Synthetic PPG Signal Generation to Improve Coronary Artery Disease Classification: Study With Physical Model of Cardiovascular System

Mazumder, Oishee; Banerjee, Rohan; Roy, Dilip; Bhattacharya, Sakyajit; Ghose, Avik; Sinha, Aniruddha

doi:10.1109/jbhi.2022.3147383

Cited by 16 publications

(9 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of complexity, CT [6], SVM [15], SSDP [24], and LR [27] are able to achieve better performance, while in terms of delay, UESR [5], JHCP MMP [13], SVM [15], and SVM [47] are able to showcase faster performance, thus can be used for high-speed clinical deployments. While, MI GAN [8], JHCP MMP [13], PCA NET [17], PD Res Net [21], SALL [25], CWT [33], 3D CNN [34], PGO [38], GERF [39], RL [42], DNN EWBO [44], EC [46], and THS GAN [50] showcase high scalability, thus can be used for identification of large number of diseases in clinical scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…By increasing diagnosis accuracy and assessment time, researchers produce predictive and effective dementia diagnostic exams and provide a sandbox interface for doctors and non-specialists. The JHCPMMP was purportedly employed in the binary classification research for Alzheimer's disease [13]. The study's objective was to create a classification system that could take the complexity and variety of brain networks into consideration.…”

Section: In-depth Review Of Different Models For Identification Of Hu...mentioning

confidence: 99%

“…TSA CNN [26], and RL [42] showcase high accuracy, whereas DLN [40], Fuzzy GBDT [22], PD Res Net [21], GERF [39], PC SVM [41], SSDP [24], SALL [25], THS GAN [50], ACGA [ In terms of complexity, CT [6], SVM [15], SSDP [24], and LR [27] are able to achieve higher performance. On the other hand, in terms of delay, UESR [5], JHCP MMP [13], SVM [15], and SVM [47] are able to display quicker performance, and hence may be utilized for high-speed clinical deployments. While models such as MI GAN [8, JHCP MMP [13], PCA NET [17], PD Res Net [21], SALL [25], CWT [33], 3D CNN [34], PGO [38], GERF [39], RL [42], DNN EWBO [44], EC [46], and THS GAN [50] exhibit high scalability, these models cannot be utilized for the identification of a large number of diseases in clinical settings.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

“…On the other hand, in terms of delay, UESR [5], JHCP MMP [13], SVM [15], and SVM [47] are able to display quicker performance, and hence may be utilized for high-speed clinical deployments. While models such as MI GAN [8, JHCP MMP [13], PCA NET [17], PD Res Net [21], SALL [25], CWT [33], 3D CNN [34], PGO [38], GERF [39], RL [42], DNN EWBO [44], EC [46], and THS GAN [50] exhibit high scalability, these models cannot be utilized for the identification of a large number of diseases in clinical settings. In the future, researchers will be able to improve the performance of these models by fusing them together, and they will also be able to apply hybrid bioinspired methodologies for ongoing improvements of real-time situations.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

“…[12] Healthy controls are more likely to be able to accomplish this. Since people may communicate with one another, they can also express their feelings and sentiments in addition to their views and facts [13]. Because Parkinsonism has been shown to have negative effects on the vocal tract (dysarthria) [10], in addition to having an impact on cognitive function [14], this method is hampered in this condition.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Review Of Models & Techniques For Identification Of Human Health Diseases

2022

pnr

View full text Add to dashboard Cite

Human health diseases are caused due to genetic disorders, environmental conditions, presence of Alpha synuclein bodies, etc. Identification of this disease can be done via estimation of body tremors, slowed movements, fever, change in corpuscles, rigid muscles, impaired posture & balance, pain analysis, loss of automatic movements, speech changes, and writing changes. To estimate these effects, researchers have proposed a wide variety of machine learning techniques that can analyze speech signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, handwriting variations, gait identification & classification, etc. These models are highly variant in terms of their internal operating characteristics, which makes it ambiguous for clinical designers to identify optimal models for their deployments. Furthermore, these models also showcase highly variant performance levels as per their input feature sets. Thus, it is difficult to identify optimal models for a given set of input parameters. To overcome these issues, this paper initially reviews a wide variety of Human Health disease identification models in terms of their contextual nuances, operating advantages, functional limitations, quantitative characteristics, and deployment-specific future scopes. This will assist readers to identify functional models that can be applied for their operation-specific use cases. After referring this comparison, readers will be presented with a comparative analysis of these models in terms of their detection accuracy, classification delay, input datasets, computational complexity, and scalability levels. Based on this comparison, readers will be able to identify optimally performing models as per their qualitative characteristics.

show abstract