Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices

From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring

The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people's needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.

LSTM-based real-time signal quality assessment for blood volume pulse analysis

Remote photoplethysmogram (rPPG) is a low-cost method to extract blood volume pulse (BVP). Some crucial vital signs, such as heart rate (HR) and respiratory rate (RR) etc. can be achieved from BVP for clinical medicine and healthcare application. As compared to the conventional PPG methods, rPPG is more promising because of its non-contacted measurement. However, both BVP detection methods, especially rPPG, are susceptible to motion and illumination artifacts, which lead to inaccurate estimation of vital signs. Signal quality assessment (SQA) is a method to measure the quality of BVP signals and ensure the credibility of estimated physiological parameters. But the existing SQA methods are not suitable for real-time processing. In this paper, we proposed an end-to-end BVP signal quality evaluation method based on a long short-term memory network (LSTM-SQA). Two LSTM-SQA models were trained using the BVP signals obtained with PPG and rPPG techniques so that the quality of BVP signals derived from these two methods can be evaluated, respectively. As there is no publicly available rPPG dataset with quality annotations, we designed a training sample generation method with blind source separation, by which two kinds of training datasets respective to PPG and rPPG were built. Each dataset consists of 38400 high and low-quality BVP segments. The achieved models were verified on three public datasets (IIP-HCI dataset, UBFC-Phys dataset, and LGI-PPGI dataset). The experimental results show that the proposed LSTM-SQA models can effectively predict the quality of the BVP signal in real-time.

Empowering People with a User-Friendly Wearable Platform for Unobtrusive Monitoring of Vital Physiological Parameters

Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological parameters. Bearing in mind all the parameters involved, we developed a user-friendly wrist-wearable device combined with a web-based application, to adequately address this need. The proposed compilation obtains the photoplethysmogram (PPG) from the subject's wrist and simultaneously extracts, in real time, the physiological parameters of heart rate (HR), blood oxygen saturation (SpO₂) and respiratory rate (RR), based on algorithms embedded on the wearable device. The described process is conducted solely within the device, favoring the optimal use of the available resources. The aggregated data are transmitted via Wi-Fi to a cloud environment and stored in a database. A corresponding web-based application serves as a visualization and analytics tool, allowing the individuals to catch a glimpse of their physiological parameters on a screen and share their digital information with health professionals who can perform further processing and obtain valuable health information.

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification

OBJECTIVE

The primary purpose of this work is to analyze the ability of N-BEATS architecture for the problem of prediction and classification of electrocar- diogram (ECG) signals. To achieve this, performance comparison with various types of other SotA (state-of-the-art) recurrent neural network architectures commonly used for such problems is conducted.

APPROACH

Four architectures (N-BEATS, LSTM, LSTM with peepholes, GRU) were tested for performance and dimension reduction problems for different number of leads (2, 3, 4, 6, 12), both in variants consisting of blended branches, allowing retaining ac- curacy while reducing the computational capacity needed. The analysis was performed on datasets and using metrics from Challenges in Cardiology (CinC) 2021 competition.

MAIN RESULTS

Best results were achieved for LSTM with peepholes, then LSTM, GRU and the worst for N-BEATS (challenge metrics respectively: 0.42, 0.40, 0.39, 0.35; for times: 0.0395 s, 0.0036 s, 0.0027 s, 0.0002 s). Commonly used LSTM outperforms N- BEATS in terms of multi-label classification, data set resilience, and obtained challenge metrics. Still, N-BEATS can obtain acceptable results for 2 lead classification (metric of 0.35 for N-BEATS and 0.38 for other networks) and outperforms other solutions in terms of complexity and speed.

SIGNIFICANCE

This paper features a novel approach of using the N-BEATS, which was previously used only for forecasting, to classify ECG signals with success. While N- BEATS multi-label classification capacity is lower than LSTM, its speed obtaining results with a reduced number of leads (faster by one to two degrees of magnitude) allows for arrhythmias detection and classification while using off-the-shelf wearable devices (Holter monitors, sport bands, etc.).