Objective: Microwave breast imaging has seen significant academic and commercial development in recent years, with four new operational microwave imaging systems used with patients since 2016. In this work, a comprehensive review of these recent clinical advances is presented, comparing patient populations and study outcomes. For the first time, the design of operational microwave imaging systems are compared in detail. Methods: Firstly, the current understanding of dielectric properties of human breast tissues is reviewed; considering evidence from operational microwave imaging systems and from dielectric properties measurement studies. Secondly, design features of operational microwave imaging systems are discussed in terms of advantages and disadvantages during clinical operation. Results: Reported results from patient imaging trials are compared, contrasting the principal results from each trial. Additionally, clinical experience from each trial is highlighted, identifying desirable system design features for clinical use. Conclusions: Increasingly, evidence from patient imaging studies indicate that a contrast in dielectric properties between healthy and cancerous breast tissues exists. However, despite the significant and encouraging results from patient trials, variation still exists in microwave imaging system design. Significance: This work seeks to define the current state-of-the-art in microwave breast imaging, and identify suitable design characteristics for ease of clinical use.

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.

Stress is known as a silent killer that contributes to several life-threatening health conditions such as high blood pressure, heart disease, and diabetes. The current standard for stress evaluation is based on self-reported questionnaires and standardized stress scores. There is no gold standard to independently evaluate stress levels despite the availability of numerous biophysiological stress indicators. With an increasing interest in wearable health monitoring in recent years, several studies have explored the potential of various biophysiological indicators of stress for this purpose. However, there is no clear understanding of the relative sensitivity and specificity of these stress-related biophysiological indicators of stress in the literature. Hence this study aims to perform statistical analysis and classification modelling of biophysiological data gathered from healthy individuals, undergoing various induced emotional states, and to assess the relative sensitivity and specificity of common biophysiological indicators of stress. In this paper, several frequently used key indicators of stress, such as heart rate, respiratory rate, skin conductance, RR interval, heart rate variability in the electrocardiogram, and muscle activation measured by electromyography, are evaluated based on a detailed statistical analysis of the data gathered from an already existing, publicly available WESAD (Wearable Stress and Affect Detection) dataset. Respiratory rate and heart rate were the two best features for distinguishing between stressed and unstressed states.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is associated with significant morbidity and mortality. The autonomic nervous system (ANS) plays an important role in the initiation and development of AF, causing alterations in atrial structure and electrophysiological defects. The intrinsic ANS of the heart consists of multiple ganglionated plexi (GP), commonly nestled in epicardial fat pads. These GPs contain both parasympathetic and sympathetic afferent and efferent neuronal circuits that control the electrophysiological properties of the myocardium. Pulmonary vein isolation and other cardiac catheter ablation targets including GP ablation can disrupt the fibers connecting GPs or directly damage the GPs, mediating the benefits of the ablation procedure. Ablation of GPs has been evaluated over the past decade as an adjunctive procedure for the treatment of patients suffering from AF. The success rate of GP ablation is strongly associated with specific ablation sites, surgical techniques, localization techniques, method of access and the incorporation of additional interventions. In this review, we present the current data on the clinical utility of GP ablation and its significance in AF elimination and the restoration of normal sinus rhythm in humans.