CardiaQloud: A Remote ECG Monitoring System Using Cloud Services for eHealth and mHealth Applications

Villanueva-Miranda, Ismael; Nazeran, Homer; Martínek, Radek

doi:10.1109/healthcom.2018.8531164

“…where devices can also be used for the management of insulin as well [37], cardiac disease through ECG [38], sleep apnea monitoring [39] or as generic monitoring platforms such as smart-monitor [40] to provide 'a la carte' system based on the patient health circumstances. Machine learning methods can then be applied to these physiological signals for predictive 301 health management.…”

Section: Ai In Remote Patient Monitoringmentioning

confidence: 99%

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

Qureshi¹,

Irfan²,

Ali³

et al. 2023

Preprint

0

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<p>Data generated from sources such as wearable sensors, medical imaging, personal health records, pathology records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, Graphical Processing Units (GPUs), and Tensor Processing Units (TPUs), provide the means to utilize these data. Consequently, many Artificial Intelligence (AI)-based methods have been developed to infer from large healthcare data. Here, we present an overview of recent progress in artificial intelligence and biosensors in medical and life sciences. We discuss the role of machine learning in medical imaging, precision medicine, and biosensors for the Internet of Things (IoT). We review the most recent advancements in wearable biosensing technologies that use AI to assist in monitoring bodily electro-physiological and electro-chemical signals and disease diagnosis, demonstrating the trend towards personalized medicine with highly effective, inexpensive, and precise point-of-care treatment. Furthermore, an overview of the advances in computing technologies, such as accelerated artificial intelligence, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues that biosensors and IoT-based healthcare generate, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects </p>

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“…where devices can also be used for the management of insulin as well [40], cardiac disease through ECG [41], sleep apnea monitoring [42] or as generic monitoring platforms such as smart-monitor [43] to provide 'a la carte' system based on the 300 patient health circumstances. Machine learning methods can 301 then be applied to these physiological signals for predictive 302 health management.…”

Section: Ai In Remote Patient Monitoringmentioning

confidence: 99%

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

Qureshi¹,

Irfan²,

Ali³

et al. 2023

Preprint

1

0

View full text Add to dashboard Cite

<p>Data generated from sources such as wearable sensors, medical imaging, personal health records, pathology records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, Graphical Processing Units (GPUs), and Tensor Processing Units (TPUs), provide the means to utilize these data. Consequently, many Artificial Intelligence (AI)-based methods have been developed to infer from large healthcare data. Here, we present an overview of recent progress in artificial intelligence and biosensors in medical and life sciences. We discuss the role of machine learning in medical imaging, precision medicine, and biosensors for the Internet of Things (IoT). We review the most recent advancements in wearable biosensing technologies that use AI to assist in monitoring bodily electro-physiological and electro-chemical signals and disease diagnosis, demonstrating the trend towards personalized medicine with highly effective, inexpensive, and precise point-of-care treatment. Furthermore, an overview of the advances in computing technologies, such as accelerated artificial intelligence, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues that biosensors and IoT-based healthcare generate, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects </p>

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“…Each OMP iteration step involves matrix-vector products, the computation of a pseudo-inverse of progressively larger size and, above all, the search for a peak value over a vector the same size as the dictionary column. Computational cost increases with the number of components modelled by (2). Shorter segments can be processed faster, but the number of segments gets larger and, with fixed overlap length, efficiency decreases.…”

Section: Decompositionmentioning

confidence: 99%

“…In a mobile context, data acquisition and processing devices are tasked with delivering healthcare measurement information to a data collection and analysis centre, most likely a cloud-based one [2]. For instance, Bluetooth lowenergy (BLE) is often employed for short-range data transmission from sensing units to a smartphone, the latter then providing the link to a cloud-based application [3].…”

Section: Introductionmentioning

confidence: 99%

Gaussian-based analysis for accurate compressed ECG trace streaming

Galli¹,

Giorgi²,

Narduzzi³

2022

Proceedings of the 25th IMEKO TC4 International Symposium and 23rd International Workshop on ADC and DAC Modelling and Testing

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Wearable cardiac monitors can usefully contribute to early detection of potential cardiovascular pathologies, however ECG trace data streaming over wireless links creates some significant challenges. We propose a signal analysis approach based on a Gaussian dictionary to model and compress ECG traces. The algorithm operates on fixed-length segments, and achieves effective compression for wireless data transmission, associating just 10 bytes to each Gaussian feature. At the same time it enables accurate reconstruction of ECG traces from the reduced data set. We tested our method on a set of 46 ECG recordings taken from the Physionet MIT-BIH Arrythmia Database, obtaining 90% data compression rates, while percent relative deviation of reconstructed traces is always below 5%.

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CardiaQloud: A Remote ECG Monitoring System Using Cloud Services for eHealth and mHealth Applications

Cited by 9 publications

References 4 publications

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

Gaussian-based analysis for accurate compressed ECG trace streaming

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