SPHERE wrist ECG data

Description of Data: - ECG data as recorded from the wrists for the IEEE Access paper ‘An ultra low power personalizable wrist worn ECG monitor integrated with IoT infrastructure’. - Data is recorded for 5 participants, ages 23 – 36. - Sampling rate is 128 Hz. Files are saved in the Matlab .mat format

SPHERE wrist ECG data

Description of Data: - ECG data as recorded from the wrists for the IEEE Access paper ‘An ultra low power personalizable wrist worn ECG monitor integrated with IoT infrastructure’. - Data is recorded for 5 participants, ages 23 – 36. - Sampling rate is 128 Hz. Files are saved in the Matlab .mat format

SPHERE wrist ECG data

Description of Data: - ECG data as recorded from the wrists for the IEEE Access paper ‘An ultra low power personalizable wrist worn ECG monitor integrated with IoT infrastructure’. - Data is recorded for 5 participants, ages 23 – 36. - Sampling rate is 128 Hz. Files are saved in the Matlab .mat format

SPHERE wrist ECG data

Description of Data:- ECG data as recorded from the wrists for the IEEE Access paper ‘An ultra low power personalizable wrist worn ECG monitor integrated with IoT infrastructure’. - Data is recorded for 5 participants, ages 23 – 36. - Sampling rate is 128 Hz. Files are saved in the Matlab .mat format

ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering

Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy. Therefore, filtering ECG signals became an important task. Currently, deep learning schemes are widely employed in signal-filtering tasks due to their efficient architecture of feature learning. This work presents a deep learning-based scheme for ECG signal filtering, which is based on the deep autoencoder module. According to this scheme, the data is processed through the encoder and decoder layer to reconstruct by eliminating noises. The proposed deep learning architecture uses a modified ReLU function to improve the learning of attributes because standard ReLU cannot adapt to huge variations. Further, a skip connection is also incorporated in the proposed architecture, which retains the key feature of the encoder layer while mapping these features to the decoder layer. Similarly, an attention model is also included, which performs channel and spatial attention, which generates the robust map by using channel and average pooling operations, resulting in improving the learning performance. The proposed approach is tested on a publicly available MIT-BIH dataset where different types of noise, such as electrode motion, baseline water and motion artifacts, are added to the original signal at varied SNR levels

IoT in healthcare: A scientometric analysis

This paper reviews scientific articles and patents about Internet of Things (IoT) in healthcare. The aim is to explore both the domain of research and the one of practice simultaneously. We compare the annual growth, the country production, and the trend topics of publications and patents, by focusing on the most relevant themes concerning the IoT in the healthcare industry. The analysis started with the selection of the publications and patents for the period 2015–2020. Since this comparative analysis between scientometric data in healthcare is new, the findings of this study can represent the basis for future studies to determine novel research opportunities on IoT. The study provides scholars with a better understanding of IoT research in healthcare and simultaneously extends knowledge of entrepreneurship in this field. Practitioners may benefit from this review to understand new and underexplored opportunities

SPHERE wrist ECG data

Description of Data:- ECG data as recorded from the wrists for the IEEE Access paper ‘An ultra low power personalizable wrist worn ECG monitor integrated with IoT infrastructure’. - Data is recorded for 5 participants, ages 23 – 36. - Sampling rate is 128 Hz. Files are saved in the Matlab .mat format.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

An Ultra Low Power Personalizable Wrist Worn ECG Monitor Integrated with IoT Infrastructure

© 2013 IEEE. Cardiovascular diseases are the leading cause of death in the U.K., motivating the use of long term wearable devices to monitor the heart in out-of-the-clinic settings. While a wide number of heart rate measuring wearable devices are now available, they are principally based upon photoplethysmography rather than the electrocardiogram (ECG) and are stand-alone devices rather than integrated with Internet-of-Things infrastructures which collect and combine information from a wide range of sensors. This paper presents a wrist worn ECG sensor which integrates with the SPHERE IoT platform-the UK\u27s demonstrator platform for health monitoring in the home environment, combining a range of on-person and ambient sensors. The ECG device integrates ultralow power consumption electronics with personalizable 3-D printed casings which maintain gold standard Ag/AgCl electrodes to provide measurements of the raw ECG waveform, heart rate, and meanNN and SDNN heart rate variability parameters. The end device allows for more than a month of battery life for a weight of \u3c50 g including the watch straps. The design and heart sensing performance of the device are presented in detail, together with the integration with the SPHERE IoT platform