26 citations · 38 across the 5 of their papers we have counts for
6 papers
Toward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices
Floriaan Bulten, Yawar Rasheed, Arlene John +2
Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia often require long-term monitoring for effective detection and diagnosis. Howeve…
EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction
Vigneshwar Hariharan, Chithra Reghuvaran, Arlene John +4
Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life. Despite advancements in dia…
A Review on Multisensor Data Fusion for Wearable Health Monitoring
Arlene John, Barry Cardiff, Deepu John
The growing demand for accurate, continuous, and non-invasive health monitoring has propelled multi-sensor data fusion to the forefront of healthcare technology. This review aims t…
SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches
Arlene John, Koushik Kumar Nundy, Barry Cardiff +1
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…
A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors
Arlene John, Barry Cardiff, Deepu John
Internet of Things (IoT) enabled wearable sensors for health monitoring are widely used to reduce the cost of personal healthcare and improve quality of life. The sleep apnea-hypop…
A Generalized Signal Quality Estimation Method for IoT Sensors
Arlene John, Barry Cardiff, Deepu John
IoT wearable devices are widely expected to reduce the cost and risk of personal healthcare. However, ambulatory data collected from such devices are often corrupted or contaminate…