From Federal Policy to Bedside Care: A Canadian Perspective on Key Considerations for Nurse Executive Action for AI Adoption - A Commentary.

The integration of artificial intelligence (AI) into healthcare represents a paradigm shift with the potential to enhance patient care and streamline clinical operations. This commentary explores the Canadian perspective on key organizational considerations for nurse executives, emphasizing the critical role they play in fostering the establishment of AI governance structures and advancing the front-line adoption of AI in nursing practice. The discussion delves into five domains of consideration, analyzing recent developments and implications for nursing executives.

Sensor-Location-Specific Joint Acquisition of Peripheral Artery Bioimpedance and Photoplethysmogram for Wearable Applications.

Background: Cardiovascular diseases (CVDs), being the culprit for one-third of deaths globally, constitute a challenge for biomedical instrumentation development, especially for early disease detection. Pulsating arterial blood flow, providing access to cardiac-related parameters, involves the whole body. Unobtrusive and continuous acquisition of electrical bioimpedance (EBI) and photoplethysmography (PPG) constitute important techniques for monitoring the peripheral arteries, requiring novel approaches and clever means.

ANNet: A Lightweight Neural Network for ECG Anomaly Detection in IoT Edge Sensors.

In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG) anomaly detection and system level power reduction of wearable Internet of Things (IoT) Edge sensors. The proposed network utilizes a novel hybrid architecture consisting of Long Short Term Memory (LSTM) cells and Multi-Layer Perceptrons (MLP). The LSTM block takes a sequence of coefficients representing the morphology of ECG beats while the MLP input layer is fed with features derived from instantaneous heart rate.

Evaluation of Level-Crossing ADCs for Event-Driven ECG Classification.

In this paper, a new methodology for choosing design parameters of level-crossing analog-to-digital converters (LC-ADCs) is presented that improves sampling accuracy and reduces the data stream rate. Using the MIT-BIH Arrhythmia dataset, several LC-ADC models are designed, simulated and then evaluated in terms of compression and signal-to-distortion ratio. A new one-dimensional convolutional neural network (1D-CNN) based classifier is presented.

Study of Electrode Locations for Joint Acquisition of Impedance- and Electro-cardiography Signals.

ICG (impedance cardiography) and ECG (electrocardiography) provide important indications about functioning of the heart and of overall cardiovascular system. Measuring ICG along with ECG using wearable devices will improve the quality of health monitoring, as ICG points to important hemodynamic parameters (such as time intervals, stroke volume, cardiac output, and their variability). In this work, various electrode locations (12 different setups) have been tested for possible joint ECG & ICG data acquisition (using the same electrodes) and signal quality has been evaluated for every setup.