Detecting QRS complexes or R-peaks from the electrocardiogram (ECG) is the basis for heart rate determination and heart rate variability analysis. Over the years, multiple different methods have been proposed as solutions to this problem. Vast majority of the proposed methods are traditional rule based algorithms that are vulnerable to noise. We propose a new R-peak detection method that is based on the Long Short-Term Memory (LSTM) network. LSTM networks excel at temporal modelling tasks that include long-term dependencies, making it suitable for ECG analysis. Additionally, we propose data generator for creating noisy ECG data that is used to train the robust R-peak detector. Our initial testing shows that the proposed method outperforms traditional algorithms while the greatest competitive edge is achieved with the noisy ECG signals.
Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.
Background Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs). Objective This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach. Methods This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported. Results A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models’ characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Conclusions Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set.
BACKGROUND Preterm birth (PTB) as a common pregnancy complication is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly impacts around 15 million children annually across the world. Conventional approaches to predict PTB may neither be applicable for first-time mothers nor possess reliable predictive power. Recently, machine learning (ML) models have shown the potential as an appropriate complementary approach for PTB prediction. OBJECTIVE In this article we systematically reviewed the literature concerned with PTB prediction using ML modeling. METHODS This systematic review was conducted in accordance with the PRISMA statement. A comprehensive search was performed in seven bibliographic databases up until 15 May 2021. The quality of studies was assessed, and the descriptive information including socio-demographic characteristics, ML modeling processes, and model performance were extracted and reported. RESULTS A total of 732 papers were screened through title and abstract. Of these, 23 studies were screened by full text resulting in 13 papers that met the inclusion criteria. CONCLUSIONS We identified various ML models used for different EHR data resulting in a desirable performance for PTB prediction. However, evaluation metrics, software/package used, data size and type, and selected features, and importantly data management method often varied from study to study threatening the reliability and generalizability of the model. CLINICALTRIAL n.a.
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