Browsing by Author "Jat, T."

Now showing 1 - 3 of 3

COVID-19 Social Distancing Detection andÂ Email Violation Mechanisms
(Springer Science and Business Media Deutschland GmbH, 2023) Bhojak, D.; Jat, T.; Naik, D.
The technique of flattening the curve for coronavirus-infected cases is challenging in addressing the worldwide ongoing rampant novel COVID-19 pandemic crisis unless citizens take steps to halt the virusâ€™s spread. Maintaining a safe space between individuals around us in public is one of the most important behaviors. Deep learning algorithms have been used in the proposed work to mitigate the spreading of the coronavirus utilizing social distance detection. This proposed work analyzes a pre-recorded video feed of walking pedestrians to alert people to maintain a safe distance. The goal is achieved using YOLOv3 and YOLOv4 for object detection in the video frame used as input. Furthermore, an email-based alert mechanism is also implemented if the number of violations exceeds the defined limit. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Detection of Heart Abnormality with Stethoscope Sounds
(Springer, 2025) Jat, T.; Bhat, P.; Patil, N.
Cardiac rhythm assessment is a critical step in the early diagnosis of cardiac arrhythmia, which has been identified as a kind of cardiovascular disease (CVD) that affects millions of individuals worldwide. Although electrocardiography is the right test to confirm cardiovascular diseases, due to the time and cost involved in the test, an alternate solution like heart abnormality detection using a stethoscope test is needed. A stethoscope test is a facility that is relatively easily available in rural parts of the country and can aid in the early diagnosis of heart abnormalities. The central aim of this work is to build a deep learning model for classifying heartbeat sounds which are captured with the help of iStethoscope Pro iPhone app or using a digital stethoscope. The proposed methodology is implemented using the Heart Sounds Classification Challenge dataset from PASCAL and the 2016 Physionet Challenge dataset. To extract features from the recorded heart sounds, we employ Mel spectrograms, Mel-frequency cepstral coefficients (MFCC), and Chroma short-time Fourier transform (STFT). A key novelty of our approach lies in the use of the stacked Bidirectional and Unidirectional Long Short Term Memory (SBU-LSTM) and deep BiLSTM architectures, which, combined with the three feature types (Mel spectrograms, Chroma STFT, and MFCC), enhances model performance for the classification task. Additionally, we introduce the use of Mel spectrograms and Chroma STFT with the 2D Convolutional Neural Network (CNN) architecture, which, as far as we know, has not been investigated in prior research. Experimental results show that the best accuracy achieved is 72% for PASCAL’s Dataset-A, 66% for Dataset-B, 83% for Dataset A + B, and 89% for Physionet’s Dataset-C. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Detection of heart arrhythmia with electrocardiography
(Springer, 2024) Jat, T.; Patil, N.; Bhat, P.
Early detection of cardiac arrhythmia, a prevalent form of cardiovascular disease (CVD) impacting millions globally, is heavily reliant on the accurate analysis of heartbeats. Physicians often recommend that patients wear Holter monitors for 24 h or longer to observe concerning cardiac issues, resulting in the collection of substantial amounts of electrocardiogram (ECG) data. Consequently, there is a need to automate the process of interpreting ECGs to detect cardiac abnormalities efficiently. Current state-of-the-art studies rely on handcrafted feature extraction, which may not effectively capture the intricate temporal relationships inherent in ECG signal data. To address this limitation and facilitate the diagnosis of cardiac diseases, this study proposes a technique that converts electrocardiogram signals into images, subsequently training a deep learning model on the generated images. Image encoding techniques such as Gramian Angular Difference Field (GADF), Gramian Angular Summation Field (GASF) and Markov Transition Field (MTF) are employed to translate the ECG signals into images. The highest accuracy, 96.71%, was achieved by training the Convolutional Neural Network (CNN) model using the concatenation of these three image encoding techniques. The proposed approach is assessed using ECG recordings from the MIT-BIH Arrhythmia Database to detect heart arrhythmia, demonstrating the efficacy of the approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.