## 2 Related Researches  
In this section, we will review and summarize published literature on the application of machine learning to detect neurological complications in general. Another topic of discussion will be how TVM and VTA have performed previously when used as tools to deploy machine learning algorithms in FPGA. Several search engines and repositories, e.g., Google Scholar, IEEE Explorer, MDPI, and PubMed are used to identify existing and relevant articles. After a comprehensive review of the literature retrieved initially, we found that despite the current success of machine learning in detection models for many scientific areas including health complications, the eminence of machine learning in neurological complications for rescue patients, is yet to be well explored. The data type we are using, i.e., rescue data are unheard of in this area of research as per our exploration. The meta-analysis of neurological diseases diagnosis with deep learning presented by [PERSON] et al., 2020 [8] has explored 136 research articles to confer the discipline, frameworks, and methodologies used by different deep learning techniques to diagnose different human neurological disorders like stroke, Alzheimer, Parkinson's, epilepsy, autism, migraine, cerebral palsy, and multiple sclerosis. The goal of that research was to analyze and examine the performance and publication trend of different deep learning techniques employed in the investigation of these diseases. The research study showed that classification accuracy in the diagnosis of particular neurological and neuropsychiatric disorders lies in the range between 90 and 100%. Diagnosis of Alzheimer's disorder has the highest rate of accuracy, i.e., 100% using a convolution neural network. The researchers concluded that the scope of such diagnosis should be extended with deep learning to achieve more effective performance. [PERSON] et al., 2019 [9] provided a systematic review of employing deep learning in the clinical neuroscience field with the existing challenges and a look to the future. The research highlighted the broad spectrum of medical image-based data, their classification, segmentation, and their effectiveness in risk prognostication. The performance of machine learning models using non-image data like electroencephalography is also reviewed stating their high sensitivity and specificity for epileptic seizure detection. The authors concluded that for improved diagnosis or early detection of acute neurological events, various domains of deep learning can play a significant role, especially when the patient is suffering from AD, ASD, or ADHD. It was also emphasized that to understand the true potential of deep learning, two major challenges need to be overcome. Firstly, while the technical challenges surrounding the generalizability and interpretability of machine learning models exist, more difficult challenges like data privacy and accessibility are important. The second challenge is the data quality, which is an identical problem to our research, as the data collected through rescue procedures are often very raw and require a lot of filtering before using in a model. Other research like that [10] from [PERSON] et. al. provided a comprehensive overview of works focused on specific neurological disorders, in this case, automated epileptic seizure detection using deep learning techniques and neuroimaging modalities. A performance-based review of different machine learning algorithms for detecting early falls of elderly persons was provided in [11] by [PERSON] The author used recurrent neural network, random forest and support vector machine to develop the detection model which provided an accuracy of 96-98%, but no significant research was found that works with rescue data to detect neurological complications in general which is the prime goal of this research.