Wearable recordings of neurophysiological signals captured from the wrist offer enormous
potential for seizure monitoring. Yet, data quality remains one of the most challenging factors …

Cardiovascular diseases are the leading cause of death globally and contribute significantly
to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using …

This chapter presents the fundamental signal processing techniques used to analyze the
PPG signal. The chapter starts by providing an overview of the PPG signal, covering its …

Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the
introduction of mobile and wearable health care, it is becoming increasingly important to …

Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to
measure vital signs (eg, heart rate). The method is, however, highly susceptible to motion …

Photoplethysmography is a method to visualize the variation in blood volume within tissues
with light. The signal obtained has been used for the monitoring of patients, interpretation for …

Photoplethysmography (PPG) is a non-invasive optical method to acquire various vital signs,
including heart rate (HR) and heart rate variability (HRV). The PPG method is highly …

Background With the development and promotion of wearable devices and their mobile
health (mHealth) apps, physiological signals have become a research hotspot. However …

Research on noninvasive blood pressure (NIBP) measurement using electrocardiogram
(ECG)/photoplethysmogram (PPG) and their combinations has been most popular in …

Machine learning algorithms have brought remarkable advancements in detecting motion
artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference …