## 1 Introduction  
It is clear that biomedical sensors will keep improving exponentially, as they become more accessible; more readily available on the market; more intelligent; smaller and more compact; and integrated into personal belongings, such as cellular phones, watches, and eyeglasses. It is an outstanding contribution to human health, entertainment, the military, security, sports, and leisure and in analysing a patient's physiological data and their interpretation. These sensors are an integral part of biomedical devices; however, this innovation has an ever-evolving trend coupled along with challenges to perform intelligently [1]. The integration of these sensors, subjected to different environments in both wired and wireless implementations, adds random noise to the system. The sudden upswing of the noise floor yields a degradation in the signal-to-noise ratio (C/N) and low energy bit per noise reference (Eb/No) during data processing. The drastic increase in the probability of error (Pe) eventually decreases the accuracy in such a system [2]. This interference results in flawed calculation and decision making, especially for artificial neural networks (ANNs) in biomedical applications [3]. An artificial neural network (ANN) is a part of a computing system that simulates the ability of the human neuron to learn the complex characteristics of the environment, to recognise patterns, and to generalise the inter-relationships between the features, including multidimensional datasets. It is part of the umbrella of artificial intelligence (AI) and deep learning, and it solves problems that would be impossible or difficult to solve by human effort or statistical criteria. ANNs have self-learning skills, enhancing their efficiency as more effective and reliable data become available to them. Primarily, ANNs enable the complex inter-relationships between the features within a given dataset to be identified and have seen widespread adoption in different applications, including biomedical and signal processing, which is readily and publicly available by wearable sensors [4]. Since data are everywhere--such as on the Internet--neglecting the integrity of a source of information poses a significant threat for ANNs to misinterpret the data. For critical aspects of the medical field, the efficacy of a diagnosis is threatened if the analysis that aids medical experts in research has a weak source, especially if dealing with the multitude of datasets that result in the presence of noisy and biased data, which often occurs from seemingly reliable sources [5]. Low-powered sensors are a critical source of unreliable information. Sudden changes in conditions can introduce environmental noise, and there are many possible avenues for noise and interference to blend into any part of the system. A sensor's scalability is another problem, given that the sensors nowadays are low power and with limited computation. Placement or location is another significant constraint, and if they are placed too close to other devices, they may be affected by crosstalk interference [6]. To understand the data, first, one needs to understand their composition. Data comprise valid and dirty data. Accurate data contain information that is accurate, holds predictive power, and is generalised to the entire set. Dirty data contain information that is misleading, noisy, or erroneous data, such as pragmatic contexts and the semantic- and syntactic-biased errors shown in Figure 1[7]. Noisy data are any random fluctuations considered unwanted, unpleasant, loud, or disruptive that hinder the generalisation of the entire dataset. For an ANN with predictive power to analyse the data, the dataset needs to be free from noise to reduce errors in classification and prediction, which would significantly impact medical experts in their professional biomedical interpretation. Given recent technological solutions, one relevant control algorithm is highlighted in solving these challenges [8]. Figure 1: Dirty data.