Usha Desai
N.M.A.M. Institute of Technology
36 Papers
107 Citations
Usha Desai is an academic researcher from N.M.A.M. Institute of Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 7, co-authored 15 publications. Previous affiliations of Usha Desai include Reva Institute of Technology and Management.
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Papers
Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques
Usha Desai,Roshan Joy Martis,C. Gurudas Nayak,K. Sarika,G. Seshikala +4 more
- 01 Dec 2015
TL;DR: A machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features and is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.
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Discrete Cosine Transform Features in Automated Classification of Cardiac Arrhythmia Beats
Usha Desai,Usha Desai,Roshan Joy Martis,C. Gurudas Nayak,K. Sarika,Sagar G Nayak,Ashwin Shirva,Vishwas Nayak,Shaik Mudassir +8 more
- 01 Jan 2015
TL;DR: A machine learning-based methodology proposed for automated cardiac arrhythmia detection using k-Nearest Neighbor (k-NN) classifier and the statistical significance of PCA features is verified using Analysis of Variance (ANOVA) test.
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Application of ensemble classifiers in accurate diagnosis of myocardial ischemia conditions
TL;DR: This study presents an approach for automated diagnosis of MI condition using ECG beats, which is non-invasive and cost-efficient and helps in screening of MI, predominantly in rural and remote areas.
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Analysing the power of deep learning techniques over the traditional methods using medicare utilisation and provider data
TL;DR: It was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study, which indicates a stronger case for using DLTs over the traditional predictive analytics.
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Hybrid Deep Learning Approaches for sEMG Signal-Based Lower Limb Activity Recognition
TL;DR: In this article , a hybrid deep learning model was proposed for lower limb activity recognition using wearable sensors including accelerometers, gyroscopes, and surface electromyography (EMG) sensors.
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