Accurate and reliable 3-lead to 12-lead ECG reconstruction methodology for remote health monitoring applications

Maheshwari, Sidharth; Acharyya, Amit; Rajalakshmi, P.; Puddu, Paolo Emilio; Schiariti, Michele

doi:10.1016/j.irbm.2014.07.004

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…As it is changing with time, usually it represents time vs. heart activity plot. Many architectures for the ECG signal acquisition system have been developed in the past [9]- [11]. There are 12 different ECG leads, but only leads I, II and III are sufficient to derive the information present in the 12 leads.…”

Section: A Signal Acquisition and Feature Extractionmentioning

confidence: 99%

“…A single packet in WiFi has a size of 1524 bytes, where 24 bytes account for header fields. The number of packets required to be transmitted can be calculated using the equation (8) and total length of data to be transmitted is given by equation (9).…”

Section: B Energy Consumption and Life Time Of Network Nodementioning

confidence: 99%

“…In equation (9), L i indicates the data length to be transmitted by the radio in bytes. The energy consumption is analysed in two scenarios, constant burst transmission and the adaptive rule engine based smart transmission.…”

Section: B Energy Consumption and Life Time Of Network Nodementioning

confidence: 99%

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System Architecture for Low-Power Ubiquitously Connected Remote Health Monitoring Applications With Smart Transmission Mechanism

et al. 2015

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We present a novel smart transmission technique with seamless handoff mechanism to achieve ubiquitous connectivity using multiple on-chip radios targeting remote health monitoring applications. For the first time to the best of our knowledge, a system architecture for low-power ubiquitously connected multiparametric remote health monitoring system is proposed in this paper. The architecture proposed uses a generic adaptive rule engine for classifying the collected multiparametric data from patient and smartly transmit the data when only needed. The on-chip seamless handoff mechanism proposed aids for the ubiquitous connectivity with a very good energy savings by intelligent controlling of the multiple on-chip radios. The performance analysis of the proposed on-chip seamless handoff mechanism along with adaptive rule engine-based smart transmission mechanism achieves on an average of 50.39% of energy saving and 51.01% reduction in duty cycle of transmitter taken over 20 users compared with the continuous transmission. From the hardware complexity analysis made on the proposed seamless handoff controller and adaptive rule engine concludes that they require only 2082 CMOS transistors for real-time implementation.Index Terms-Ubiquitous connectivity, seamless hand-off, RSSI, path loss model, random waypoint mobility (RWP).

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Section: A Signal Acquisition and Feature Extractionmentioning

confidence: 99%

Section: B Energy Consumption and Life Time Of Network Nodementioning

confidence: 99%

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System Architecture for Low-Power Ubiquitously Connected Remote Health Monitoring Applications With Smart Transmission Mechanism

et al. 2015

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“…None of the aforementioned methodologies aimed at finding application in remote health monitoring. In our previous works [11,15,21,22], we have focused on this applications of lead reconstruction in a RHM scenario. All possible combinations of subsets of 3-leads of S12 system (S12S systems) have been used to reconstruct the missing leads using personalized methodology and have been shown to produce superior results compared to other investigations by various researchers.…”

Section: Previous Work On Lead Reconstruction For Rhm Applicationmentioning

confidence: 99%

Personalized Reduced 3-Lead System formation methodology for Remote Health Monitoring Applications and Reconstruction of Standard 12-Lead system

Maheshwari

Acharyya²,

Schiariti³

et al. 2015

Int Arch Med

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Remote Health Monitoring (RHM) applications encounter limitations from technological front viz. bandwidth, storage and transmission time and the medical science front i.e. usage of 2-3 lead systems instead of the standard 12-lead (S12) system. Technological limitations constraint the number of leads to 2-3 while cardiologists accustomed with 12-Lead ECG may find these 2-3 lead systems insufficient for diagnosis. Thus, the aforementioned limitations pose self-contradicting challenges for RHM. A personalized reduced 2/3 lead system is required which can offer equivalent information as contained in S12 system, so as to accurately reconstruct S12 system from reduced lead system for diagnosis. In this paper, we propose a personalized reduced 3-lead (R3L) system formation methodology which employs principal component analysis, thereby, reducing redundancy and increasing SNR ratio, hence, making it suitable for wireless transmission. Accurate S12 system is made available using personalized lead reconstruction methodology, thus addressing medical constraints. Mean R 2 statistics values obtained for reconstruction of S12 system from the proposed R3L system using PhysioNet's PTB and TWA databases were 95.63% and 96.37% respectively. To substantiate the superior diagnostic quality of reconstructed leads, root mean square error (RMSE) metrics obtained upon comparing the ECG features extracted from the original and reconstructed leads, using our recently proposed Time Domain Morphology and Gradient (TDMG) algorithm, have been analyzed and discussed. The proposed system does not require any extra electrode or modification in placement positions and hence, can readily find application in computerized ECG machines.

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“…The standard 12 lead ECG acquisition system uses 10 electrodes to collect the 12 Lead ECG data [7]. Recent developments in the ECG data acquisition methodologies made it possible to extract the 12 lead ECG data from 3 lead ECG data [8]. The construction of the 3 lead ECG data acquisition system is easy rather than the standard 12 lead ECG data acquisition system due to the number of electrodes that are to be connected to the body for monitoring of the data.…”

Section: A 12 Lead Ecg Data Conditioning and Acquisition Systemmentioning

confidence: 99%

Adaptive rule engine based IoT enabled remote health care data acquisition and smart transmission system

Kiran

Rajalakshmi

Bharadwaj

et al. 2014

2014 IEEE World Forum on Internet of Things (WF-IoT)

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In the remote health care monitoring applications, the collected medical data from bio-medical sensors should be transmitted to the nearest gateway for further processing. Transmission of data contributes to a significant amount of power consumption by the transmitter and increase in the network traffic. In this paper we propose a low complex rule engine based health care data acquisition and smart transmission system architecture, which uses IEEE 802.15.4 standard for transferring data to the gateway. The power consumed and the network traffic generated by the device can be reduced by event based transmission rather than continuous transmission of data. We developed two different rule engines: static rule engine and adaptive rule engine, which decides whether to transmit the collected data based on the important features extracted from the data, thereby achieving power saving. In this paper, ECG data acquisition and transmission architecture is considered. The metrics used for performance analysis are the amount of power saving and reduction in network traffic. It is shown that the proposed rule engine gives a significant reduction in energy consumption and network traffic generated.

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Accurate and reliable 3-lead to 12-lead ECG reconstruction methodology for remote health monitoring applications

Cited by 15 publications

References 21 publications

System Architecture for Low-Power Ubiquitously Connected Remote Health Monitoring Applications With Smart Transmission Mechanism

System Architecture for Low-Power Ubiquitously Connected Remote Health Monitoring Applications With Smart Transmission Mechanism

Personalized Reduced 3-Lead System formation methodology for Remote Health Monitoring Applications and Reconstruction of Standard 12-Lead system

Adaptive rule engine based IoT enabled remote health care data acquisition and smart transmission system

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