Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.

Fog-computing-based heartbeat detection and arrhythmia classification using machine learning / Scirè, Alessandro; Tropeano, Fabrizio; Anagnostopoulos, Aris; Chatzigiannakis, Ioannis. - In: ALGORITHMS. - ISSN 1999-4893. - 12:2(2019). [10.3390/a12020032]

Fog-computing-based heartbeat detection and arrhythmia classification using machine learning

Anagnostopoulos, Aris
;
Chatzigiannakis, Ioannis
2019

Abstract

Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.
2019
Algorithm engineering; Automated detection of abnormalities; Data mining; ECG; Experimental evaluation; Heartbeat classification; Long-short term memory; Recurrent neural network; Theoretical Computer Science; Numerical Analysis; Computational Theory and Mathematics; Computational Mathematics
01 Pubblicazione su rivista::01a Articolo in rivista
Fog-computing-based heartbeat detection and arrhythmia classification using machine learning / Scirè, Alessandro; Tropeano, Fabrizio; Anagnostopoulos, Aris; Chatzigiannakis, Ioannis. - In: ALGORITHMS. - ISSN 1999-4893. - 12:2(2019). [10.3390/a12020032]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1271809
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