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Personalized wearable systems for real-time ECG classification and healthcare interoperability: Real-time ECG classification and FHIR interoperability

Walinjkar, A and Woods, J (2017) Personalized wearable systems for real-time ECG classification and healthcare interoperability: Real-time ECG classification and FHIR interoperability. In: 2017 Internet Technologies and Applications (ITA), 2017-09-12 - 2017-09-15, Wrexham, UK.

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Abstract

Continuous monitoring of an individual's health using wearable biomedical devices is becoming a norm these days with a large number of wearable kits becoming easily available. Modern wearable health monitoring devices have become easily available in the consumer market, however, real-time analyses and prediction along with alerts and alarms about a health hazard are not adequately addressed in such devices. Taking ECG monitoring as a case study the research paper focusses on signal processing, arrhythmia detection and classification and at the same time focusses on updating the electronic health records database in realtime such that the concerned medical practitioners become aware of an emergent situation the patient being monitored might face. Also, heart rate variability (HRV) analysis is usually considered as a basis for arrhythmia classification which largely depends on the morphology of the ECG waveforms and the sensitivity of the biopotential measurements of the ECG kits, so it may not yield accurate results. Initially, the ECG readings from the 3-Lead ECG analog front-end were de-noised, zero-offset corrected, filtered using recursive least square adaptive filter and smoothed using Savitzky-Golay filter and subsequently passed to the data analysis component with a unique feature extraction method to increase the accuracy of classification. The machine learning models trained on MITDB arrhythmia database (MIT-BIH Physionet) showed more than 97% accuracy using kNN classifiers. Neuralnet fitting models showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. ECG abnormalities based on annotations in MITDB could be classified and these ECG observations could be logged to a server implementation based on FHIR standards. The instruments were networked using IoT (Internet of Things) devices and ECG event observations were coded according to SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to - ake appropriate and timely decisions. The system emphasizes on `preventive care rather than remedial cure' as the next generation personalized health-care monitoring devices become available.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2017 Internet Technologies and Applications (ITA)
Uncontrolled Keywords: adaptive filters, bioelectric potentials, diseases, electrocardiography, electronic health records, feature extraction, health care, learning (artificial intelligence), least squares approximations, medical signal processing, patient monitoring, pattern classification, regression analysis, signal classification, signal denoising, 3-Lead ECG analog front-end, ECG classification, ECG event observations, ECG kits biopotential measurements, ECG monitoring, ECG waveforms, FHIR interoperability, MIT-BIH Physionet, MITDB arrhythmia database, SNOMED coding system, Savitzky-Golay filter, arrhythmia classification, electronic health records database, feature extraction method, healthcare interoperability, heart rate variability analysis, kNN classifiers, machine learning models, neural net fitting models, personalized health-care monitoring devices, personalized wearable systems, recursive least square adaptive filter, signal processing, wearable biomedical devices, wearable health monitoring devices, Adaptive filters, Biomedical monitoring, Databases, Electrocardiography, Monitoring, Real-time systems, Arrhythmia Neural-Net, Arrhythmia classification, Arrhythmia detection, ECG FHIR, ECG signal processing, GP Connect, HAPI FHIR, HL7, Healthcare monitoring, MITDB Physionet, SNOMED-CT FHIR, Wearable IoT
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 02 Mar 2018 10:39
Last Modified: 02 Mar 2018 11:15
URI: http://repository.essex.ac.uk/id/eprint/21588

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