Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals’ nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal’s structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of $0.0070\pm 0.0028$ and an average MAE of $0.0522\pm 0.0098$ across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model’s accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.

Temperature analysis has been considered as a complementary method in medical evaluation and diagnosis. Several studies demonstrated that monitoring the temperature variations of the feet of diabetic patients can be helpful in the early identification of diabetic foot manifestations, and also in changing behaviors, which may contribute to reducing its incidence. In this review, several and most used techniques for assessing the temperature of the feet are presented, along with original published work on specific applications in diabetic foot complications. A review of solutions and equipment that operate according to the temperature assessment techniques is also presented. Finally, a comparison between the various technologies is presented, and the authors share their perspective on what will be the state of affairs in 5 years.

Parkinson's disease is a neurodegenerative disease. It is often diagnosed at an advanced stage, which can influence the control over the illness. Therefore, the possibility of diagnosing Parkinson's disease at an earlier stage, and possibly prognosticate it, could be an advantage. Given this, a literature review that covers current studies in the field is relevant. The aim of this study is to present a systematic literature review in which the models used for the diagnosis and prognosis of Parkinson's disease through voice and speech assessment are elucidated. Three databases were consulted to obtain the studies between 2019 and 2023: SienceDirect, IEEE Xplore and ACM Library . One hundred and six studies were considered eligible, considering the definition of inclusion and exclusion criteria. The vast majority of these studies (94.34%) focus on diagnosing the disease, while the remainder (11.32%) focus on prognosis. Voice analysis for the diagnosis and prognosis of Parkinson's disease using machine learning techniques can be achieved, with very satisfactory performance results, like is demonstrated in this systematic literature review.