Analysing of diverse stress pattern in ECG using machine learning techniques

ECG signals are commonly used to diagnose various cardiac conditions, and the classification of these signals is an important task in Stress identification. With the advancement of ML techniques, several ML models have been developed to classify ECG signals with high accuracy. This paper aims to review recent studies that use ML models for ECG classification, equivalence to the working nature of different prototype, and discuss the challenges and limitations of these models. The study found that deep learning models, such as CNNs and RNNs, outperformed traditional ML models in terms of closeness and validity. The results also showed that the combination of multiple models could improve the classification performance. However, the study also highlighted the need for large annotated ECG datasets and the requirement of domain expertise for feature engineering. Overall, this review provides a complete analysis of the current state of ECG classification using ML models and identifies areas for future research and improvement. Even though the accuracy of ML models in ECG classification is not always perfect, they can still provide valuable information and support to healthcare professionals. In some cases, even a lower accuracy can still be useful, especially in the context of large-scale screening and monitoring programs, where the goal is to identify individuals who require further evaluation. In these cases, the high computational speed and scalability of ML models make them an attractive option, as they can quickly process large amounts of data and provide preliminary results that can be followed up with more comprehensive and accurate tests. Additionally, the results from ML models can also be used as a starting point for further analysis by healthcare professionals, who can use their domain knowledge to interpret the results and make informed decisions. Therefore, despite their limitations, ML models can still play an vital role in the field of Electrocardiogram classification and contribute to improving patient outcomes.