Developing a Chemiresistive Gas Sensor Array for Simultaneous Detection of Ammonia and Carbon Dioxide Gases.

Chemiresistive polymer-based sensors are promising platforms for monitoring various gases and volatile organic compounds. While they offer appealing attributes, such as ease of fabrication, flexibility, and cost-effectiveness, most of these sensors have a nearly identical response to cross-reactive gases, such as ammonia (NH3) and carbon dioxide (CO2). Aiming to address the shortcomings of chemiresistive polymer-based sensors in selectivity and simultaneous measurements of cross-reactive gases, a chemiresistive sensor array was developed consisting of components sensitive to carbon dioxide and ammonia as well as a control segment to provide the baseline. The designed system demonstrated a wide detection range for both ammonia (ranging from 0.05 to 1000 ppm) and carbon dioxide (ranging from 103 to 106 ppm) at both room and low temperatures (e.g., 4 °C). Our results also demonstrate the ability of this sensor array for the simultaneous detection of carbon dioxide and ammonia selectively in the presence of other gases and volatile organic compounds. Finally, the array was used to monitor CO2/NH3 in real food samples to demonstrate the potential for real-world applications.

S4D-ECG: A Shallow State-of-the-Art Model for Cardiac Abnormality Classification.

PURPOSE: This study introduces an algorithm specifically designed for processing unprocessed 12-lead electrocardiogram (ECG) data, with the primary aim of detecting cardiac abnormalities. METHODS: The proposed model integrates Diagonal State Space Sequence (S4D) model into its architecture, leveraging its effectiveness in capturing dynamics within time-series data. The S4D model is designed with stacked S4D layers for processing raw input data and a simplified decoder using a dense layer for predicting abnormality types. Experimental optimization determines the optimal number of S4D layers, striking a balance between computational efficiency and predictive performance. This comprehensive approach ensures the model's suitability for real-time processing on hardware devices with limited capabilities, offering a streamlined yet effective solution for heart monitoring. RESULTS: Among the notable features of this algorithm is its strong resilience to noise, enabling the algorithm to achieve an average F1-score of 81.2% and an AUROC of 95.5% in generalization. The model underwent testing specifically on the lead II ECG signal, exhibiting consistent performance with an F1-score of 79.5% and an AUROC of 95.7%. CONCLUSION: It is characterized by the elimination of pre-processing features and the availability of a low-complexity architecture that makes it suitable for implementation on numerous computing devices because it is easily implementable. Consequently, this algorithm exhibits considerable potential for practical applications in analyzing real-world ECG data. This model can be placed on the cloud for diagnosis. The model was also tested on lead II of the ECG alone and has demonstrated promising results, supporting its potential for on-device application.

Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware.

This study introduces two models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), designed for abnormality identification using electrocardiogram (ECG) data. Trained on the Telehealth Network of Minas Gerais (TNMG) subset dataset, both models were evaluated for their performance, generalizability capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels. Remarkably, the models were successfully deployed on a resource-constrained microcontroller, proving their suitability for edge device applications. Generalization capabilities were confirmed through the evaluation on the China Physiological Signal Challenge 2018 (CPSC) dataset. The models' efficient resource utilization, occupying 70.6% of memory and 9.4% of flash memory, makes them promising candidates for real-world healthcare applications. Overall, this research advances abnormality identification in ECG data, contributing to the progress of AI in healthcare.

Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism.

Aim: Deep learning's widespread use prompts heightened scrutiny, particularly in the biomedical fields, with a specific focus on model generalizability. This study delves into the influence of training data characteristics on the generalization performance of models, specifically in cardiac abnormality detection. Materials & methods: Leveraging diverse electrocardiogram datasets, models are trained on subsets with varying characteristics and subsequently compared for performance. Additionally, the introduction of the attention mechanism aims to improve generalizability. Results: Experiments reveal that using a balanced dataset, just 1% of a large dataset, leads to equal performance in generalization tasks, notably in detecting cardiology abnormalities. Conclusion: This balanced training data notably enhances model generalizability, while the integration of the attention mechanism further refines the model's ability to generalize effectively.

This study tackles a common problem for deep learning models: they often struggle when faced with new, unfamiliar data that they have not been trained on. This phenomenon is also known as performance drop in out-of-distribution generalization. This reduced performance on out-of-distribution generalization is a key focus of the research, aiming to improve the models' ability to handle diverse data sets beyond their training data.The study examines how the characteristics of the dataset used to train deep learning models affect their ability to detect abnormal heart activities when applied to new, unseen data. Researchers trained these models using various sets of electrocardiogram (ECG) data and then evaluated their performance in identifying abnormalities. They also introduced an attention mechanism to enhance the models' learning capabilities. The attention mechanism in deep learning is like a spotlight that helps the model focus on important information while ignoring less relevant details.The findings were particularly noteworthy. Despite being trained on a small, well-balanced subset of a larger dataset, the models excelled in detecting heart abnormalities in new, unfamiliar data. This training method significantly improved the models' generalization and performance with unseen data. Furthermore, integrating the attention mechanism substantially enhanced the models' ability to generalize effectively on new information.