activity
20192021
most citedMultistage Pruning of CNN Based ECG Classifiers for Edge Devices

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2021

Identifying Stroke Indicators Using Rough Sets

Muhammad Salman Pathan, Jianbiao Zhang, Deepu John +2

Stroke is widely considered as the second most common cause of mortality. The adverse consequences of stroke have led to global interest and work for improving the management and d…

cs.LG20211 cited

Multistage Pruning of CNN Based ECG Classifiers for Edge Devices

Xiaolin Li, Rajesh Panicker, Barry Cardiff +1

Using smart wearable devices to monitor patients electrocardiogram (ECG) for real-time detection of arrhythmias can significantly improve healthcare outcomes. Convolutional neural…

cs.CR2021

Continuous User Authentication using IoT Wearable Sensors

Conor Smyth, Guoxin Wang, Rajesh Panicker +3

Over the past several years, the electrocardiogram (ECG) has been investigated for its uniqueness and potential to discriminate between individuals. This paper discusses how this d…

eess.SP2020

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…

q-bio.QM2019

Predicting Stroke from Electronic Health Records

Chidozie Shamrock Nwosu, Soumyabrata Dev, Peru Bhardwaj +2

Studies have identified various risk factors associated with the onset of stroke in an individual. Data mining techniques have been used to predict the occurrence of stroke based o…