This review systematically analyzes the latest advancements in preprocessing techniques for Electrocardiography (ECG) and Magnetocardiography (MCG) signals over the past decade. ECG and MCG play crucial roles in cardiovascular disease (CVD) detection, but both are susceptible to noise interference. This paper categorizes and compares different ECG denoising methods based on noise types, such as baseline wander (BW), electromyographic noise (EMG), power line interference (PLI), and composite noise. It also examines the complexity of MCG signal denoising, highlighting the challenges posed by environmental and instrumental interference. This review is the first to systematically compare the characteristics of ECG and MCG signals, emphasizing their complementary nature. MCG holds significant potential for improving the precision of CVD clinical diagnosis. Additionally, it evaluates the limitations of current denoising methods in clinical applications and outlines future directions, including the potential of explainable neural networks, multi-task neural networks, and the combination of deep learning with traditional methods to enhance denoising performance and diagnostic accuracy. In summary, while traditional filtering techniques remain relevant, hybrid strategies combining machine learning offer substantial potential for advancing signal processing and clinical diagnostics. This review contributes to the field by providing a comprehensive framework for selecting and improving denoising techniques, better facilitating signal quality enhancement and the accuracy of CVD diagnostics.

Objective . Optically pumped magnetometers (OPMs) are recently developed magnetocardiography (MCG) sensors that can detect cardiac diseases. This is of great clinical significance for detecting acute myocardial infarction (AMI) and premature ventricular contractions (PVC). This study investigates the use of an array of OPMs to detect heart disease in animals. Approach. An array of OPMs was used to detect the MCG of AMI and PVC in dogs. We used four dogs in this study, and models of AMI with different degrees of severity were established by ligating the middle and proximal segments of the left anterior descending coronary artery. The dogs had PVC at the time of AMI. Continuous MCG time series with corresponding electrocardiograms (ECGs) and average MCG for each dog in different states are presented. The MCG features were extracted from the MCG butterfly diagram, magnetic field map, and pseudo current density map. The MCG features were used to quantify and compare with the gold-standard ECG measures. Main results. The results show that MCG features can accurately distinguish different states of dogs. That is, an array of OPMs can effectively detect AMI and PVC in dogs. Significance. We conclude that the array of OPMs can detect heart diseases in animals. Moreover, OPMs can complement or even replace superconducting quantum interference devices for MCG measurement in animals and diagnosis of human heart diseases in the future.

Magnetoencephalography (MEG) is a non-invasive imaging technique that captures neural activity with high spatio-temporal resolution. In recent years, novel wearable devices based on Optically Pumped Magnetometer (OPM) have emerged as a new driving force for advancing MEG due to their cost-effectiveness, portability, and mobility. In practical applications, MEG signals are frequently influenced by various interference sources, resulting in degradation of signal quality. Consequently, numerous suppression techniques have been proposed to overcome these challenges. This manuscript presents a comprehensive review of the most advanced methods for suppressing MEG noise or artifacts, with a specific focus on mitigating background noise, physiological artifacts (such as those caused by heartbeat, eye movements, and muscle contractions), as well as technical artifacts (including system-related artifacts associated with devices, motion-induced artifacts, and metal-induced artifacts). Additionally, the current limitations and challenges of these approaches in real-world scenarios are highlighted. Reviewing nearly a decade of research, there is an urgent need for a lightweight noise analysis framework in the complex measurement environment of wearable OPM-MEG devices. This framework should be capable of effectively detecting, classifying, and suppressing individual and combined MEG interference. By addressing this need, we can enhance the reliability and practicality of MEG signals while advancing brain science research.