzese M, Pecchia L. Time adaptive ECG driven cardiovascular disease detector. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Zhou H, Kan C. Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things. SENSORS (BASEL, SWITZERLAND) 2021; 21:4173. [PMID: 34204575 PMCID: PMC8234952 DOI: 10.3390/s21124173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/08/2021] [Accepted: 06/11/2021] [Indexed: 12/02/2022]
Abstract
Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling T2 chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the T2 control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.
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Affiliation(s)
| | - Chen Kan
- Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA;
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Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. SENSORS (BASEL, SWITZERLAND) 2021; 21:2852. [PMID: 33919583 PMCID: PMC8074091 DOI: 10.3390/s21082852] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/08/2021] [Accepted: 04/16/2021] [Indexed: 12/18/2022]
Abstract
Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.
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Affiliation(s)
- Parvathaneni Naga Srinivasu
- Department of Computer Science and Engineering, Gitam Institute of Technology, GITAM Deemed to be University, Rushikonda, Visakhapatnam 530045, India;
| | | | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea;
| | - Akash Kumar Bhoi
- Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, India;
| | - Wonjoon Kim
- Division of Future Convergence (HCI Science Major), Dongduk Women’s University, Seoul 02748, Korea
| | - James Jin Kang
- School of Science, Edith Cowan University, Joondalup 6027, Australia
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Nannavecchia A, Girardi F, Fina PR, Scalera M, Dimauro G. Personal Heart Health Monitoring Based on 1D Convolutional Neural Network. J Imaging 2021; 7:26. [PMID: 34460625 PMCID: PMC8321282 DOI: 10.3390/jimaging7020026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 12/05/2022] Open
Abstract
The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.
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Affiliation(s)
- Antonella Nannavecchia
- Department of Management, Finance and Technology, University LUM Jean Monnet, 70010 Casamassima, Italy;
| | | | - Pio Raffaele Fina
- Department of Computer Science, University of Torino, 10124 Torino, Italy;
| | - Michele Scalera
- Department of Computer Science, University of Bari, 70125 Bari, Italy;
| | - Giovanni Dimauro
- Department of Computer Science, University of Bari, 70125 Bari, Italy;
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A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning. INFORMATION 2020. [DOI: 10.3390/info11120556] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.
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