EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification

Background: Deep learning has significantly advanced ECG arrhythmia classification, enabling high accuracy in detecting various cardiac conditions. The use of single-lead ECG systems is crucial for portable devices, as they offer convenience and accessibility for continuous monitoring in diverse settings. However, the interpretability and reliability of deep learning models in clinical applications poses challenges due to their black-box nature. Methods: To address these challenges, we propose EXGnet, a single-lead, trustworthy ECG arrhythmia classification network that integrates multiresolution feature extraction with Explainable Artificial Intelligence (XAI) guidance and train only quantitative features. Results: Trained on two public datasets, including Chapman and Ningbo, EXGnet demonstrates superior performance through key metrics such as Accuracy, F1-score, Sensitivity, and Specificity. The proposed method achieved average five fold accuracy of 98.762%, and 96.932% and average F1-score of 97.910%, and 95.527% on the Chapman and Ningbo datasets, respectively. Conclusions: By employing XAI techniques, specifically Grad-CAM, the model provides visual insights into the relevant ECG segments it analyzes, thereby enhancing clinician trust in its predictions. While quantitative features further improve classification performance, they are not required during testing, making the model suitable for real-world applications. Overall, EXGnet not only achieves better classification accuracy but also addresses the critical need for interpretability in deep learning, facilitating broader adoption in portable ECG monitoring.

An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning

An independent, automated method of decoding and transcribing oral speech is known as automatic speech recognition (ASR). A typical ASR system extracts feature from audio recordings or streams and run one or more algorithms to map the features to corresponding texts. Numerous of research has been done in the field of speech signal processing in recent years. When given adequate resources, both conventional ASR and emerging end-to-end (E2E) speech recognition have produced promising results. However, for low-resource languages like Bengali, the current state of ASR lags behind, although the low resource state does not reflect upon the fact that this language is spoken by over 500 million people all over the world. Despite its popularity, there aren't many diverse open-source datasets available, which makes it difficult to conduct research on Bengali speech recognition systems. This paper is a part of the competition named `BUET CSE Fest DL Sprint'. The purpose of this paper is to improve the speech recognition performance of the Bengali language by adopting speech recognition technology on the E2E structure based on the transfer learning framework. The proposed method effectively models the Bengali language and achieves 3.819 score in `Levenshtein Mean Distance' on the test dataset of 7747 samples, when only 1000 samples of train dataset were used to train.