Fetal ECG Extraction From Maternal ECG Using Attention-Based CycleGAN.

A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7% F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the "very good" and "good" ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.

A localization method for wireless capsule endoscopy using side wall cameras and IMU sensor.

Localizing the endoscopy capsule inside gastrointestinal (GI) system provides key information which leads to GI abnormality tracking and precision medical delivery. In this paper, we have proposed a new method to localize the capsule inside human GI track. We propose to equip the capsule with four side wall cameras and an Inertial Measurement Unit (IMU), that consists of 9 Degree-Of-Freedom (DOF) including a gyroscope, an accelerometer and a magnetometer to monitor the capsule's orientation and direction of travel. The low resolution mono-chromatic cameras, installed along the wide wall, are responsible to measure the actual capsule movement, not the involuntary motion of the small intestine. Finally, a fusion algorithm is used to combine all data to derive the traveled path and plot the trajectory. Compared to other methods, the presented system is resistive to surrounding conditions, such as GI nonhomogeneous structure and involuntary small bowel movements. In addition, it does not require external antenna or arrays. Therefore, GI tracking can be achieved without disturbing patients' daily activities.

Automatic Detection of Intestinal Bleeding using an Optical Sensor for Wireless Capsule Endoscopy.

Wireless capsule endoscopy (WCE) has been an effective and safe way to diagnose gastrointestinal (GI) disorders, such as, colon cancers, polyps and bleeding. The detection of bleeding and other anomalies is currently determined through conventional visual inspection of the WCE images by the physicians. An on-chip bleeding sensor is thus required, that can perform an automatic prescreening of the bleeding areas in real-time using blood's optical properties to assist the diagnosis. In this study, a spectrophotometer was initially used to evaluate the chromatic properties of blood. It is found that the reflection ratio pairs of 700 nm to 630 nm and 480 nm to 530 nm provide important statistics to separate blood from non-blood samples. It has been implemented hardware using small LEDs and photodiodes to validate the results. Therefore, the proposed sensor system works as a good candidate to be integrated in a WCE device to detect GI bleeding quickly and in real-time.

A Fluorescence-Based Wireless Capsule Endoscopy System for Detecting Colorectal Cancer.

Wireless capsule endoscopy (WCE) has been widely used in gastrointestinal (GI) diagnosis that allows the physicians to examine the interior wall of the human GI tract through a pain-free procedure. However, there are still several limitations of the technology, which limits its functionality, ultimately limiting its wide acceptance. Its counterpart, the wired endoscopic system is a painful procedure that demotivates patients from going through the procedure, and adversely affects early diagnosis. Furthermore, the current generation of capsules is unable to automate the detection of abnormality. As a result, physicians are required to spend longer hours to examine each image from the endoscopic capsule for abnormalities, which makes this technology tiresome and error-prone. Early detection of cancer is important to improve the survival rate in patients with colorectal cancer. Hence, a fluorescence-imaging-based endoscopic capsule that automates the detection process of colorectal cancer was designed and developed in our lab. The proof of concept of this endoscopic capsule was tested on porcine intestine and liquid phantom. The proposed WCE system offers great possibilities for future applicability in selective and specific detection of other fluorescently labelled cancers.

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life.

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.