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.

Multistage Pruning of CNN Based ECG Classifiers for Edge Devices.

Using smart wearable devices to monitor patients' electrocardiogram (ECG) for real-time detection of arrhythmias can significantly improve healthcare outcomes. Convolutional neural network (CNN) based deep learning has been used successfully to detect anomalous beats in ECG. However, the computational complexity of existing CNN models prohibits them from being implemented in low-powered edge devices.

SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches.

The abnormal pause or rate reduction in breathing is known as the sleep-apnea hypopnea syndrome and affects the quality of sleep of an individual. A novel method for the detection of sleep apnea events (pause in breathing) from peripheral oxygen saturation (SpO2) signals obtained from wearable devices is discussed in this paper. The paper details an apnea detection algorithm of a very high resolution on a per-second basis for which a 1-dimensional convolutional neural network- which we termed SomnNET- is developed.

Multimodal Multiresolution Data Fusion Using Convolutional Neural Networks for IoT Wearable Sensing.

With advances in circuit design and sensing technology, the acquisition of data from a large number of Internet of Things (IoT) sensors simultaneously to enable more accurate inferences has become mainstream. In this work, we propose a novel convolutional neural network (CNN) model for the fusion of multimodal and multiresolution data obtained from several sensors. The proposed model enables the fusion of multiresolution sensor data, without having to resort to padding/ resampling to correct for frequency resolution differences even when carrying out temporal inferences like high-resolution event detection.

Exploring nurses' perceptions of collecting and using HOBIC measures to guide clinical practice and improve care.

Ontario's Health Outcomes for Better Information and Care (HOBIC) is designed to help organizations and nurses plan and evaluate care by comparing patient outcomes with historical data on similar cases. Yet, fewer than 15% of patients in a 2010 study were found to have complete admission and discharge data sets. This low utilization rate of HOBIC measures prompted the current qualitative study, in which nurses from three clinical settings in an academic teaching hospital were interviewed to gain their perceptions related to collecting and using HOBIC measures in practice.

Performance improvement of 10 Gb/s direct modulation OFDM by optical injection using monolithically integrated Discrete Mode lasers.

Performance improvement of a directly modulated 10 Gb/s OFDM system by optical injection of monolithically integrated lasers is shown experimentally over differing fibre lengths. The modulation and optical injection is performed using monolithically integrated Discrete Mode lasers. It is shown that optical injection with this device reduces third order inter-modulation distortion by up to 10dB and this results in an improvement in system performance from above a forward error correction BER threshold of 1 × 10(-3) to significantly below it.

Linking HOBIC measures with length of stay and alternate levels of care: implications for nurse leaders in their efforts to improve patient flow and quality of care.

Integral to understanding and leveraging performance data to monitor and drive quality improvement (QI) efforts to enhance patient care is a partnership between researchers (who generate data) and nurse executives (who lead QI efforts). In Canada, evidence-based, nursing-sensitive patient outcome data are included in the Health Outcomes for Better Information and Care (HOBIC) initiative. A descriptive study was undertaken to examine the relationships and predictive abilities of HOBIC measures with length of stay (LOS) and alternate levels of care (ALC) measures.

Severe enamel hypoplasia in a case of intestinal lymphangiectasia: a rare protein-losing enteropathy.

A unique case of enamel hypoplasia is presented. This appears to be the first case to be published in connection with the rare protein-losing enteropathy, intestinal lymphangiectasia. It seems likely that if the treatment of systemic disease is delayed, disruption of normal tooth formation is inevitable.