This paper conducts an extensive review of flexible cardiac sensing devices designed for electrocardiogram (ECG) acquisitions, with emphasis on their application in cardiac health monitoring. This study focuses on characteristics crucial to these devices, including: flexibility, durability, biocompatibility, sensitivity, and stretchability. It provides a comprehensive overview of prevalent fabrication methods and materials employed for flexible electrode production, with insights from several studies that utilize these electrodes across diverse applications. Furthermore, the review highlights the significant role of machine learning (ML) in cardiac health monitoring and broader ECG analysis applications. With the most used methods being deep learning, support vector machines, random forest, and linear discriminant analysis, the paper delves into studies that leverage ML for heart disease classification as well as other applications such as emotion detection and biometric recognition. The paper culminates with an overview of studies that integrate both flexible sensing technology and ML, particularly in the domain of cardiac health monitoring. It sheds light on the important relationship between these two techniques, underscoring their impact on advancing ECG-based health monitoring methodologies. Besides reviewing the current state of these technologies, the paper also outlines future perspectives and potential directions for research in this domain.

Breast tumor is one of the most prominent indicators for diagnosis of breast cancer. Magnetic Resonance Imaging (MRI) is a relevant imaging modality tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. This paper presents a pipeline to automatically segment multiple tumors in breast MRI scans, following the methodology proposed by one previous study, addressing its limitations in detecting multiple tumors and automatically selecting seed points using a 3D region growing algorithm. The pre-processing includes bias field correction, data normalization, and image filtering. The segmentation process involved several steps, including identifying high-intensity points, followed by identifying high-intensity regions using k-means clustering. Then, the centers of the regions were used as seeds for the 3D region growing algorithm, resulting in a mask with 3D structures. These masks were then analyzed in terms of their volume, compactness, and circularity. Despite the need for further adjustments in the model parameters, the successful segmentation of four tumors proved that our solution is a promising approach for automatic multi-tumor segmentation with the potential to be combined with a classification model relying on the characteristics of the segmented structures.

In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.

Recently, several studies have demonstrated the potential of electrocardiogram (ECG) to be used as a physiological signature for biometric systems (BS). We investigated the potential of ECG as a biometric trait for the identification and authentication of individuals. We used data from a public database, CYBHi, containing two off-the-person records from 63 subjects, separated by 3 months. For the BS, two templates were generated: (1) cardiac cycles (CC) and (2) scalograms. The identification with CC was performed with LDA, kNN, DT, and SVM, whereas a convolutional neural network (CNN) and a distance-based algorithm were used for scalograms. The authentication was performed with a distance-based algorithm, with a leave-one-out cross validation, for impostors evaluation. The identification system yielded accuracies of 79.37% and 69.84% for CC with LDA and scalograms with CNN, respectively. The authentication yielded an accuracy of 90.48% and an impostor score of 13.06% for CC, and it had an accuracy of 98.42% and an impostor score of 14.34% for scalograms. The obtained results support the claim that ECG can be successfully used for personal recognition. To the best of our knowledge, our study is the first to thoroughly compare templates and methodologies to optimize the performance of an ECG-based biometric system.